Findings of the Association for Computational Linguistics: EMNLP 2025

Large Language Models Might Not Care What You Are Saying: Prompt Format Beats Descriptions (2025.findings-emnlp)

Challenge:	Large language models acquire general knowledge from pretraining but pretraining data contain undesirable social biases which can be perpetuated or even amplified by LLMs.
Approach:	They propose an efficient yet effective annotation pipeline to investigate social biases in pretraining data.
Outcome:	The proposed pipeline investigates social biases in the pretraining corpus using protected attribute detection and regard classification.

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Chenming Tang, Zhixiang Wang, Hao Sun, Yunfang Wu

Boundary Matters: Leveraging Structured Text Plots for Long Text Outline Generation (2025.findings-emnlp)

Challenge:	In-context learning has improved performance of large language models, but descriptive instructions are still under-explored.
Approach:	They propose an ensemble prompt framework to describe selection criteria of multiple in-context examples. preliminary experiments on machine translation confirm that this framework boosts ICL performance.
Outcome:	The proposed framework improves on commonsense, math, logical reasoning and hallucination tasks with three LLMs.

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Yuanchi Ma, Jiamou Liu, Hui He, Libo Zhang, Haoyuan Li, Zhendong Niu

Challenge:	Existing methods for generating readable outlines are inability to segment long texts .
Approach:	They propose an unsupervised framework to guide large language model outline generation . framework ensures each structured plot encapsulates complete causality by accurately identifying plot boundaries.
Outcome:	The proposed framework ensures that each structured plot encapsulates complete causality by accurately identifying plot boundaries.

Can Large Language Models Personalize Dialogues to Generational Styles? (2025.findings-emnlp)

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Pier Felice Balestrucci, Ondrej Dusek, Luca Anselma, Alessandro Mazzei

Toward Optimal LLM Alignments Using Two-Player Games (2025.findings-emnlp)

Challenge:	a human evaluation reveals that annotators were able to most accurately identify the generation behind P-MultiWoZ dialogues, based only on a single query-reply pair.
Approach:	They create a personalized, generation-specific version of MultiWOZ 2.2 by prompting LLMs to generate personalized dialogue responses.
Outcome:	The proposed model is a personalized version of MultiWOZ 2.2 for Generation X, Y, and Z . it is validated by automatic and human evaluations to determine whether it reflects generational linguistic traits.

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Rui Zheng, Hongyi Guo, Zhihan Liu, Xiaoying Zhang, Yuanshun Yao, Xiaojun Xu, Zhaoran Wang, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang, Yang Liu, Hang Li

Structural Patent Classification Using Label Hierarchy Optimization (2025.findings-emnlp)

Challenge:	Alignment of large language models (LLM) is a process that ensures the model’s responses to user prompts align with human intentions and social values.
Approach:	They propose an alignment method based on a two-agent game consisting of an adversarial agent and a defensive agent.
Outcome:	The proposed method improves on a two-agent game with an adversarial agent and a defensive agent.

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Mengting Gui, Shufeng Hao, Chongyang Shi, Qi Zhang

Exploring Hyperbolic Hierarchical Structure for Multimodal Rumor Detection (2025.findings-emnlp)

Challenge:	Existing methods for patent classification ignore key technical content claims and citation relationships . existing methods treat labels as independent targets, failing to exploit semantic and structural information within the label taxonomy.
Approach:	They propose a Claim Structure based Patent Classification model with Label Awareness . structural graph learning is used to mine the internal logic of patent claims .
Outcome:	The proposed method is more effective than state-of-the-art classification models.

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Md Mahbubur Rahman, Shufeng Hao, Chongyang Shi, An Lao, Jinyan Liu

Multi-Surrogate-Objective Optimization for Neural Topic Models (2025.findings-emnlp)

Challenge:	rumor detection models often assume a simplistic one-to-one alignment between modalities . authors present a method that preserves hierarchical, non-linear relationships .
Approach:	They propose a method that uses hyperbolic geometry to preserve hierarchical relationships . it decomposes image and text content into three levels and embeds them in hyperbolical space .
Outcome:	The proposed method preserves hierarchical relationships rather than representing them at a flat semantic level.

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Tue Le, Hoang Tran Vuong, Tung Nguyen, Linh Ngo Van, Dinh Viet Sang, Trung Le, Thien Huu Nguyen

How Diversely Can Language Models Solve Problems? Exploring the Algorithmic Diversity of Model-Generated Code (2025.findings-emnlp)

Challenge:	Neural topic modeling incorporates multiple loss functions but can be difficult to optimize for disparate magnitudes of these losses.
Approach:	They propose a gradient-based multi-objective optimization approach that integrates MOO algorithms into the model without the need for hard-parameter sharing.
Outcome:	The proposed approach outperforms direct MOO applications on NTMs.

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Seonghyeon Lee, HeeJae Chon, Joonwon Jang, Dongha Lee, Hwanjo Yu

ReAL: How Can LLMs Simulate the Real Teacher? Retrieval-enhanced Agent for Adaptive Learning (2025.findings-emnlp)

Challenge:	Language models (LMs) have exhibited impressive abilities in generating code from natural language requirements.
Approach:	They propose to introduce various metrics with inter-code similarity to evaluate the diversity of generated code by comparing model-generated solutions with human-written ones.
Outcome:	The proposed method leverages LMs’ capabilities in code understanding and reasoning, resulting in a set of metrics that represent the number of algorithms in model-generated solutions.

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Rui Lv, Qi Liu, Weibo Gao, Jiatong Li, Kai Zhang, Shiwei Tong

LLMsPark: A Benchmark for Evaluating Large Language Models in Strategic Gaming Contexts (2025.findings-emnlp)

Challenge:	Prior methods model learner-item interactions based only on ID sequences, leading to insufficient use of both learner and item information.
Approach:	They propose a Retrieval-enhanced Agent for Adaptive Learning powered by large language models to simulate teacher decision-making with extensive prior knowledge and teaching experience.
Outcome:	The proposed model outperforms existing models on three real-world datasets in both internal and external perspectives.

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Junhao Chen, Jingbo Sun, Xiang Li, Haidong Xin, Yuhao Xue, Yibin Xu, Hao Zhao

Versatile Framework for Song Generation with Prompt-based Control (2025.findings-emnlp)

Challenge:	Large language models (LLMs) are increasingly important for their intelligence evaluation.
Approach:	They propose a game theory-based evaluation platform that measures LLMs’ decision-making strategies and social behaviors in classic game-theoretic settings.
Outcome:	The proposed system cross-evaluates 15 leading LLMs using leaderboard rankings and scoring mechanisms.

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Yu Zhang, Wenxiang Guo, Changhao Pan, Zhiyuan Zhu, Ruiqi Li, Jingyu Lu, Rongjie Huang, Ruiyuan Zhang, Zhiqing Hong, Ziyue Jiang, Zhou Zhao

InsBank: Evolving Instruction Subset for Ongoing Alignment (2025.findings-emnlp)

Challenge:	Existing methods for song generation fail to generate vocals with prompt-based control and proper alignment.
Approach:	VersBand is a multi-task song generation framework for synthesizing high-quality songs with prompt-based control.
Outcome:	Experimental results show that VersBand performs better than baseline models across multiple song generation tasks.

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Jiayi Shi, Yiwei Li, Shaoxiong Feng, Peiwen Yuan, Xinglin Wang, Yueqi Zhang, Chuyi Tan, Boyuan Pan, Huan Ren, Yao Hu, Kan Li

TL-Training: A Task-Feature-Based Framework for Training Large Language Models in Tool Use (2025.findings-emnlp)

Challenge:	Recent studies emphasize that quality and diversity of instruction data are more crucial than quantity, highlighting the need to select diverse, high-quality subsets to reduce training costs.
Approach:	They propose to use a continuously updated repository to integrate the latest valuable instruction data with a progressive evolution framework to evolve InsBank over time.
Outcome:	The proposed framework outperforms baselines in InsBank evolution and extracts budget-specific subsets.

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Junjie Ye, Yilong Wu, Sixian Li, Yuming Yang, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang, Peng Wang, Zhongchao Shi, Jianping Fan, Zhengyin Du

DCMKC: A Dual Consistency Matching Approach for Multi-hop Question Answering in LLMs (2025.findings-emnlp)

Challenge:	a new approach to training large language models (LLMs) overlooks task-specific characteristics in tool use, leading to performance bottlenecks.
Approach:	They propose a task-feature-based framework that mitigates the effects of suboptimal training data . they use a dataset to train large-scale LLMs and a reward mechanism tailored to error categories .
Outcome:	The proposed framework matches or surpasses open- and closed-source LLMs in tool-use performance using only 1,217 training data points.

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Xinyi Wang, Yiping Song, Chang Liu, Tingjin Luo, Bo Liu, Zheng Xie, Minlie Huang

On Domain-Adaptive Post-Training for Multimodal Large Language Models (2025.findings-emnlp)

Challenge:	Existing reasoning based on chains of thought (CoTs) fails to find logical connections between reasoning steps .
Approach:	They propose a method to match KG reasoning chains with CoTs based on semantic similarity . they use a knowledge graph to find relevant information "within" each reasoning step .
Outcome:	The proposed method outperforms baselines on multi-answer questions with 5.1% improvement over baselines.

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Daixuan Cheng, Shaohan Huang, Ziyu Zhu, Xintong Zhang, Xin Zhao, Zhongzhi Luan, Bo Dai, Zhenliang Zhang

CPO: Addressing Reward Ambiguity in Role-playing Dialogue via Comparative Policy Optimization (2025.findings-emnlp)

Challenge:	Adapting general multimodal large language models to specific domains is important for practical applications.
Approach:	They investigate domain adaptation of multimodal large language models via post-training . they develop a generate-then-filter pipeline that curates diverse visual instruction tasks .
Outcome:	The proposed model outperforms existing models in domain adaptation by combining data from open-source models with training pipelines.

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Jing Ye, Rui Wang, Yuchuan Wu, Victor Ma, Feiteng Fang, Fei Huang, Yongbin Li

SPPD: Self-training with Process Preference Learning Using Dynamic Value Margin (2025.findings-emnlp)

Challenge:	Comparative Policy Optimization (CPO) redefines the reward evaluation paradigm by shifting from sample-wise scoring to comparative group-wise score.
Approach:	They propose a method to optimize subjective tasks by shifting from sample-wise to comparative group-wise scoring.
Outcome:	The proposed framework shifts from sample-wise scoring to comparative group-wise score . it minimizes contextual bias and enables more robust and fair performance evaluation.

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Hao Yi, Qingyang Li, Yulan Hu, Fuzheng Zhang, Di Zhang, Yong Liu

Numerical Reasoning

Challenge:	Existing approaches to improve numerical and logical reasoning of Large Language Models are limited . existing approaches rely on prompt engineering and pretrained knowledge to ensure correctness .
Approach:	They propose to train LLMs with process-based reasoning using a dynamic value margin . they use the Bellman optimality equation to derive a value margin for step-level preference optimization .
Outcome:	The proposed method is equivalent to on-policy policy gradient methods under constrained reward functions.

Error Classification of Large Language Models on Math Word Problems: A Dynamically Adaptive Framework (2025.findings-emnlp)

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Zhangyue Yin, YuHong Sun, Xuanjing Huang, Xipeng Qiu, Hui Zhao

sudoLLM: On Multi-role Alignment of Language Models (2025.findings-emnlp)

Challenge:	Current error classification methods rely on static and predefined categories to capture error patterns.
Approach:	They propose a framework for automated dynamic error classification in mathematical reasoning that incorporates common error patterns as explicit guidance.
Outcome:	The proposed framework reduces human bias and fine-grained analysis of error patterns.

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Soumadeep Saha, Akshay Chaturvedi, Joy Mahapatra, Utpal Garain

DAC: Decomposed Automation Correction for Text-to-SQL (2025.findings-emnlp)

Challenge:	a framework that allows users to control access rights has not been extensively studied in the large language model realm.
Approach:	They propose a framework that allows users to control access rights in a multi-role manner.
Outcome:	The proposed framework improves alignment, generalization and resistance to prefix-based jailbreaking attacks.

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Dingzirui Wang, Longxu Dou, Xuanliang Zhang, Qingfu Zhu, Wanxiang Che

VehicleWorld: A Highly Integrated Multi-Device Environment for Intelligent Vehicle Interaction (2025.findings-emnlp)

Challenge:	Existing methods to improve text-to-SQL performance are hard to detect errors in SQL directly.
Approach:	They propose to use decomposed correction to improve text-to-SQL performance . they first detect errors based on decompose subtasks, then use it to correct them .
Outcome:	The proposed method improves text-to-SQL performance by 1.4% compared with previous methods .

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Jie Yang, Jiajun Chen, Zhangyue Yin, Shuo Chen, Yuxin Wang, Yiran Guo, Yuan Li, Yining Zheng, Xuanjing Huang, Xipeng Qiu

Challenge:	Traditional Function Calling (FC) approaches operate statelessly, requiring multiple exploratory calls to build environmental awareness before execution, leading to inefficiency and limited error recovery.
Approach:	They propose a state-based function call approach that maintains explicit system state awareness and implements direct state transitions to achieve target conditions.
Outcome:	The proposed approach outperforms traditional function calling approaches, achieving superior execution accuracy and reduced latency.

End-to-End Optimization for Multimodal Retrieval-Augmented Generation via Reward Backpropagation (2025.findings-emnlp)

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Zhiyuan Fan, Longfei Yun, Ming Yan, Yumeng Wang, Dadi Guo, Brian Mak, James Kwok, Yi R. Fung

Audio-Aware Large Language Models as Judges for Speaking Styles (2025.findings-emnlp)

Challenge:	MM-RAG is a promising approach for enhancing the reliability and factuality of large vision-language models . current methods focus on component-level optimizations and necessitate extensive component-specific training datasets .
Approach:	They propose a new paradigm that backpropagates global rewards to each component . this backpropage transforms local losses into specific local losses .
Outcome:	The proposed paradigm achieves high training efficiency on knowledge-intensive multimodal benchmarks.

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Cheng-Han Chiang, Xiaofei Wang, Chung-Ching Lin, Kevin Lin, Linjie Li, Radu Kopetz, Yao Qian, Zhendong Wang, Zhengyuan Yang, Hung-yi Lee, Lijuan Wang

Evaluation of Text-to-Image Generation from a Creativity Perspective (2025.findings-emnlp)

Challenge:	Audio-aware large language models (ALLMs) can understand textual and non-textual information in the audio input.
Approach:	They use audio-aware large language models (ALLMs) to evaluate the speaking styles of SLMs on two tasks: voice style instruction following and role-playing.
Outcome:	The proposed models can understand the textual and non-textual information in the audio input and can be used as a judge to assess the speaking styles of SLMs.

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Xinhao Wang, Xinyu Ma, ShengYong Ding, Derek F. Wong

Perovskite-LLM: Knowledge-Enhanced Large Language Models for Perovskite Solar Cell Research (2025.findings-emnlp)

Challenge:	Recent studies have assessed the creativity of T2I models, but little has been done on the quality of generated images and image-text alignment.
Approach:	They define the creativity of T2I models and propose metrics to test reliability . they also develop a pipeline capable of transforming existing image-text datasets into benchmarks .
Outcome:	The proposed method tests the reliability of the metric and a fully automated pipeline capable of transforming image-text datasets into benchmarks tailored for evaluating creativity.

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Xiang Liu, Penglei Sun, Shuyan Chen, Longhan Zhang, Peijie Dong, Huajie You, Yongqi Zhang, Chang Yan, Xiaowen Chu, Tong-yi Zhang

ProPy: Building Interactive Prompt Pyramids upon CLIP for Partially Relevant Video Retrieval (2025.findings-emnlp)

Challenge:	a rapid advancement of perovskite solar cells has led to an exponential growth in research publications.
Approach:	They propose a knowledge-enhanced system for perovskite solar cells that integrates three key components.
Outcome:	The proposed system outperforms existing models in domain-specific knowledge retrieval and scientific reasoning tasks.

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Yi Pan, Yujia Zhang, Michael Kampffmeyer, Xiaoguang Zhao

Multilingual Datasets for Custom Input Extraction and Explanation Requests Parsing in Conversational XAI Systems (2025.findings-emnlp)

Challenge:	Existing models for PRVR use unimodal features, but powerful pretrained vision-language models like CLIP are underexplored.
Approach:	ProPy is a model with systematic architectural adaptation of CLIP specifically designed for PRVR.
Outcome:	ProPy outperforms existing models on three public datasets in terms of performance on the datasets.

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Qianli Wang, Tatiana Anikina, Nils Feldhus, Simon Ostermann, Fedor Splitt, Jiaao Li, Yoana Tsoneva, Sebastian Möller, Vera Schmitt

Toolscaler: Scalable Generative Tool Calling via Structure-Aware Semantic Tokenization (2025.findings-emnlp)

Challenge:	Current ConvXAI systems are based on intent recognition to accurately identify the user’s desired intention and map it to an explainability method.
Approach:	They propose a multilingual extension of the CoXQL dataset spanning five typologically diverse languages, including one low-resource language.
Outcome:	The proposed model enables multilingual generalization in a multilingual dataset spanning five typologically diverse languages, including one low-resource language.

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Yunyue Su, Zhang Jinshuai, Bowen Fang, Wen Ye, Jinghao Zhang, Bowen Song, Weiqiang Wang, Qiang Liu, Liang Wang

LaMP-Val: Large Language Models Empower Personalized Valuation in Auction (2025.findings-emnlp)

Challenge:	Extensive experiments demonstrate the effectiveness of SGTC across various tasks.
Approach:	They propose a generative tool invocation framework that introduces structure-aware semantic tokenization to encode tools as discrete code sequences.
Outcome:	The proposed framework reduces the size of the representation space and underutilizes collaborative signals among tools in downstream tasks.

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Jie Sun, Tianyu Zhang, Houcheng Jiang, Kexin Huang, Xiang Shu, Zhibo Zhu, Lintao Ma, Xingyu Lu, Jun Zhou, Junkang Wu, Chi Luo, An Zhang, Jiancan Wu, Xiang Wang

Exploring Model Kinship for Merging Large Language Models (2025.findings-emnlp)

Challenge:	Currently, most research focuses on the bidding algorithms used within auction mechanisms.
Approach:	They propose a personalized valuation framework that integrates Large Language Models to incorporate personalized semantic preference into users valuation process.
Outcome:	The proposed framework incorporates Large Language Models to incorporate personalized semantic preference into users valuation process.

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Yedi Hu, Yunzhi Yao, Ningyu Zhang, Huajun Chen, Shumin Deng

MULTITAT: Benchmarking Multilingual Table-and-Text Question Answering (2025.findings-emnlp)

Challenge:	Model merging has become one of the key technologies for enhancing the capabilities and efficiency of Large Language Models.
Approach:	They propose a model merging strategy that incorporates model kinship to improve model performance.
Outcome:	The proposed model merging strategy can yield better performance on benchmark datasets.

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Xuanliang Zhang, Dingzirui Wang, Keyan Xu, Qingfu Zhu, Wanxiang Che

LoRA-MGPO: Mitigating Double Descent in Low-Rank Adaptation via Momentum-Guided Perturbation Optimization (2025.findings-emnlp)

Challenge:	Existing TATQA datasets are limited to English, leading to drawbacks . existing datasets overlook challenges of multilingual TAT-QA and do not reflect real-world multilingual scenarios .
Approach:	They propose a multilingual TATQA dataset that can be translated into 10 languages . they use data from 3 mainstream TATQ datasets and analyze the results .
Outcome:	The proposed dataset outperforms other baselines by an average of 3.3 .

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Yupeng Chang, Chenlu Guo, Yi Chang, Yuan Wu

R-LoRA: Randomized Multi-Head LoRA for Efficient Multi-task Learning (2025.findings-emnlp)

Challenge:	Low-Rank Adaptation (LoRA) adapts large language models by training only a small fraction of parameters, but as the rank of the low-rank matrices increases, LoRA exhibits an unstable “double descent” phenomenon, which delays convergence and impairs generalization by causing instability due to the attraction to sharp local minima.
Approach:	They propose a framework that incorporates Momentum-Guided Perturbation Optimization (MGPO) MGPO stabilizes training dynamics by mitigating double descent phenomenon and guiding weight perturbations using momentum vectors from the optimizer’s state.
Outcome:	The proposed framework improves performance on natural language understanding benchmarks and shows that it improves convergence and generalization.

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Jinda Liu, Yi Chang, Yuan Wu

RACQC: Advanced Retrieval-Augmented Generation for Chinese Query Correction (2025.findings-emnlp)

Challenge:	Low-Rank Adaptation (LoRA) improves performance in multi-task learning by diversifying the head matrices through Multi-Head Dropout and Multi-head Random Initialization.
Approach:	They propose a low-rank adaptive approach to fine-tune large language models by approximating weight updates through low-ranked matrices.
Outcome:	The proposed approach improves performance in multi-task learning while reducing memory usage and training time.

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Jinbo Su, Lingzhe Gao, Wei Li, Shihao Liu, Haojie Lei, Xinyi Wang, Yuanzhao Guo, Ke Wang, Daiting Shi, Dawei Yin

Mechanistic Understanding and Mitigation of Language Confusion in English-Centric Large Language Models (2025.findings-emnlp)

Challenge:	Large language models (LLMs) exhibit remarkable capabilities across many tasks, but face critical challenges in the CSC scenario: (1) poor generalization to rare entities in open-domain searches; and (2) failure to adapt to temporal entity variations due to static parameters, resulting in serious over-correction issues.
Approach:	They propose a Chinese Spelling Check system with RAG and multi-task learning that integrates dynamic knowledge retrieval and entity-centric RAG to address rare entities.
Outcome:	The proposed system outperforms existing baselines in the CSC task and achieves a maximum improvement of +9.92% on the search scenario benchmark and +3.2% on the general-domain dataset.

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Ercong Nie, Helmut Schmid, Hinrich Schuetze

Assessing and Mitigating Medical Knowledge Drift and Conflicts in Large Language Models (2025.findings-emnlp)

Challenge:	Language confusion is a critical challenge for large language models, especially for English-centric models.
Approach:	They propose to combine behavioral benchmarking with neuron-level analysis to study language confusion.
Outcome:	The proposed approach matches multilingual alignment in confusion reduction for many languages and yields cleaner, higher-quality outputs.

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Weiyi Wu, Xinwen Xu, Chongyang Gao, Xingjian Diao, Siting Li, Lucas A. Salas, Jiang Gui

Improving LLM Reasoning through Interpretable Role-Playing Steering (2025.findings-emnlp)

Challenge:	Rapid medical concept drift can lead LLMs to provide incorrect or outdated advice.
Approach:	They propose to evaluate how large language models manage knowledge conflicts in clinical guidelines.
Outcome:	The proposed benchmark evaluates how LLMs manage varied knowledge conflicts in clinical guidelines.

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Anyi Wang, Dong Shu, Yifan Wang, Yunpu Ma, Mengnan Du

R2A-TLS: Reflective Retrieval-Augmented Timeline Summarization with Causal-Semantic Integration (2025.findings-emnlp)

Challenge:	Existing methods for role-playing rely on prompt engineering, which lacks stability and interpretability.
Approach:	They propose a framework that extracts latent representations from role-play prompts and constructs a steering vector that can be injected into the model's residual stream with controllable intensity.
Outcome:	The proposed framework extracts latent representations from role-play prompts, selects the most relevant features based on activation patterns, and constructs a steering vector that can be injected into the model’s residual stream with controllable intensity.

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Chenlong Bao, Shijie Li, Minghao Hu, Ming Qiao, Bin Zhang, Jin-Tao Tang, Shasha Li, Ting Wang

MedEBench: Diagnosing Reliability in Text-Guided Medical Image Editing (2025.findings-emnlp)

Challenge:	Existing methods struggle to capture coherent event narratives due to fragmented descriptions . Existing approaches accumulate noise through iterative retrieval strategies that lack relevance evaluation.
Approach:	They propose a reflective retrieval-augmented timeline summarization with Causal-Semantic Intergration approach for open-domain timeline summarizing .
Outcome:	The proposed approach outperforms the best prior published approaches.

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Minghao Liu, Zhitao He, Zhiyuan Fan, Qingyun Wang, Yi R. Fung

FairCoT: Enhancing Fairness in Text-to-Image Generation via Chain of Thought Reasoning with Multimodal Large Language Models (2025.findings-emnlp)

Challenge:	Text-guided image editing has seen significant progress in natural image domains, but its application in medical imaging remains limited.
Approach:	a new benchmark is designed to diagnose reliability in text-guided medical image editing. a clinically grounded evaluation framework measures Editing Accuracy, Context Preservation, and Visual Quality.
Outcome:	a new benchmark is designed to diagnose reliability in medical image editing.

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Zahraa Al Sahili, Ioannis Patras, Matthew Purver

Bag of Tricks for Sparse Mixture-of-Experts: A Benchmark Across Reasoning, Efficiency, and Safety (2025.findings-emnlp)

Challenge:	FairCoT enhances fairness in text-to-image generative models by integrating iterative reasoning . experimental evaluations demonstrate FairCot significantly enhances diversity without sacrificing image quality or semantic fidelity.
Approach:	FairCoT is a framework that enhances fairness in text-to-image generative models . it employs iterative CoT refinement to mitigate biases and dynamically adjusts textual prompts .
Outcome:	FairCoT combines iterative CoT refinement with iterating reasoning processes . it addresses limitations of zero-shot CoT in sensitive scenarios, authors say .

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Mufan Qiu, Zheyu Shen, Pingzhi Li, Ang Li, Tianlong Chen

Don’t Take the Premise for Granted: Evaluating the Premise Critique Ability of Large Language Models (2025.findings-emnlp)

Challenge:	Existing benchmarks focus on isolated aspects of MoE, with conflicting conclusions . a lack of consensus on optimal design choices is limiting to specific aspects of the model.
Approach:	They propose to evaluate two popular MoE backbones across four dimensions of design choices . they find token-level routing and z-loss regularization improve reasoning performance .
Outcome:	The proposed framework evaluates two popular MoE backbones on over eight metrics.

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Jinzhe Li, Gengxu Li, Yi Chang, Yuan Wu

Mitigating Geospatial Knowledge Hallucination in Large Language Models: Benchmarking and Dynamic Factuality Aligning (2025.findings-emnlp)

Challenge:	Existing studies assess LLMs’ reasoning ability in ideal settings, ignoring their vulnerabilities when faced with flawed premises.
Approach:	They propose to evaluate LLMs' ability to proactively identify and articulate errors in input premises.
Outcome:	The proposed model enables LLMs to proactively identify and articulate errors in input premises.

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Shengyuan Wang, Jie Feng, Tianhui Liu, Dan Pei, Yong Li

The Power of Framing: How News Headlines Guide Search Behavior (2025.findings-emnlp)

Challenge:	Large language models (LLMs) have extensive world knowledge, but often generate inaccurate geospatial knowledge.
Approach:	They propose a framework for evaluation of large language models to mitigate hallucinations . they use Kahneman-Tversky Optimization to align LLMs with their reality .
Outcome:	The proposed evaluation framework uncovers hallucinations in 20 advanced LLMs.

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Amrit Poudel, Maria Milkowski, Tim Weninger

DivLogicEval: A Framework for Benchmarking Logical Reasoning Evaluation in Large Language Models (2025.findings-emnlp)

Challenge:	Framing effects on judgment are well documented, but their impact on subsequent search behavior is less understood.
Approach:	They conducted a controlled experiment where participants issued queries and selected headlines filtered by specific linguistic frames.
Outcome:	The results suggest that even brief exposure to framing can meaningfully alter the direction of users’ information-seeking behavior.

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Tsz Ting Chung, Lemao Liu, Mo Yu, Dit-Yan Yeung

THCM-CAL: Temporal-Hierarchical Causal Modelling with Conformal Calibration for Clinical Risk Prediction (2025.findings-emnlp)

Challenge:	Existing logic reasoning benchmarks are limited in language diversity and their distributions are deviated from ideal distributions, which may lead to biased evaluation results.
Approach:	They propose a new logic benchmark DivLogicEval that uses natural sentences to evaluate logical reasoning .
Outcome:	The proposed evaluation metric mitigates bias and randomness inherent in LLMs.

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Xin Zhang, Qiyu Wei, Yingjie Zhu, Fanyi Wu, Sophia Ananiadou

Challenge:	Existing approaches to risk prediction from EHRs handle structured diagnostic codes and unstructured narrative notes separately.
Approach:	They propose a Temporal-Hierarchical Causal Model with Conformal Calibration . they construct a multimodal causal graph where nodes represent clinical entities from two modalities .
Outcome:	The proposed model infers three clinically grounded interactions from textual propositions and ICD codes mapped to textual descriptions.

GenPilot: A Multi-Agent System for Test-Time Prompt Optimization in Image Generation (2025.findings-emnlp)

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Wen Ye, Zhaocheng Liu, Gui Yuwei, Tingyu Yuan, Yunyue Su, Bowen Fang, Chaoyang Zhao, Qiang Liu, Liang Wang

Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models (2025.findings-emnlp)

Challenge:	Existing methods for text-to-image synthesis lack systematic error analysis and refinement strategies, resulting in limited reliability and effectiveness.
Approach:	They propose a plug-and-play multi-agent system called GenPilot that integrates error analysis, clustering-based adaptive exploration, fine-grained verification and a memory module for iterative optimization.
Outcome:	The proposed method improves text consistency and structural coherence on images with a plug-and-play system.

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Haibo Wang, Zhiyang Xu, Yu Cheng, Shizhe Diao, Yufan Zhou, Yixin Cao, Qifan Wang, Weifeng Ge, Lifu Huang

DongbaMIE: A Multimodal Information Extraction Dataset for Evaluating Semantic Understanding of Dongba Pictograms (2025.findings-emnlp)

Challenge:	Video Large Language Models (VLMs) have been praised for their performance in coarse-grained video understanding but still face ineffective temporal grounding and inadequate timestamp representations.
Approach:	They propose a novel Video-LLM that senses and reasoned over specific video moments with fine-grained temporal precision.
Outcome:	The proposed model surpasses existing models in fine-grained video understanding tasks and exhibits strong potential as a general video understanding assistant.

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Xiaojun Bi, Shuo Li, Junyao Xing, Ziyue Wang, Fuwen Luo, Weizheng Qiao, Lu Han, Ziwei Sun, Peng Li, Yang Liu

Optimizing Cross-Client Domain Coverage for Federated Instruction Tuning of Large Language Models (2025.findings-emnlp)

Challenge:	Dongba pictographic is the only pictograph script still in use in the world.
Approach:	DongbaMIE is the first dataset focusing on multimodal information extraction of Dongbe pictographs.
Outcome:	The dataset contains 23,530 sentence-level and 2,539 paragraph-level high-quality text-image pairs.

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Zezhou Wang, Yaxin Du, Xingjun Ma, Yu-Gang Jiang, Zhuzhong Qian, Siheng Chen

Aligning Black-Box LLMs for Aspect Sentiment Quad Prediction (2025.findings-emnlp)

Challenge:	Federated domain-specific instruction tuning (FedDIT) for large language models (LLMs) aims to enhance performance in specialized domains using distributed private and limited data.
Approach:	They introduce an algorithm that explicitly maximizes cross-client domain coverage through diversity-oriented client center selection and retrieval-based augmentation.
Outcome:	The proposed algorithm achieves performance gains of 29.19% and domain coverage improvements of 4.82%-21.36% over 11 baselines.

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Shichen Li, Jiawei Zhang, Zhongqing Wang, Peifeng Li

Multifaceted Evaluation of Audio-Visual Capability for MLLMs: Effectiveness, Efficiency, Generalizability and Robustness (2025.findings-emnlp)

Challenge:	Large language models lack task-specific alignment with ASQP . supervised small language models (SLMs) lack the extensive knowledge of LLMs.
Approach:	They propose a framework that combines large language models and small language models to align LLM outputs with human preferences.
Outcome:	The proposed framework improves Aspect Sentiment Quad Prediction performance by combining SLMs and LLMs.

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Yusheng Zhao, Xiao Luo, Junyu Luo, Weizhi Zhang, Zhiping Xiao, Wei Ju, Philip S. Yu, Ming Zhang

Two Steps from Hell: Compositionality on Chemical LMs (2025.findings-emnlp)

Challenge:	Multi-modal large language models have been used for processing and understanding information from diverse modalities.
Approach:	They propose to evaluate the audio-visual capabilities of multi-modal large language models . they focus on effectiveness, efficiency, generalizability, and robustness .
Outcome:	The proposed models exhibit strong zero-shot and few-shot generalization abilities . their success relies heavily on the vision modality, which impairs performance when visual input is corrupted or missing.

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Veronika Ganeeva, Kuzma Khrabrov, Artur Kadurin, Elena Tutubalina

GTA: Supervised-Guided Reinforcement Learning for Text Classification with Large Language Models (2025.findings-emnlp)

Challenge:	Experiments with state-of-the-art ChemLLMs show significant performance drops in compositional tasks, highlighting the need for models that move beyond pattern recognition.
Approach:	They introduce a benchmark to evaluate chemical language models' understanding of chemical language by identifying and analyzing compositional patterns within chemical data.
Outcome:	The proposed benchmark shows that existing LLMs can handle complex queries without pattern recognition.

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Min Zeng, Jingfei Sun, Xueyou Luo, Shiqi Zhang, Li Xie, Caiquan Liu, Xiaoxin Chen

Unearthing Gems from Stones: Policy Optimization with Negative Sample Augmentation for LLM Reasoning (2025.findings-emnlp)

Challenge:	Reinforcement learning fine-tuning methods suffer from inefficient exploration and slow convergence . supervised fine- tuning methods have limited performance ceiling and less solid theoretical foundation .
Approach:	They propose a Guess-Think-Answer framework that combines supervised and supervised learning in a unified training paradigm.
Outcome:	The proposed framework outperforms both standalone SFT and RL training models on three text classification benchmarks.

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Zhaohui Yang, Yuxiao Ye, Shilei Jiang, Shihong Deng, Chen Hu, Linjing Li, Daxin Jiang

LEAF: Large Language Diffusion Model for Time Series Forecasting (2025.findings-emnlp)

Challenge:	Recent advances in reasoning language models have witnessed a paradigm shift from short to long CoT pattern.
Approach:	They propose a behavior-constrained policy gradient with negative sample augmented (BCPG-NSA) negative steps are valuable components in long CoT models, authors argue .
Outcome:	The proposed framework outperforms baselines on math/coding reasoning benchmarks using the same training dataset.

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Yuhang Pei, Tao Ren, Yifan Wang, Zhipeng Sun, Wei Ju, Chong Chen, Xian-Sheng Hua, Xiao Luo

SPFT-SQL: Enhancing Large Language Model for Text-to-SQL Parsing by Self-Play Fine-Tuning (2025.findings-emnlp)

Challenge:	Recent work has applied large language models (LLMs) into time series forecasting, but they lack an understanding of holistic temporal patterns with potential error accumulation.
Approach:	They propose a framework that marries Larg e Langu age Diffusion Model with time series forecasting (LEAF) they propose converting time series into tokens and adopting language diffusion models to capture temporal dependencies.
Outcome:	The proposed framework generates future predictions with a diffusion model from a holistic view.

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Yuhao Zhang, Shaoming Duan, Jinhang Su, Chuanyi Liu, Peiyi Han

Multilingual Verbalisation of Knowledge Graphs (2025.findings-emnlp)

Challenge:	Existing methods for self-play fine-tuning do not generate new information and the large number of correct SQL queries produced by the opponent model reduces the main model’s ability to generate accurate SQL queries.
Approach:	They propose a self-play fine-tuning method tailored for the Text-to-SQL task that synthesizes high-quality fine- tuning data iteratively based on the database schema and validation feedback to enhance model performance.
Outcome:	The proposed method outperforms existing state-of-the-art methods on six open-source LLMs and five widely used benchmarks.

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Yifei Song, William Soto Martinez, Anna Nikiforovskaya, Evan Parker Kelly Chapple, Claire Gardent

LAGCL4Rec: When LLMs Activate Interactions Potential in Graph Contrastive Learning for Recommendation (2025.findings-emnlp)

Challenge:	Most work on Knowledge Graph (KG) verbalisation is monolingual leaving open the question of how to scale KG-to-Text generation to languages with varying amounts of resources.
Approach:	They explore how to scale KG-to-Text generation to languages with varying resources . they construct multilingual training data and test data for each language .
Outcome:	The proposed approach performs best on all 9 languages, compared with other approaches on low vs high resource languages and on in- v. out-of-domain data.

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Leqi Zheng, Chaokun Wang, Canzhi Chen, Jiajun Zhang, Cheng Wu, Zixin Song, Shannan Yan, Ziyang Liu, Hongwei Li

English as Defense Proxy: Mitigating Multilingual Jailbreak via Eliciting English Safety Knowledge (2025.findings-emnlp)

Challenge:	Traditional contrastive learning methods treat negative feedback as equally hard or easy, ignoring informative semantic difficulty during training.
Approach:	They propose a framework leveraging Large Language Models to Activate interactions in Graph Contrastive Learning for Recommendation.
Outcome:	The proposed framework outperforms state-of-the-art benchmarks on multiple benchmarks.

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Zekai Zhang, Yiduo Guo, Jiuheng Lin, Shanghaoran Quan, Huishuai Zhang, Dongyan Zhao

Dagger Behind Smile: Fool LLMs with a Happy Ending Story (2025.findings-emnlp)

Challenge:	Large language models excel in many tasks, but their safety guarantees vary by language.
Approach:	They propose a unified approach that leverages English as a universal safety anchor.
Outcome:	The proposed approach leverages English as defense proxy (E-Proxy) to transfer safety knowledge across languages.

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Xurui Song, Zhixin Xie, Shuo Huai, Jiayi Kong, Jun Luo

Mitigating Object Hallucinations in MLLMs via Multi-Frequency Perturbations (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have attracted significant attention from jailbreak attacks . existing manual designs are either easily detectable or require intricate interactions with LLMs.
Approach:	They propose a happy ending attack that wraps up a malicious request in a scenario template .
Outcome:	The proposed attack wraps up a malicious request in a scenario template involving a positive prompt formed mainly via a happy ending, fooling LLMs into jailbreaking either immediately or at a follow-up malicious request.

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Shuo Li, Jiajun Sun, Guodong Zheng, Xiaoran Fan, Yujiong Shen, Yi Lu, Zhiheng Xi, Yuming Yang, Wenming Tan, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang

Natural Context Drift Undermines the Natural Language Understanding of Large Language Models (2025.findings-emnlp)

Challenge:	Multimodal large language models have demonstrated remarkable performance in visual-language tasks, but their authenticity is often compromised by object hallucinations.
Approach:	They propose a multi-frequency perturbation method that leverages both low-frequency and high-frequency features of images to perturb visual feature representations and explicitly suppress redundant frequency-domain features during inference.
Outcome:	The proposed method significantly mitigates object hallucinations across various model architectures.

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Yulong Wu, Viktor Schlegel, Riza Batista-Navarro

Minimal Ranks, Maximum Confidence: Parameter-efficient Uncertainty Quantification for LoRA (2025.findings-emnlp)

Challenge:	generative Large Language Models (LLMs) are based on natural text evolution .
Approach:	They propose a framework for curating naturally evolved variants of reading passages from contemporary QA benchmarks and for analysing LLM performance across a range of semantic similarity scores.
Outcome:	The proposed framework evaluates QA datasets and LLMs with publicly available training data.

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Patryk Marszałek, Klaudia Bałazy, Jacek Tabor, Tomasz Kuśmierczyk

Chain-of-Thought Prompting Obscures Hallucination Cues in Large Language Models: An Empirical Evaluation (2025.findings-emnlp)

Challenge:	Low-Rank Adaptation (LoRA) enables parameter-efficient fine-tuning of large language models . standard LoRA lacks mechanisms for uncertainty quantification, leading to overconfident and poorly calibrated models.
Approach:	They propose a parameter-efficient Bayesian LoRA method that decomposes weight updates into low-rank matrices.
Outcome:	The proposed method achieves strong performance with improved calibration and generalization while maintaining computational efficiency.

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Jiahao Cheng, Tiancheng Su, Jia Yuan, Guoxiu He, Jiawei Liu, Xinqi Tao, Jingwen Xie, Huaxia Li

Large Language Model Evaluation via Matrix Nuclear-Norm (2025.findings-emnlp)

Challenge:	Chain-of-Thought (CoT) prompting can mitigate hallucinations by encouraging step-by-step reasoning, but its impact on halluciation detection remains underexplored.
Approach:	They conduct an empirical evaluation of CoT prompting in Large Language Models (LLMs) to examine their impact on hallucination detection methods.
Outcome:	The proposed method significantly affects the internal states and token probability distributions of the LLM.

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Yahan Li, Tingyu Xia, Yuan Wu, Yi Chang

From Grounding to Manipulation: Case Studies of Foundation Model Integration in Embodied Robotic Systems (2025.findings-emnlp)

Challenge:	Large language models (LLMs) are computationally intensive due to their O(n3) time complexity with Singular Value Decomposition (SVD).
Approach:	They propose a metric to quantify the data compression proficiency of large language models and a convex approximation of matrix rank to capture both predictive discriminability and diversity.
Outcome:	The proposed model achieves speeds 8 to 24 times faster than Matrix Entropy for the CEREBRAS-GPT model as models increase from 111M to 6.7B .

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Xiuchao Sui, Daiying Tian, Qi Sun, Ruirui Chen, Dongkyu Choi, Kenneth Kwok, Soujanya Poria

Flexible Thinking for Multimodal Emotional Support Conversation via Reinforcement Learning (2025.findings-emnlp)

Challenge:	a new study examines the operational characteristics of different integration strategies for robotics . end-to-end vision-language-action models implicitly unify perception and planning .
Approach:	They propose end-to-end vision-language-action models that implicitly unify perception and planning . they also propose modular pipelines using either vision-linguistic models or MLLMs .
Outcome:	The proposed frameworks implicitly unify perception and planning, and modular pipelines using either vision-language models or multimodal large language models.

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Fanfan Wang, Xiangqing Shen, Jianfei Yu, Rui Xia

ORAL: Prompting Your Large-Scale LoRAs via Conditional Recurrent Diffusion (2025.findings-emnlp)

Challenge:	Current Chain-of-Thought based ESC methods often employ rigid, text-only reasoning, limiting adaptability in dynamic, multimodal interactions and introducing reasoning noise that degrades support quality.
Approach:	They propose a framework that integrates supervised fine-tuning with reinforcement learning to improve ESC models' response quality.
Outcome:	The proposed framework enables models to select contextually relevant thinking aspects: Visual Scene, Emotion, Situation, and Response Strategy.

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Rana Shahroz, Dongwen Tang, Pingzhi Li, Kai Wang, Tianlong Chen

NLoRA: Nyström-Initiated Low-Rank Adaptation for Large Language Models (2025.findings-emnlp)

Challenge:	Existing approaches to low-rank Adaptation (LoRA) are limited in scalability and controllability.
Approach:	They propose a conditional recurrent diffusion framework that generates LoRA parameters directly . they integrate model architecture and textual task specifications to generate task-specific parameters .
Outcome:	The proposed framework scales to billions-of-parameter LLMs and maintains controllability.

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Chenlu Guo, Yi Chang, Yuan Wu

Bhaasha, Bhāṣā, Zaban: A Survey for Low-Resourced Languages in South Asia – Current Stage and Challenges (2025.findings-emnlp)

Challenge:	Parameter-efficient fine-tuning is essential for adapting large language models (LLMs). However, LoRA suffers from slow convergence and some recent LoRA variants, such as PiSSA, rely on Singular Value Decomposition (SVD) for initialization.
Approach:	They propose to introduce a small intermediate matrix between the low-rank matrices (A) and (B) and propose NyströmLoRA (NLoRA) which leverages Nyström-based initialization for SLoRA to improve its effectiveness and efficiency.
Outcome:	The proposed approach improves on 5 natural language generation tasks and 8 natural language understanding tasks with minimal parameter overhead.

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Sampoorna Poria, Xiaolei Huang

DISCO Balances the Scales: Adaptive Domain- and Difficulty-Aware Reinforcement Learning on Imbalanced Data (2025.findings-emnlp)

Challenge:	a survey examines the current efforts and challenges of NLP models for South Asian languages . there are more than 650 languages in South Asia, but many have very limited computational resources or are missing from existing models.
Approach:	a survey examines efforts and challenges of NLP for South Asian languages . they focus on transformer-based models such as BERT, T5, & GPT . findings highlight substantial issues, including missing data in critical domains .
Outcome:	The findings highlight significant issues, including missing data in critical domains . the survey aims to raise awareness within the NLP community for more targeted data curation .

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Yuhang Zhou, Jing Zhu, Shengyi Qian, Zhuokai Zhao, Xiyao Wang, Xiaoyu Liu, Ming Li, Paiheng Xu, Wei Ai, Furong Huang

What Makes for Good Image Captions? (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) are increasingly aligned with human preferences through Reinforcement Learning from Human Feedback (RLHF).
Approach:	a new study proposes a domain-informed self-consistency policy optimization extension to GRPO that addresses inter-group imbalance.
Outcome:	a new extension of GRPO addresses inter-group imbalance with two key innovations . the proposed method outperforms existing GR PO variants by 5% on Qwen3 models .

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Delong Chen, Samuel Cahyawijaya, Etsuko Ishii, Ho Shu Chan, Yejin Bang, Pascale Fung

What’s Not Said Still Hurts: A Description-Based Evaluation Framework for Measuring Social Bias in LLMs (2025.findings-emnlp)

Challenge:	a formal information-theoretic framework is developed for image captioning . the pyramid of captions is a method that generates enriched captions by integrating local and global visual information.
Approach:	They propose a formal information-theoretic framework for image captioning . they propose 'Pyramid of Captions' method that generates enriched captions .
Outcome:	The proposed framework provides a flexible foundation for analyzing and optimizing image captioning systems across diverse task requirements.

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Jinhao Pan, Chahat Raj, Ziyu Yao, Ziwei Zhu

Identifying Rare Languages in Common Crawl Data is a Needles-in-a-Haystack Problem (2025.findings-emnlp)

Challenge:	Existing benchmarks evaluate bias by term-based mode, but they fail to capture hidden biases in nuanced settings.
Approach:	They propose a dataset to assess bias at the semantic level that bias concepts are hidden within naturalistic, subtly framed contexts in real-world scenarios.
Outcome:	The proposed dataset shows that models reduce bias in response at term level, but reinforce bias in nuanced settings.

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Rasul Dent, Pedro Ortiz Suarez, Thibault Clérice, Benoît Sagot

Training Language Models to Critique With Multi-agent Feedback (2025.findings-emnlp)

Challenge:	a new pipeline can be used to create corpora for over-looked languages .
Approach:	We propose a new pipeline that can filter a single snapshot in twohours.
Outcome:	The proposed pipeline can filter a single snapshot in twohours.

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Tian Lan, Wenwei Zhang, Chengqi Lyu, Shuaibin Li, Chen Xu, Heyan Huang, Dahua Lin, Xian-Ling Mao, Kai Chen

RELIC: Enhancing Reward Model Generalization for Low-Resource Indic Languages with Few-Shot Examples (2025.findings-emnlp)

Challenge:	utilizing human annotations can enhance critique ability, but model-generated critiques suffer from inherent flaws due to complexity of critique . a new framework that leverages multi-agent feedback improves critique ability .
Approach:	They propose a framework that leverages multi-agent feedback to improve critique ability . they propose to use supervised fine-tuning and reinforcement learning to improve this capability .
Outcome:	The proposed framework improves critique ability in both supervised fine-tuning and reinforcement learning stages.

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Soumya Suvra Ghosal, Vaibhav Singh, Akash Ghosh, Soumyabrata Pal, Subhadip Baidya, Sriparna Saha, Dinesh Manocha

Invoke Interfaces Only When Needed: Adaptive Invocation for Large Language Models in Question Answering (2025.findings-emnlp)

Challenge:	a new reward model for low-resource Indic languages is proposed . a preference-based training approach is prohibitively expensive, authors say .
Approach:	a new in-context learning framework is proposed to train a retriever to select in-constext examples from low-resource Indic languages.
Outcome:	a new in-context learning framework for reward modeling in low-resource Indic languages is developed . the proposed framework outperforms existing examples on three preference datasets .

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Jihao Zhao, Chunlai Zhou, Daixuan Li, Shuaishuai Zu, Biao Qin

SQLSpace: A Representation Space for Text-to-SQL to Discover and Mitigate Robustness Gaps (2025.findings-emnlp)

Challenge:	a new metric is developed to pinpoint the moment of invocation when hallucinations arise in small LMs.
Approach:	They propose a metric that measures hallucinations during the generation process of small LMs.
Outcome:	The proposed metric outperforms baselines in hallucination detection across multiple QA datasets.

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Neha Srikanth, Victor Bursztyn, Puneet Mathur, Ani Nenkova

One More Modality: Does Abstract Meaning Representation Benefit Visual Question Answering? (2025.findings-emnlp)

Challenge:	SQLSpace is a representation for text-to-SQL examples derived with minimal human intervention.
Approach:	They introduce SQLSpace, a human-interpretable, generalizable, compact representation for text-to-SQL examples . they demonstrate that SQLSpace enables analysis that would be difficult with raw examples alone .
Outcome:	The proposed representations are human-interpretable and generalizable . they are used to evaluate models with a granularity beyond overall accuracy scores .

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Abhidip Bhattacharyya, Emma Markle, Shira Wein

DP-GTR: Differentially Private Prompt Protection via Group Text Rewriting (2025.findings-emnlp)

Challenge:	incorporating explicit semantic information, in the form of Abstract Meaning Representation graphs, can enhance VQA models.
Approach:	They augment two vision-language models with sentence- and document-level AMRs . they find that in well-resourced settings, models are negatively impacted by AMR .
Outcome:	The proposed model improves in well-resourced and low-resource settings with AMR graphs . the model achieves 13.1% relative gain using sentence-level AMRs compared with the smaller model .

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Mingchen Li, Heng Fan, Song Fu, Junhua Ding, Yunhe Feng

Legal Mathematical Reasoning with LLMs: Procedural Alignment through Two-Stage Reinforcement Learning (2025.findings-emnlp)

Challenge:	Existing methods for prompt privacy focus on document-level rewriting, neglecting rich, multi-granular representations of text.
Approach:	a framework that leverages local differential privacy and composition theorem via group text rewriting is proposed . the framework is compatible with existing rewrite techniques and is publicly available at anonymous.4open.science for reproducibility.
Outcome:	DP-GTR is the first framework to integrate document-level and word-level information while exploiting in-context learning to improve privacy and utility.

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Kepu Zhang, Guofu Xie, Weijie Yu, Mingyue Xu, Xu Tang, Yaxin Li, Jun Xu

ModelingAgent: Bridging LLMs and Mathematical Modeling for Real-World Challenges (2025.findings-emnlp)

Challenge:	Existing legal mathematical reasoning models lack structured numerical reasoning . existing models perform poorly on LexNum, while LexPam improves both mathematical accuracy and legal coherence.
Approach:	They propose a legal mathematical reasoning benchmark LexNum and LexPam to address this problem . LexPam is a two-stage reinforcement learning framework for efficient legal reasoning training.
Outcome:	The proposed framework improves mathematical accuracy and legal coherence . it also improves legal cohesion and generalizes effectively across tasks and domains.

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Cheng Qian, Hongyi Du, Hongru Wang, Xiusi Chen, Yuji Zhang, Avirup Sil, ChengXiang Zhai, Kathleen McKeown, Heng Ji

Numerical Reasoning

Challenge:	Existing benchmarks for large language models fail to reflect real-world complexity . existing benchmarks often fail to capture real-life problems .
Approach:	They propose a benchmark that features real-world-inspired, open-ended problems from competitions . they propose 'ModelingBench' that supports multiple valid solutions .
Outcome:	The proposed framework outperforms baselines and produces well-grounded, creative solutions.

Beyond Coarse Labels: Fine-Grained Problem Augmentation and Multi-Dimensional Feedback for Emotional Support Conversation (2025.findings-emnlp)

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Yuanchen Shi, Jiawang Hao, Fang Kong

FinHEAR: Human Expertise and Adaptive Risk-Aware Temporal Reasoning for Financial Decision-Making (2025.findings-emnlp)

Challenge:	Existing ESC datasets often use coarse-grained problem categories, limiting models’ ability to address users’ complex, overlapping challenges.
Approach:	They propose a generalizable fine-grained problem enhancement method that augments problem types, user scenarios, and profiles, enabling the construction of richer and more diverse ESC corpora.
Outcome:	The proposed method improves both automatic and human evaluation metrics across different models.

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Jiaxiang Chen, Mingxi Zou, Zhuo Wang, Qifan Wang, Danny Dongning Sun, Zhang Chi, Zenglin Xu

EvolKV: Evolutionary KV Cache Compression for LLM Inference (2025.findings-emnlp)

Challenge:	Large language models often overlook key behavioral patterns underlying human financial behavior.
Approach:	FinHEAR is a multi-agent framework for human expertise and Adaptive Risk-aware reasoning.
Outcome:	FinHEAR outperforms baseline models in trend forecasting and decision-making.

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Bohan Yu, Yekun Chai

Challenge:	Existing key-value (KV) cache compression methods ignore interplays between layer-specific feature patterns and task performance.
Approach:	They propose an adaptive framework for layer-wise, task-driven KV cache compression that optimizes memory efficiency and task performance.
Outcome:	EvolKV outperforms baseline methods on long-context tasks and surpasses heuristics by 7 percentage points on GSM8K.

A Survey on Sparse Autoencoders: Interpreting the Internal Mechanisms of Large Language Models (2025.findings-emnlp)

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Dong Shu, Xuansheng Wu, Haiyan Zhao, Daking Rai, Ziyu Yao, Ninghao Liu, Mengnan Du

Large Vision-Language Model Alignment and Misalignment: A Survey Through the Lens of Explainability (2025.findings-emnlp)

Challenge:	Sparse Autoencoders (SAEs) can disentangle complex features into more interpretable components.
Approach:	They propose to use Sparse Autoencoders to disentangle LLM features into more interpretable components.
Outcome:	The proposed method disentangles complex features into more interpretable components.

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Dong Shu, Haiyan Zhao, Jingyu Hu, Weiru Liu, Ali Payani, Lu Cheng, Mengnan Du

Attention Consistency for LLMs Explanation (2025.findings-emnlp)

Challenge:	Large Vision-Language Models have demonstrated remarkable capabilities in processing both visual and textual information.
Approach:	They examine the challenge of alignment and misalignment in LVLMs through an explainability lens.
Outcome:	The findings highlight the need for standardized evaluation protocols and in-depth explainability studies.

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Tian Lan, Jinyuan Xu, Xue He, Jenq-Neng Hwang, Lei Li

Confusion is the Final Barrier: Rethinking Jailbreak Evaluation and Investigating the Real Misuse Threat of LLMs (2025.findings-emnlp)

Challenge:	Existing interpretability methods face limitations such as low resolution and high computational cost.
Approach:	They propose a multi-layer attention consistency score to estimate the importance of input tokens in large language models.
Outcome:	The proposed heuristic achieves a favorable trade-off between interpretability quality and computational efficiency .

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Yu Yan, Sheng Sun, Zhe Wang, Yijun Lin, Zenghao Duan, Zhifei Zheng, Min Liu, Zhiyi Yin, Jianping Zhang

CCL-XCoT: An Efficient Cross-Lingual Knowledge Transfer Method for Mitigating Hallucination Generation (2025.findings-emnlp)

Challenge:	Large Language Models have been developed to deal with real-world crimes, but it remains unclear whether they internalize authentic knowledge or are forced to simulate toxic language patterns.
Approach:	They construct knowledge-intensive Q&A to investigate misuse threats of Large Language Models in terms of dangerous knowledge possession, harmful task planning utility, and harmfulness judgment robustness.
Outcome:	The findings raise concerns that jailbreak success is often attributable to a hallucination loop between jailbroken LLM and judger LLM .

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Zheng Weihua, Roy Ka-Wei Lee, Zhengyuan Liu, Wu Kui, AiTi Aw, Bowei Zou

Evaluating Step-by-step Reasoning Traces: A Survey (2025.findings-emnlp)

Challenge:	Multilingual Large Language Models (MLLMs) exhibit strong generalization across languages, yet they remain prone to hallucinations due to training data imbalances.
Approach:	They propose a cross-lingual Chain-of-Thought framework that enhances cross-linguistic alignment . the framework guides the model to reason in a high-resource language before generating answers in low-resourced language.
Outcome:	The proposed framework reduces hallucination rates by up to 62% and significantly improves factual knowledge transfer across language pairs.

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Jinu Lee, Julia Hockenmaier

Beyond Guilt: Legal Judgment Prediction with Trichotomous Reasoning (2025.findings-emnlp)

Challenge:	Existing evaluation practices are inconsistent, resulting in fragmented progress across evaluator design and benchmark development.
Approach:	a survey provides a comprehensive overview of step-by-step reasoning evaluation . existing evaluation practices are inconsistent, resulting in fragmented progress .
Outcome:	The proposed evaluation criteria are based on four top-level categories . the results are presented in a systematic review of the literature.

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Kepu Zhang, Haoyue Yang, Xu Tang, Weijie Yu, Jun Xu

Not Every Token Needs Forgetting: Selective Unlearning Balancing Forgetting and Utility in Large Language Models (2025.findings-emnlp)

Challenge:	Current legal large language models lack trichotomous reasoning capabilities due to the absence of an appropriate benchmark dataset.
Approach:	They propose a benchmark dataset for Legal Judgment Prediction with Innocent Verdicts that incorporates trichotomous dogmatics into zero-shot prompting and fine-tuning.
Outcome:	The proposed dataset extends three widely-used legal datasets through LLM-based augmentation and manual verification.

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Yixin Wan, Anil Ramakrishna, Kai-Wei Chang, Volkan Cevher, Rahul Gupta

DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster Management (2025.findings-emnlp)

Challenge:	Conventional unlearning approaches forget all tokens in a target document, including common tokens that carry general knowledge.
Approach:	They propose a method that identifies a critical subset of tokens within the forgetting set that is relevant to the unwanted information and unlearns only those tokens.
Outcome:	Experiments on two benchmarks and six baseline unlearning algorithms show that selective unlearning achieves effective unlearning on the targeted forget data.

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Kai Yin, Xiangjue Dong, Chengkai Liu, Lipai Huang, Yiming Xiao, Zhewei Liu, Ali Mostafavi, James Caverlee

Data or Language Supervision: What Makes CLIP Better than DINO? (2025.findings-emnlp)

Challenge:	Existing information retrieval benchmarks focus on general or specialized domains, such as medicine or finance, neglecting the unique linguistic complexity and diverse information needs encountered in disaster management scenarios.
Approach:	DisastIR is the first comprehensive IR evaluation benchmark specifically tailored for disaster management.
Outcome:	DisastIR covers 48 retrieval tasks derived from six search intents and eight general disaster categories . evaluations show no single model excelling universally .

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Yiming Liu, Yuhui Zhang, Dhruba Ghosh, Ludwig Schmidt, Serena Yeung-Levy

Do LLMs Understand Wine Descriptors Across Cultures? A Benchmark for Cultural Adaptations of Wine Reviews (2025.findings-emnlp)

Challenge:	CLIP outperforms self-supervised models like DINO as vision encoders for vision-language models (VLMs) but it remains unclear whether this advantage stems from CLIP’s language supervision or its much larger training data.
Approach:	Embedding analysis shows CLIP captures high-level semantics while DINO is more responsive to low-level features like colors and styles.
Outcome:	Embedding analysis shows that CLIP captures high-level semantics, while DINO is more responsive to low-level features like colors and styles.

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Chenye Zou, Xingyue Wen, Tianyi Hu, Qian Janice Wang, Daniel Hershcovich

DeFT-X: Denoised Sparse Fine-Tuning for Zero-Shot Cross-Lingual Transfer (2025.findings-emnlp)

Challenge:	Recent advances in large language models have opened the door to culture-aware language tasks.
Approach:	They propose to integrate regional taste preferences and culture-specific flavor descriptors into wine reviews across Chinese and English.
Outcome:	The proposed model incorporates regional taste preferences and culture-specific flavor descriptors into the translation process.

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Sona Elza Simon, Preethi Jyothi

Memory-enhanced Large Language Model for Cross-lingual Dependency Parsing via Deep Hierarchical Syntax Understanding (2025.findings-emnlp)

Challenge:	Prior studies have explored multiple approaches to combine task knowledge from task-specific data in a (high-resource) source language with language knowledge from unlabeled text in 'low-resourced' target language.
Approach:	They propose a composable sparse fine-tuning approach that learns task-specific and language-specific sparsen masks to select a subset of the pretrained model's parameters.
Outcome:	The proposed approach performs at par or outperforms SFT and other prominent cross-lingual transfer baselines.

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Jianjian Liu, Ying Li, Zhengtao Yu, Shun Su, Shengxiang Gao, Yuxin Huang

Developing and Utilizing a Large-Scale Cantonese Dataset for Multi-Tasking in Large Language Models (2025.findings-emnlp)

Challenge:	Experimental results show that our approach can significantly improve the parsing accuracy of all baseline models, leading to new state-of-the-art results.
Approach:	They propose a deep hierarchical syntax understanding approach to improve the cross-lingual semantic memory capability of large language models by implicitly aligning linguistic knowledge between source and target languages.
Outcome:	The proposed approach improves the cross-lingual semantic memory capability of large language models by combining implicit multi-task fine-tuning and explicit label bank guiding.

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Jiyue Jiang, Alfred Kar Yin Truong, Yanyu Chen, Qinghang Bao, Sheng Wang, Pengan Chen, Jiuming Wang, Lingpeng Kong, Yu Li, Chuan Wu

A Structured Framework for Evaluating and Enhancing Interpretive Capabilities of Multimodal LLMs in Culturally Situated Tasks (2025.findings-emnlp)

Challenge:	Cantonese is considered a low-resource language due to the dominance of Mandarin . rich colloquial vocabulary of Cantone, English loanwords, and code-switching characteristics add to the complexity of corpus collection and processing.
Approach:	We collect Cantonese texts from open source corpora, Hong Kong-specific forums, Wikipedia . we refine the model through supervised fine-tuning on curated Cantonesian tasks .
Outcome:	The model achieves state-of-the-art (SOTA) performance on four Cantonese benchmarks.

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Haorui Yu, Ramon Ruiz-Dolz, Qiufeng Yi

Train a Unified Multimodal Data Quality Classifier with Synthetic Data (2025.findings-emnlp)

Challenge:	Using a zero-shot classification model, we extracted multi-dimensional evaluative features from human expert critiques and used them to evaluate selected VLMs such as Llama, Qwen, or Gemini.
Approach:	They constructed a quantitative framework for Chinese painting critique by extracting multi-dimensional evaluative features from human expert critiques using a zero-shot classification model.
Outcome:	The framework was constructed by extracting features from human critiques using a zero-shot classification model.

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Weizhi Wang, Rongmei Lin, Shiyang Li, Colin Lockard, Ritesh Sarkhel, Sanket Lokegaonkar, Jingbo Shang, Xifeng Yan, Nasser Zalmout, Xian Li

Self-Improvement in Multimodal Large Language Models: A Survey (2025.findings-emnlp)

Challenge:	Multimodal Large Language Models are pre-trained on image-text caption data and interleaved document data.
Approach:	They propose to train an efficient MLLM as a Unified Mulitmodal Data Quality Classifier to filter image-text caption and interleaved data.
Outcome:	The proposed method enables efficient creation of sample-score pairs for caption and interleaved data to train UniFilter.

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Shijian Deng, Kai Wang, Tianyu Yang, Harsh Singh, Yapeng Tian

Towards Achieving Concept Completeness for Textual Concept Bottleneck Models (2025.findings-emnlp)

Challenge:	Using data and data, self-improvement for Large Language Models has improved model capabilities without significantly increasing costs.
Approach:	This survey provides a comprehensive overview of self-improvement for Large Language Models . it includes commonly used evaluations and downstream applications .
Outcome:	The authors provide a comprehensive overview of self-improvement in Multimodal LLMs.

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Milan Bhan, Yann Choho, Jean-Noël Vittaut, Nicolas Chesneau, Pierre Moreau, Marie-Jeanne Lesot

Challenge:	a novel TCBM generator is proposed to build concept labels in unsupervised manner using a small language model.
Approach:	They propose a complete textual concept bottleneck model that builds concept labels in unsupervised manner using a small language model.
Outcome:	The proposed model achieves striking results against existing models in terms of concept basis completeness and concept detection accuracy.

EmoBench-UA: A Benchmark Dataset for Emotion Detection in Ukrainian (2025.findings-emnlp)

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Daryna Dementieva, Nikolay Babakov, Alexander Fraser

Scientific Paper Retrieval with LLM-Guided Semantic-Based Ranking (2025.findings-emnlp)

Challenge:	EmoBench-UA is the first annotated dataset for emotion classification in Ukrainian texts.
Approach:	They introduce EmoBench-UA, the first annotated dataset for emotion detection in Ukrainian texts.
Outcome:	The first annotated dataset for emotion detection in Ukrainian texts is presented in this paper . the dataset was created through crowdsourcing using the Toloka.ai platform .

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Yunyi Zhang, Ruozhen Yang, Siqi Jiao, SeongKu Kang, Jiawei Han

DLIR: Spherical Adaptation for Cross-Lingual Knowledge Transfer of Sociological Concepts Alignment (2025.findings-emnlp)

Challenge:	Recent studies also use large language models (LLMs) for query understanding, but these methods lack grounding in corpus-specific knowledge and may generate unreliable or unfaithful content.
Approach:	They propose a paper retrieval framework that combines large language models (LLMs) with a concept-based semantic index to capture scientific concepts.
Outcome:	The proposed framework improves the performance of various base retrievers, surpasses strong existing LLM-based baselines, and remains highly efficient.

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Zeqiang Wang, Jon Johnson, Suparna De

Test-Time Steering for Lossless Text Compression via Weighted Product of Experts (2025.findings-emnlp)

Challenge:	Existing methods for identifying nuanced sociological concepts fail to capture domain-specific subtleties or require extensive parallel data.
Approach:	a new approach to aligning nuanced sociological concepts is proposed . a dual-branch LoRA approach captures core semantics and counteracts specific language perturbations.
Outcome:	a new approach outperforms baselines on cross-lingual sociological concept retrieval across 10 languages.

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Qihang Zhang, Muchen Li, Ziao Wang, Renjie Liao, Lele Wang

Zero-Shot Contextual Embeddings via Offline Synthetic Corpus Generation (2025.findings-emnlp)

Challenge:	gzip and neural compression models often lead to poor performance in unseen data.
Approach:	They propose a framework that performs Test-Time Steering via a Weighted Product of Experts (wPoE)
Outcome:	The proposed framework performs Test-Time Steering via a Weighted Product of Experts (wPoE) it integrates with any autoregressive language model, providing a practical solution for enhancing text compression across diverse data distributions.

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Philip Lippmann, Jie Yang

The Hallucination Tax of Reinforcement Finetuning (2025.findings-emnlp)

Challenge:	Context-aware embedding methods boost retrieval accuracy by conditioning on corpus statistics extracted from neighboring documents.
Approach:	They propose a zero-shot contextual adaptation framework that replaces real corpus access with a one-time offline synthesis of a compact proxy.
Outcome:	The proposed framework replaces real corpus access with offline synthesis of a compact proxy.

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Linxin Song, Taiwei Shi, Jieyu Zhao

Tracing Multilingual Factual Knowledge Acquisition in Pretraining (2025.findings-emnlp)

Challenge:	Reinforcement finetuning (RFT) has become a standard approach for enhancing the reasoning capabilities of large language models (LLMs).
Approach:	They propose to incorporate 10% SUM into RFT to restore appropriate refusal behavior with minimal accuracy trade-offs on solvable tasks.
Outcome:	The proposed approach reduces model refusal rates by more than 80%, which significantly increases model’s tendency to hallucinate.

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Yihong Liu, Mingyang Wang, Amir Hossein Kargaran, Felicia Körner, Ercong Nie, Barbara Plank, François Yvon, Hinrich Schuetze

Exploring the Vulnerability of the Content Moderation Guardrail in Large Language Models via Intent Manipulation (2025.findings-emnlp)

Challenge:	Large Language Models are capable of recalling multilingual factual knowledge, but most studies evaluate only the final model, leaving the development of factual recall and crosslingual consistency unexplored.
Approach:	They trace how factual recall and crosslingual consistency evolve during pretraining, focusing on OLMo-7B as a case study.
Outcome:	The results show that fact frequency is the key to a better recall of multilingual facts, regardless of language, and some low-frequency facts in non-English languages can still be correctly recalled.

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Jun Zhuang, Haibo Jin, Ye Zhang, Zhengjian Kang, Wenbin Zhang, Gaby G. Dagher, Haohan Wang

Examining Multilingual Embedding Models Cross-Lingually Through LLM-Generated Adversarial Examples (2025.findings-emnlp)

Challenge:	Prior work has shown that intent detection enhances LLMs’ moderation guardrails, but the robustness of these guardrail mechanisms under malicious manipulations remains under-explored.
Approach:	They propose a two-stage intent-based prompt-refinement framework that first transforms harmful inquiries into structured outlines and further reframes them into declarative-style narratives.
Outcome:	The proposed framework outperforms several cutting-edge jailbreak methods and evades even advanced Intent Analysis (IA) and Chain-of-Thought (CoT)-based defenses.

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Andrianos Michail, Simon Clematide, Rico Sennrich

EmoGist: Efficient In-Context Learning for Visual Emotion Understanding (2025.findings-emnlp)

Challenge:	Cross-Lingual Semantic Discrimination (CLSD) is a lightweight evaluation task that requires only parallel sentences and a Large Language Model (LLM) to generate adversarial distractors.
Approach:	They propose a lightweight task that requires only parallel sentences and a Large Language Model (LLM) to generate adversarial distractors.
Outcome:	The proposed task requires only parallel sentences and a Large Language Model (LLM) to generate adversarial distractors.

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Ronald Seoh, Dan Goldwasser

Soft Token Attacks Cannot Reliably Audit Unlearning in Large Language Models (2025.findings-emnlp)

Challenge:	EmoGist is a training-free, in-context learning method for visual emotion classification . context-dependent definitions of emotion labels could allow more accurate predictions of emotions .
Approach:	They introduce EmoGist, a training-free, in-context learning method for performing visual emotion classification with LVLMs.
Outcome:	The proposed method improves micro F1 scores and macro F1 with LVLMs.

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Haokun Chen, Sebastian Szyller, Weilin Xu, Nageen Himayat

Bridging the Editing Gap in LLMs: FineEdit for Precise and Targeted Text Modifications (2025.findings-emnlp)

Challenge:	Recent work shows that soft token attacks can extract unlearned information from large language models.
Approach:	They show that soft token attacks can extract unlearned information from LLMs .
Outcome:	The proposed attacks can extract unlearned information from large language models .

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Yiming Zeng, Wanhao Yu, Zexin Li, Tao Ren, Yu Ma, Jinghan Cao, Xiyan Chen, Tingting Yu

Challenge:	a recent study shows that large language models can perform precise text editing tasks.
Approach:	InstrEditBench is a benchmark dataset that compares 30,000 structured editing tasks . experimental evaluations show FineEdit outperforms state-of-the-art models .
Outcome:	The proposed model outperforms state-of-the-art models on single-turn edits and mistral-7B-OpenOrca on direct edits.

LLM-based Conversational Recommendation Agents with Collaborative Verbalized Experience (2025.findings-emnlp)

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Yaochen Zhu, Harald Steck, Dawen Liang, Yinhan He, Nathan Kallus, Jundong Li

Hardware-Aware Parallel Prompt Decoding for Memory-Efficient Acceleration of LLM Inference (2025.findings-emnlp)

Challenge:	Large language models (LLMs) have demonstrated impressive zero-shot capabilities in conversational recommender systems (CRS).
Approach:	They propose LLM-based CRS-based LLMs with Collaborative Verbalized Experience to enhance historical conversations by sampling trajectories of LLM agents on historical queries and establishing verbalized experience banks .
Outcome:	The proposed system improves on existing approaches to enhancing historical conversations by leveraging trajectories and verbalized experiences from LLMs on historical queries and user feedback.

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Hao Mark Chen, Wayne Luk, Yiu Ka Fai Cedric, Rui Li, Konstantin Mishchenko, Stylianos Venieris, Hongxiang Fan

Measuring Sycophancy of Language Models in Multi-turn Dialogues (2025.findings-emnlp)

Challenge:	Auto-regressive decoding of Large Language Models results in significant overheads in hardware performance . a novel parallel prompt decoding approach is proposed to overcome these limitations .
Approach:	They propose a parallel prompt decoding that uses a single model for speculation and verification.
Outcome:	The proposed approach speeds up auto-regressive decoding of large language models 2.49 times . it can be used on mobileLlama to Vicuna-13B on a wide range of benchmarks .

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Jiseung Hong, Grace Byun, Seungone Kim, Kai Shu

On the Role of Entity and Event Level Conceptualization in Generalizable Reasoning: A Survey of Tasks, Methods, Applications, and Future Directions (2025.findings-emnlp)

Challenge:	Prior research on sycophancy has focused on single-turn factual correctness, overlooking the dynamics of real-world interactions.
Approach:	They propose a new evaluation suite that assesses sycophantic behavior in multi-turn, free-form conversational settings.
Outcome:	The proposed evaluation suite measures how quickly a model conforms to the user and how frequently it shifts its stance under sustained user pressure.

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Weiqi Wang, Tianqing Fang, Haochen Shi, Baixuan Xu, Wenxuan Ding, Liyu Zhang, Wei Fan, Jiaxin Bai, Haoran Li, Xin Liu, Yangqiu Song

Mitigating Visual Knowledge Forgetting in MLLM Instruction-tuning via Modality-decoupled Gradient Descent (2025.findings-emnlp)

Challenge:	Conceptualization is a fundamental element of human cognition and plays a pivotal role in generalizable reasoning.
Approach:	They propose to categorize different types of conceptualizations into four levels based on the types of instances being conceptualized.
Outcome:	The proposed categorization of different types of conceptualizations into four levels based on the types of instances being conceptualized .

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Junda Wu, Yuxin Xiong, Xintong Li, Yu Xia, Ruoyu Wang, Yu Wang, Tong Yu, Sungchul Kim, Ryan A. Rossi, Lina Yao, Jingbo Shang, Julian McAuley

PathoHR: Hierarchical Reasoning for Vision-Language Models in Pathology (2025.findings-emnlp)

Challenge:	Existing fine-tuning and continual learning methods compress visual representations and emphasize task alignment over visual retention.
Approach:	They propose a modality-decoupled gradient descent (MDGD) that regulates gradient updates to preserve effective rank of visual features and explicitly disentangles visual learning from task-specific alignment.
Outcome:	The proposed model reduces visual forgetting and improves visual retention . it disentangles visual learning from task-specific alignment and preserves effective rank .

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Yating Huang, Ziyan Huang, Lintao Xiang, Qijun Yang, Hujun Yin

“What’s Up, Doc?”: Analyzing How Users Seek Health Information in Large-Scale Conversational AI Datasets (2025.findings-emnlp)

Challenge:	Current vision-language (VL) models fail to capture complex reasoning required for interpreting structured pathological reports.
Approach:	They propose a pathology-specific VL training scheme that generates enhanced and perturbed samples for multimodal contrastive learning.
Outcome:	The proposed approach achieves state-of-the-art performance on PathoHR-Bench and six additional pathology datasets, highlighting its effectiveness in fine-grained pathology representation.

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Akshay Paruchuri, Maryam Aziz, Rohit Vartak, Ayman Ali, Best Uchehara, Xin Liu, Ishan Chatterjee, Monica Agrawal

Dynamic Evaluation for Oversensitivity in LLMs (2025.findings-emnlp)

Challenge:	a growing number of people are seeking healthcare information from large language models via chatbots, yet the nature and inherent risks of these interactions remain unexplored.
Approach:	They use a curated dataset of 11K real-world conversations composed of 25K user messages to analyze user interactions across 21 health specialties.
Outcome:	The proposed dataset consists of 11K real-world conversations composed of 25K user messages.

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Sophia Xiao Pu, Sitao Cheng, Xin Eric Wang, William Yang Wang

Self-Correcting Code Generation Using Small Language Models (2025.findings-emnlp)

Challenge:	Existing benchmarks rely on static datasets that degrade over time as models evolve, leading to data contamination and diminished evaluative power.
Approach:	They construct a framework that generates model-specific challenging datasets and aggregates them across diverse LLM families.
Outcome:	The framework captures emerging defensive patterns and aligns with each model’s unique behavior.

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Jeonghun Cho, Deokhyung Kang, Hyounghun Kim, Gary Lee

A Unified Framework for N-ary Property Information Extraction in Materials Science (2025.findings-emnlp)

Challenge:	a recent study has demonstrated that self-correction is a powerful tool for code generation, but whether it is effective for smaller models remains unexplored.
Approach:	They propose a method that trains small language models to maintain correct outputs while progressively correcting incorrect outputs as turns proceed.
Outcome:	The proposed approach improves the ability of small language models for multi-turn code correction.

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Van-Thuy Phi, Yuji Matsumoto

A Benchmark for Translations Across Styles and Language Variants (2025.findings-emnlp)

Challenge:	a framework for extracting n-ary property information from materials science literature is proposed . the framework addresses the critical challenge of capturing complex relationships that span multiple sentences.
Approach:	They propose a framework for extracting n-ary property information from materials science literature . they propose three complementary approaches to capture complex relationships that span multiple sentences .
Outcome:	The proposed framework outperforms existing methods in n-ary property extraction tasks.

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Xin Tan, Bowei Zou, AiTi Aw

ManuSearch: Democratizing Deep Search in Large Language Models with a Transparent and Open Multi-Agent Framework (2025.findings-emnlp)

Challenge:	lack of comprehensive evaluation benchmarks has hindered progress in this field . lack of evaluation benchmarking has hinder MT's ability to generate accurate outputs .
Approach:	They evaluate translations across semantic preservation, cultural and regional specificity, expression style, and fluency at both the word and sentence levels.
Outcome:	The proposed evaluation framework is validated on translations of state-of-the-art large language models .

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Lisheng Huang, Yichen Liu, Jinhao Jiang, Rongxiang Zhang, Jiahao Yan, Junyi Li, Xin Zhao

Proactive User Information Acquisition via Chats on User-Favored Topics (2025.findings-emnlp)

Challenge:	Existing systems with opaque architectures are limiting deep search capabilities for web-augmented large language models.
Approach:	They propose a transparent and modular multi-agent framework to democratize deep search for LLMs.
Outcome:	The proposed framework outperforms open-source systems in deep reasoning tasks.

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Shiki Sato, Jun Baba, Asahi Hentona, Shinji Iwata, Akifumi Yoshimoto, Koichiro Yoshino

Evaluating Text Generation Quality Using Spectral Distances of Surprisal (2025.findings-emnlp)

Challenge:	PIA tasks require a system to acquire user information without making the user feel abrupt while engaging in a chat on a predefined topic.
Approach:	They propose a task to acquire user's answers to predefined questions without making the user feel abrupt while engaging in a chat on a predefined topic.
Outcome:	The proposed system outperforms LLMs prompted with task instructions in a dataset of 650 PIA chats and shows that it is reasonably accurate.

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Zhichen Liu, Yongyuan Li, Yang Xu, Yu Wang, Yingfang Yuan, Zuhao Yang

NLP-ADBench: NLP Anomaly Detection Benchmark (2025.findings-emnlp)

Challenge:	Existing metric fails to capture text surprisal, but FACE-2 produces stronger agreement with human preferences.
Approach:	They propose a new automatic evaluation metric for open-ended text generation . they propose metric that extracts the dynamic patterns (spectrum) of text surprisal .
Outcome:	The proposed metric outperforms existing methods in revealing the model scaling effect . it produces stronger agreement with human preferences from a large human-annotated dataset .

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Yuangang Li, Jiaqi Li, Zhuo Xiao, Tiankai Yang, Yi Nian, Xiyang Hu, Yue Zhao

Toward Inclusive Language Models: Sparsity-Driven Calibration for Systematic and Interpretable Mitigation of Social Biases in LLMs (2025.findings-emnlp)

Challenge:	Anomaly detection (AD) is an important machine learning task, but its effectiveness in detecting harmful content, phishing attempts, and spam reviews is limited.
Approach:	They introduce NLP-ADBench, the most comprehensive NLP anomaly detection benchmark to date . it includes eight curated datasets and 19 state-of-the-art algorithms .
Outcome:	The NLP-ADBench benchmark includes 19 state-of-the-art methods and 8 curated datasets . no single model dominates across all datasets, indicating need for automated model selection .

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Prommy Sultana Hossain, Chahat Raj, Ziwei Zhu, Jessica Lin, Emanuela Marasco

Table-Text Alignment: Explaining Claim Verification Against Tables in Scientific Papers (2025.findings-emnlp)

Challenge:	a new method to mitigate stereotypical bias in large language models is needed . inherent biases from training on vast Internet datasets can amplify harmful stereotypes .
Approach:	They propose a method to identify stereotypical bias in decoder-only transformer models . they apply a localization mechanism that correlates internal activations with a new Context Influence score .
Outcome:	The proposed method reduces stereotypical biases on BBQ, StereoSet, and CrowS-Pairs while improving reasoning performance on MMLU by 10%.

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Xanh Ho, Sunisth Kumar, Yun-Ang Wu, Florian Boudin, Atsuhiro Takasu, Akiko Aizawa

DCRM: A Heuristic to Measure Response Pair Quality in Preference Optimization (2025.findings-emnlp)

Challenge:	predicting the final label alone is insufficient and offers limited interpretability.
Approach:	They propose to reframe table–text alignment as an explanation task requiring models to identify the table cells essential for claim verification.
Outcome:	The proposed taxonomy improves claim verification performance and most LLMs fail to recover human-aligned rationales, suggesting that their predictions do not stem from faithful reasoning.

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Chengyu Huang, Tanya Goyal

Advancing Reasoning with Off-the-Shelf LLMs: A Semantic Structure Perspective (2025.findings-emnlp)

Challenge:	Recent research has attempted to associate preference optimization (PO) performance with the underlying preference datasets.
Approach:	They propose a best-of-N2 pairing method that selects response pairs with the highest DCRM to measure the quality of a response pair for PO.
Outcome:	The proposed method produces training datasets that can further improve models’ performance on AlpacaEval, MT-Bench, and Arena-Hard over the existing training sets.

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Pengfei He, Zitao Li, Yue Xing, Yaliang Li, Jiliang Tang, Bolin Ding

LLM-based Open Domain Planning by Leveraging Entity-Attribute-Level Domain Models (2025.findings-emnlp)

Challenge:	Existing reasoning models suffer from hallucinations and unfaithfulness, whereas general LLMs perform suboptimal on complex tasks.
Approach:	They propose a structure analysis method that helps LLMs better understand the question structure and guide the problem-solving process.
Outcome:	The proposed method improves zero-shot performance on knowledge-intensive and mathematical tasks while demonstrating strong robustness against corrupted reasoning paths.

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Dongning Rao, Songlin He, Zhihua Jiang, Ruishi Liang

Challenge:	Currently, large language models (LLMs) based on Open domain Natural language planning have limited application potential.
Approach:	They propose a dataset with a baseline for Open domain Natural language planning . the dataset provides the largest dataset for textual procedures to date .
Outcome:	The proposed dataset provides the largest dataset for textual procedures to date . it leverages entity-attribute-level action models to reveal relevant physical properties .

DICP: Deep In-Context Prompt for Event Causality Identification (2025.findings-emnlp)

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Lin Mu, Jun Shen, Li Ni, Lei Sang, Zhize Wu, Peiquan Jin, Yiwen Zhang

Seeing is Believing: Emotion-Aware Audio-Visual Language Modeling for Expressive Speech Generation (2025.findings-emnlp)

Challenge:	Existing prompt-learning-based methods concatenate in-context examples only at the input layer, limiting the model’s ability to capture abstract semantic cues necessary for identifying complex causal relationships.
Approach:	They propose a model that injects in-context examples into the deeper layer of a pre-trained language model (PLM) this model leverages hierarchical semantic representations formed in deeper layers, thereby enhancing its capacity to learn high-level causal abstractions.
Outcome:	The proposed model improves on two widely used datasets and shows that it can learn high-level causal abstractions.

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Weiting Tan, Jiachen Lian, Hirofumi Inaguma, Paden Tomasello, Philipp Koehn, Xutai Ma

GRV-KBQA: A Three-Stage Framework for Knowledge Base Question Answering with Decoupled Logical Structure, Semantic Grounding and Structure-Aware Validation (2025.findings-emnlp)

Challenge:	AVLM integrates full-face visual cues into a pre-trained expressive speech model.
Approach:	They propose an Audio-Visual Language Model (AVLM) for expressive speech generation by integrating full-face visual cues into a pre-trained expressive speech model.
Outcome:	The proposed model incorporates full-face visual cues into a pre-trained expressive speech model.

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Yuhang Tian, Pan Yang, Dandan Song, Zhijing Wu, Hao Wang

Improving Prompt Generalization for Cross-prompt Essay Trait Scoring from the Scoring-invariance Perspective (2025.findings-emnlp)

Challenge:	Existing methods for Knowledge Base Question Answering generate non-executable queries and inefficiencies in query execution.
Approach:	a framework that decouples logical structure generation from semantic grounding is proposed . the framework explicitly enforces KB constraints to improve alignment between generated logical forms and KB structures.
Outcome:	GRV-KBQA decouples logical structure generation from semantic grounding and incorporates structure-aware validation to enhance accuracy.

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Jiong Wang, Shengquan Yu

When Format Changes Meaning: Investigating Semantic Inconsistency of Large Language Models (2025.findings-emnlp)

Challenge:	Existing research on cross-prompt trait essay scoring focuses on improving model generalization by obtaining prompt-invariant representations.
Approach:	They propose a scoring-invariant learning objective that encourages the model to focus on intrinsic information within the essay that reflects its quality during training, thereby learning generic scoring features.
Outcome:	The proposed scoring-invariant learning objective encourages the model to focus on intrinsic information within the essay that reflects its quality during training, thereby learning generic scoring features.

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Cheongwoong Kang, Jongeun Baek, Yeonjea Kim, Jaesik Choi

ASTPrompter: Preference-Aligned Automated Language Model Red-Teaming to Generate Low-Perplexity Unsafe Prompts (2025.findings-emnlp)

Challenge:	Large language models are vulnerable to semantic inconsistency, a study finds . minor formatting variations result in divergent predictions for semantically equivalent inputs.
Approach:	They evaluate LLMs for semantic inconsistency and find they remain vulnerable . they propose to use mechanistic analysis to develop models that improve their reliability .
Outcome:	The proposed model is vulnerable to semantic inconsistency, the authors show . their model is brittle even in state-of-the-art models, they say .

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Amelia Hardy, Houjun Liu, Allie Griffith, Bernard Lange, Duncan Eddy, Mykel Kochenderfer

How Do Large Language Models Perform on PDE Discovery: A Coarse-to-fine Perspective (2025.findings-emnlp)

Challenge:	Existing red-teaming approaches prioritize high attack success rate, resulting in high-perplexity prompts.
Approach:	a new method uses contrastive preference learning to train an attacker to maintain low perplexity while achieving a high attack success rate.
Outcome:	ASTPrompter achieves 5.1 times higher attack success rate on Llama-8.1B . low-perplexity attacks are more difficult to filter and more likely to arise during benign usage .

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Xiao Luo, Changhu Wang, Yizhou Sun, Wei Wang

Rethinking Data Selection at Scale: Random Selection is Almost All You Need (2025.findings-emnlp)

Challenge:	Existing methods to identify partial differential equations using large language models suffer from performance degradation under extreme data scarcity.
Approach:	They propose a framework to use large language models to identify underlying partial differential equations out of very limited observations of a physical system.
Outcome:	The proposed framework is based on a coarse-to-fine paradigm to discover PDEs out of very limited observations of a physical system.

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Tingyu Xia, Bowen Yu, Kai Dang, An Yang, Yuan Wu, Yuan Tian, Yi Chang, Junyang Lin

PromptKeeper: Safeguarding System Prompts for LLMs (2025.findings-emnlp)

Challenge:	Existing data selection techniques are designed for small data pools, a study finds . filtering data by token length is an efficient method for improving results .
Approach:	They use self-scoring methods that do not rely on external help to perform fine-tuning . they also find that filtering data by token length offers a stable and efficient method .
Outcome:	The proposed methods outperform random selection on large datasets on large data pools.

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Zhifeng Jiang, Zhihua Jin, Guoliang He

Automating eHMI Action Design with LLMs for Automated Vehicle Communication (2025.findings-emnlp)

Challenge:	PromptKeeper is a defense mechanism designed to safeguard system prompts . adversarial and regular queries can exploit LLM vulnerabilities to expose hidden prompts.
Approach:	PromptKeeper is a defense mechanism designed to safeguard system prompts . it detects both explicit and subtle leakage and regenerates responses using a dummy prompt .
Outcome:	PromptKeeper detects and mitigates side-channel vulnerabilities when prompts are exposed . it regenerates responses using a dummy prompt, ensuring outputs remain indistinguishable from typical interactions .

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Ding Xia, Xinyue Gui, Fan Gao, Dongyuan Li, Mark Colley, Takeo Igarashi

A Dynamic Fusion Model for Consistent Crisis Response (2025.findings-emnlp)

Challenge:	Currently, eHMIs employ predefined text messages and manually designed actions to perform these messages . this limits the real-world deployment of ehMIs, where adaptability in dynamic scenarios is essential.
Approach:	They propose a pipeline that integrates large language models and 3D renderers to generate executable actions for controlling eHMIs and rendering action clips.
Outcome:	The proposed pipeline integrates large language models and 3D renderers to generate executable actions for controlling eHMIs and rendering action clips.

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Xiaoying Song, Anirban Saha Anik, Eduardo Blanco, Vanessa Frias-Martinez, Lingzi Hong

Challenge:	a critical yet often overlooked factor is the consistency of response style . few studies have explored methods for maintaining stylistic consistency across generated responses .
Approach:	They propose a metric for evaluating style consistency and introduce a method for fusion-based generation .
Outcome:	The proposed method outperforms baselines in response quality and stylistic uniformity.

UIOrchestra: Generating High-Fidelity Code from UI Designs with a Multi-agent System (2025.findings-emnlp)

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Chuhuai Yue, Jiajun Chai, Yufei Zhang, Zixiang Ding, Xihao Liang, Peixin Wang, Shihai Chen, Wang Yixuan, null Wangyanping, Guojun Yin, Wei Lin

CrossQG: Improving Difficulty-Controllable Question Generation through Consistency Enhancement (2025.findings-emnlp)

Challenge:	Recent advances in large language models have significantly improved automated code generation . however, the translation of complex mobile UI designs into high-fidelity front-end code remains a challenge .
Approach:	They propose a collaborative multi-agent system to reconstruct static single-page apps from mockups.
Outcome:	The proposed system outperforms existing methods in reconstructing complex app pages . the code and data will be released upon paper acceptance .

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Kunze Li, Yu Zhang

Progressive Facial Granularity Aggregation with Bilateral Attribute-based Enhancement for Face-to-Speech Synthesis (2025.findings-emnlp)

Challenge:	Large language models can generate questions with controlled difficulty, but they often fail to align with the given target difficulty.
Approach:	They propose a question generation method that requires no tuning of generator parameters yet significantly improves difficulty consistency.
Outcome:	The proposed method outperforms several mainstream methods on high-quality question answering datasets and achieves superior consistency with target difficulty.

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Yejin Jeon, Youngjae Kim, Jihyun Lee, Hyounghun Kim, Gary Lee

Speaking at the Right Level: Literacy-Controlled Counterspeech Generation with RAG-RL (2025.findings-emnlp)

Challenge:	Existing methods for generating speech from facial images rely on pre-trained visual encoders and fine-tune them to align with speech embeddings.
Approach:	They propose to derive corresponding voices from facial images using face-to-voice synthesis, which derives corresponding voice from facial image.
Outcome:	The proposed approach significantly improves face-voice congruence and synthesis stability.

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Xiaoying Song, Anirban Saha Anik, Dibakar Barua, Pengcheng Luo, Junhua Ding, Lingzi Hong

FNSCC: Fuzzy Neighborhood-Aware Self-Supervised Contrastive Clustering for Short Text (2025.findings-emnlp)

Challenge:	Existing approaches produce uniform responses, ignoring that health literacy levels affect the accessibility and effectiveness of counterspeech.
Approach:	They propose a Controlled-Literacy framework that generates counterspeech adapted to different health literacy levels.
Outcome:	The proposed framework outperforms baselines by generating more accessible counterspeech to health misinformation.

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Zijian Zheng, Yonghe Lu, Jian Yin

AuraDial: A Large-Scale Human-Centric Dialogue Dataset for Chinese AI Psychological Counseling (2025.findings-emnlp)

Challenge:	Short texts pose significant challenges for clustering due to semantic sparsity, limited context and fuzzy category boundaries.
Approach:	proposed framework incorporates neighborhood information at instance and cluster levels . a cluster-level framework introduces fuzzy neighborhood-aware weighting .
Outcome:	The proposed framework outperforms state-of-the-art models on short texts . it excludes neighbors from negative sample set to enhance inter-cluster separability .

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Xiantao Zhang

TS-SQL: Test-driven Self-refinement for Text-to-SQL (2025.findings-emnlp)

Challenge:	AuraDial is a large-scale, human-centric dialogue dataset for Chinese AI psychological counseling .
Approach:	They propose a large-scale human-centric dialogue dataset for Chinese AI psychological counseling . they propose rephrasing-based data generation methodology to foster more human-like responses .
Outcome:	The proposed dataset outperforms other datasets in generating human-like responses.

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Wenbo Xu, Haifeng Zhu, Liang Yan, Chuanyi Liu, Peiyi Han, Shaoming Duan, Jeff Z. Pan

DemonAgent: Dynamically Encrypted Multi-Backdoor Implantation Attack on LLM-based Agent (2025.findings-emnlp)

Challenge:	null
Approach:	null
Outcome:	null

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Pengyu Zhu, Zhenhong Zhou, Yuanhe Zhang, Shilinlu Yan, Kun Wang, Sen Su

MotivGraph-SoIQ: Integrating Motivational Knowledge Graphs and Socratic Dialogue for Enhanced LLM Ideation (2025.findings-emnlp)

Challenge:	a new method for detecting advanced backdoors is proposed to bypass safety audits.
Approach:	They propose a backdoor implantation strategy that introduces dynamic encryption to bypass safety audits.
Outcome:	The proposed method achieves an attack success rate approaching 100% while maintaining a detection rate of 0%.

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Xinping Lei, Tong Zhou, Yubo Chen, Kang Liu, Jun Zhao

ExpertGenQA: Open-ended QA generation in Specialized Domains (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have limitations in grounding ideas and mitigating confirmation bias during refinement.
Approach:	They propose a framework that integrates a Motivational Knowledge Graph with a Q-Driven Socratic Ideator to enhance LLM ideation.
Outcome:	The proposed framework enhances LLM ideation by integrating a Motivational Knowledge Graph with a Q-Driven Socratic Ideator.

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Haz Sameen Shahgir, Chansong Lim, Jia Chen, Evangelos E. Papalexakis, Yue Dong

VisCoder: Fine-Tuning LLMs for Executable Python Visualization Code Generation (2025.findings-emnlp)

Challenge:	Existing methods for generating high-quality question–answer (QA) pairs yield generic or shallow questions that fail to reflect the depth and structure of expert-written examples.
Approach:	They propose a question-answer generation protocol that combines few-shot prompting with dual categorization by topic and question style to produce more diverse and cognitively meaningful QA pairs.
Outcome:	The proposed protocol achieves twice the efficiency of standard few-shot methods while maintaining 94.4% topic coverage.

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Yuansheng Ni, Ping Nie, Kai Zou, Xiang Yue, Wenhu Chen

Conversational Education at Scale: A Multi-LLM Agent Workflow for Procedural Learning and Pedagogic Quality Assessment (2025.findings-emnlp)

Challenge:	Existing instruction-tuning datasets lack execution-grounded supervision and offer limited support for iterative code correction.
Approach:	They propose a large-scale instruction tuning dataset for Python-based visualization and self-correction.
Outcome:	The proposed dataset outperforms strong open-source baselines and proprietary models like GPT-4o-mini.

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Jiahuan Pei, Fanghua Ye, Xin Sun, Wentao Deng, Koen Hindriks, Junxiao Wang

Visual Program Distillation with Template-Based Augmentation (2025.findings-emnlp)

Challenge:	Existing work on large language models lacks scalability and assesses pedagogic quality.
Approach:	They propose a multi-agent workflow leveraging large language models to simulate interactive teaching-learning conversations.
Outcome:	The proposed workflow integrates teacher and learner agents, an interaction manager, and an evaluator to facilitate procedural learning and assess pedagogic quality.

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Michal Shlapentokh-Rothman, Yu-Xiong Wang, Derek Hoiem

NeighXLM: Enhancing Cross-Lingual Transfer in Low-Resource Languages via Neighbor-Augmented Contrastive Pretraining (2025.findings-emnlp)

Challenge:	Adapting visual programming to specialized tasks or domains remains challenging due to high annotation and inference costs.
Approach:	They propose a low-cost visual program distillation method that can be used for models with at most 1 billion parameters and requires no human-generated program annotations.
Outcome:	The proposed method can generate high-quality visual programs with no human-generated annotations with a relatively small amount of question/answer data.

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Sicheng Wang, Wenyi Wu, Zibo Zhang

ICLER: Intent CLassification with Enhanced Reasoning (2025.findings-emnlp)

Challenge:	NeighXLM is a neighbor-augmented contrastive pretraining framework . it exploits intra-language semantic relationships captured during pretraining to construct high-quality positive pairs.
Approach:	They propose a neighbor-augmented contrastive pretraining framework that mines semantic neighbors from unlabeled corpora to enrich target-language supervision.
Outcome:	The proposed framework enriches target-language supervision by mining semantic neighbors from unlabeled corpora.

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Dezheng Gao, Dong Xiaozheng, SHuangtao Yang, Bo Fu

PreGenie: An Agentic Framework for High-quality Visual Presentation Generation (2025.findings-emnlp)

Challenge:	Existing methods for intent classification are inadequate in identifying micro-grained intentions . ICLER is based on In-Context Learning, but it is inadequate in enterprise vertical domains .
Approach:	They propose an intent classification method with enhanced reasoning that optimizes the embedding model to capture subtle sentence-level information.
Outcome:	The proposed method outperforms existing methods in intent identification tasks in vertical domains.

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Xiaojie Xu, Xinli Xu, Sirui Chen, Haoyu Chen, Fan Zhang, Ying-Cong Chen

RIVAL: Reinforcement Learning with Iterative and Adversarial Optimization for Machine Translation (2025.findings-emnlp)

Challenge:	Visual presentations are vital for effective communication, but they are limited by their complexity and lack of visual understanding.
Approach:	a new framework is proposed to generate high-quality visual presentations using multimodal large language models.
Outcome:	The proposed framework outperforms existing models in multimodal understanding and content consistency.

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Tianjiao Li, Mengran Yu, Chenyu Shi, Yanjun Zhao, Xiaojing Liu, Qi Zhang, Xuanjing Huang, Qiang Zhang, Jiayin Wang

MRAG: A Modular Retrieval Framework for Time-Sensitive Question Answering (2025.findings-emnlp)

Challenge:	Using reinforcement learning from human feedback, large language models perform poorly when applied to colloquial subtitle translation tasks.
Approach:	They propose an adversarial training framework that iteratively updates the offline reward model and the online LLM to improve training outcomes.
Outcome:	The proposed training framework significantly improves upon translation baselines.

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Siyue Zhang, Yuxiang Xue, Yiming Zhang, Xiaobao Wu, Anh Tuan Luu, Chen Zhao

CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models (2025.findings-emnlp)

Challenge:	Existing methods for answering time-sensitive questions lack temporal reasoning . existing methods struggle with these time-intensive questions, authors say .
Approach:	They propose a temporal-based question-answering framework that integrates temporal perturbations and gold evidence labels into a question processing framework.
Outcome:	The proposed framework outperforms baseline retrieval methods in retrieval performance.

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Feiyang Li, Peng Fang, Zhan Shi, Arijit Khan, Fang Wang, Weihao Wang, null Zhangxin-hw, Cui Yongjian

TabDSR: Decompose, Sanitize, and Reason for Complex Numerical Reasoning in Tabular Data (2025.findings-emnlp)

Challenge:	Chain-of-thought reasoning has two key limitations: lack of reliability when solely relying on LLM-generated reasoning chains and interference from natural language reasoning steps with the models’ inference logic.
Approach:	They propose a chain-of-thought reasoning framework with three key designs to address these issues.
Outcome:	The proposed framework improves the performance of large language models on complex tasks by incorporating knowledge graphs and learnable knowledge case-aware RAG.

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Changjiang Jiang, Fengchang Yu, Haihua Chen, Wei Lu, Jin Zeng

Chain-of-Thought Matters: Improving Long-Context Language Models with Reasoning Path Supervision (2025.findings-emnlp)

Challenge:	Large language models often underperform due to complex queries, noisy data, and limited numerical capabilities.
Approach:	They propose a framework that integrates seamlessly with mainstream LLMs to improve tabular reasoning.
Outcome:	The proposed framework outperforms existing methods in state-of-the-art analysis.

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Dawei Zhu, Xiyu Wei, Guangxiang Zhao, Wenhao Wu, Haosheng Zou, Junfeng Ran, null XWang, Lin Sun, Xiangzheng Zhang, Sujian Li

Multimodal Document-level Triple Extraction via Dynamic Graph Enhancement and Relation-Aware Reflection (2025.findings-emnlp)

Challenge:	Recent advances in Large Language Models (LLMs) have highlighted the challenge of handling long-context tasks.
Approach:	They propose a chain-of-thought framework that teaches models to generate high-quality reasoning paths for enhanced long-context performance.
Outcome:	The proposed framework generalizes across most long-context scenarios and amplifys with increasing context length.

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Xiang Li, Runhai Jiao, Zhou Changyu, Shoupeng Qiao, Ruojiao Qiao, Ruifan Li

Distill Visual Chart Reasoning Ability from LLMs to MLLMs (2025.findings-emnlp)

Challenge:	Existing methods for extracting structured triples knowledge from multimodal documents face limitations in simultaneously processing long textual content and multiple associated images for triple extraction.
Approach:	They propose a multimodal document-level triple extraction framework that integrates multimodal text and visual content into a large language model and injects the global information and external knowledge into the model.
Outcome:	The proposed framework outperforms the state-of-the-art methods and fills the gap in multimodal document extraction.

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Wei He, Zhiheng Xi, Wanxu Zhao, Xiaoran Fan, Yiwen Ding, Zifei Shan, Tao Gui, Qi Zhang, Xuanjing Huang

FlowMalTrans: Unsupervised Binary Code Translation for Malware Detection Using Flow-Adapter Architecture (2025.findings-emnlp)

Challenge:	a new method for generating chart annotations is proposed to improve visual reasoning in multimodal large language models.
Approach:	They propose a code-as-intermediary translation method for distilling visual reasoning abilities from LLMs to MLLMs.
Outcome:	The proposed method is cost-effective, efficient and scalable.

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Minghao Hu, Junzhe Wang, Weisen Zhao, Qiang Zeng, Lannan Luo

AdaTP: Attention-Debiased Token Pruning for Video Large Language Models (2025.findings-emnlp)

Challenge:	Using deep learning to detect malware has attracted great attention due to its notable performance.
Approach:	a new approach uses Neural Machine Translation and Normalizing Flows to apply deep learning to malware detection.
Outcome:	The proposed approach reduces the burden of data collection by enabling malware detection across multiple ISAs.

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Fengyuan Sun, Leqi Shen, Hui Chen, Sicheng Zhao, Jungong Han, Guiguang Ding

AdaptFlow: Adaptive Workflow Optimization via Meta-Learning (2025.findings-emnlp)

Challenge:	Existing visual token compression methods rely on attention scores but have inherent biases . global and local attention biased scores cause excessive computational overhead .
Approach:	They propose a token pruning pipeline that targets global and local attention biases . the pipeline is designed to reduce computational overhead of Video Large Language Models based on visual tokens compiled from multiple video frames .
Outcome:	The proposed method significantly reduces the computational overhead of Video Large Language Models while retaining the performance of vanilla models.

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Runchuan Zhu, Bowen Jiang, Lingrui Mei, Fangkai Yang, Lu Wang, Haoxiang Gao, Fengshuo Bai, Pu Zhao, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang

LMUNIT: Fine-grained Evaluation with Natural Language Unit Tests (2025.findings-emnlp)

Challenge:	Existing approaches to large language models rely on static templates or manual workflows.
Approach:	AdaptFlow is a language-based meta-learning framework inspired by model-agnostic meta- learning.
Outcome:	AdaptFlow outperforms manual and automated workflows on question answering, code generation and mathematical reasoning benchmarks.

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Jon Saad-Falcon, Rajan Pathe Vivek, William Berrios, Nandita Shankar Naik, Matija Franklin, Bertie Vidgen, Amanpreet Singh, Douwe Kiela, Shikib Mehri

ThinkAnswer Loss: Balancing Semantic Similarity and Exact Matching for LLM Reasoning Enhancement (2025.findings-emnlp)

Challenge:	Using natural language unit tests, language models are costly and noisy, and automated metrics provide only coarse, difficult-to-interpret signals.
Approach:	They propose a paradigm that decomposes response quality into explicit, testable criteria and a unified scoring model, LMUnit, which combines multi-objective training across preferences, direct ratings, and natural language rationales.
Outcome:	The proposed paradigm significantly improves inter-annotator agreement and enables more effective LLM development workflows.

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Shan Yang, Kun Wu, Zeju Li, Linlin Zhang, Xiangyu Pei, Leike An, Yu Liu

Detecting Stealthy Backdoor Samples based on Intra-class Distance for Large Language Models (2025.findings-emnlp)

Challenge:	Existing methods for knowledge distillation use Chain-of-Thought (CoT) and answer pairs, but they lack appropriate supervision signals.
Approach:	They propose a framework that decouples CoT and answer supervision . the framework applies semantic similarity constraints while maintaining strict literal matching for the answer .
Outcome:	The proposed framework decouples CoT and answer supervision while maintaining strict literal matching for the answer.

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Jinwen Chen, Hainan Zhang, Fei Sun, Qinnan Zhang, Sijia Wen, Ziwei Wang, Zhiming Zheng

Rust-doctor: Enhanced Feature for Rust Ownership and Lifetime Repair with Balanced Training Data Generation (2025.findings-emnlp)

Challenge:	Existing detectors use classifier-style probability signals or rely on rewriting, which can degrade quality and introduce new triggers.
Approach:	They propose to efficiently remove poisoned examples before or during fine-tuning .
Outcome:	The proposed method outperforms prior detectors on two machine translation datasets and one QA dataset.

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Wenzhang Yang, Xiaoning Ren, Cuifeng Gao, Yinxing Xue

SLIM: Subtrajectory-Level Elimination for More Effective Reasoning (2025.findings-emnlp)

Challenge:	Rust has gained popularity due to its safety features during compilation.
Approach:	They propose to use a training dataset to generate a balanced training dataset and leverage the Mid-level Intermediate Representation (MIR) as enhanced features for Rust’s unique compilation error repair.
Outcome:	The proposed model outperforms state-of-the-art models in terms of Pass@K and Acc@K.

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Xifeng Yao, Chengyuan Ma, Dongyu Lang, Yinhao Ni, Zhiwei Xu, Huarui Xie, Zihao Chen, Guang Shen, Dandan Tu, Yi Bai, Changzheng Zhang

From Cross-Task Examples to In-Task Prompts: A Graph-Based Pseudo-Labeling Framework for In-context Learning (2025.findings-emnlp)

Challenge:	Notable examples include OpenAI’s o1/o3/o4 series and DeepSeek-R1 .
Approach:	They develop a framework to identify suboptimal subtrajectories based on human-established criteria . they also use a sampling algorithm to select data whose reasoning process is free from suboptimally subtravertories to the highest degree .
Outcome:	The proposed method reduces the number of suboptimal subtrajectories by 25.9% during the inference process.

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Zihan Chen, Song Wang, Xingbo Fu, Chengshuai Shi, Zhenyu Lei, Cong Shen, Jundong Li

Instance-level Randomization: Toward More Stable LLM Evaluations (2025.findings-emnlp)

Challenge:	In-context learning (ICL) enables large language models to perform novel tasks without parameter updates by conditioning on a few input-output examples.
Approach:	They propose a cost-efficient two-stage pipeline that reduces reliance on LLMs for data labeling.
Outcome:	The proposed pipeline reduces reliance on LLMs for data labeling . it leverages readily available cross-task examples to prompt an LLM and pseudo-label a small set of target task instances.

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Yiyang Li, Yonghuang Wu, Ying Luo, Liangtai Sun, Zishu Qin, Lin Qiu, Xuezhi Cao, Xunliang Cai

Not All Voices Are Rewarded Equally: Probing and Repairing Reward Models across Human Diversity (2025.findings-emnlp)

Challenge:	Evaluations of large language models suffer from instability, where small changes of random factors can lead to drastic fluctuations of scores and even model rankings.
Approach:	They propose an instance-level randomization method to reduce variance and improve fairness in evaluations by randomizing all factors that affect evaluation scores for every single instance.
Outcome:	The proposed method reduces variance and improves fairness in model comparisons by using instance-level randomization.

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Zihao Li, Feihao Fang, Xitong Zhang, Jiaru Zou, Zhining Liu, Wei Xiong, Ziwei Wu, Baoyu Jing, Jingrui He

PAMN: Multi-phase Correlation Modeling for Contrast-Enhanced 3D Medical Image Retrieval (2025.findings-emnlp)

Challenge:	Using real-world datasets, we conduct the most comprehensive study to date, auditing various state-of-the-art reward models across nine sensitive attributes, including age, gender, ethnicity, etc.
Approach:	They propose a method to mitigate group disparities in reward modeling by using real-world data.
Outcome:	The proposed method is based on a population-based dataset with nine demographic attributes, including gender, ethnicity, age, gender, and ethnicity.

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Haonan Tong, Ke Liu, Chuang Zhang, Xinglin Zhang, Tao Chen, Jenq-Neng Hwang, Lei Li

Safety in Large Reasoning Models: A Survey (2025.findings-emnlp)

Challenge:	Current 3D medical imaging models focus on spatial features, neglecting phase-specific progression detailed in clinical reports.
Approach:	They propose a framework that fuses imaging phases with clinical text to enhance 3D medical image retrieval.
Outcome:	The proposed framework outperforms state-of-the-art models on a phase-series dataset of 12,230 hospital CT scans.

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Cheng Wang, Yue Liu, Baolong Bi, Duzhen Zhang, Zhong-Zhi Li, Yingwei Ma, Yufei He, Shengju Yu, Xinfeng Li, Junfeng Fang, Jiaheng Zhang, Bryan Hooi

SafeConf: A Confidence-Calibrated Safety Self-Evaluation Method for Large Language Models (2025.findings-emnlp)

Challenge:	Large Reasoning Models (LRMs) have a high level of advanced reasoning capabilities, but they are vulnerable and vulnerable.
Approach:	This paper presents the first comprehensive survey of Large Reasoning Models . it explores the new safety risks, attacks, and defense strategies specific to LRMs based on reasoning .
Outcome:	The proposed study examines the safety and security risks of large reasoning models.

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Bo Zhang, Cong Gao, Linkang Yang, Bingxu Han, Minghao Hu, Zhunchen Luo, Guotong Geng, Xiaoying Bai, Jun Zhang, Wen Yao, Zhong Wang

DocAssistant: Integrating Key-region Reading and Step-wise Reasoning for Robust Document Visual Question Answering (2025.findings-emnlp)

Challenge:	Large language models (LLMs) have many advantages but they also pose significant safety risks.
Approach:	They propose a method to enhance the safety self-evaluation capability of LLMs . they perform semantic mutations on the original safety evaluation questions .
Outcome:	The proposed method improves safety self-evaluation accuracy by 5.86% and 7.79% over baseline methods on Chinese and English datasets.

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Jinxu Zhang, Qiyuan Fan, Yu Zhang

LNE-Blocking: An Efficient Framework for Contamination Mitigation Evaluation on Large Language Models (2025.findings-emnlp)

Challenge:	Existing document understanding models focus on key information and generate answers straightforwardly . existing models ignore evidence from source documents and lack interpretability .
Approach:	They propose a visual encoder that fuses text into visual encoded visual encodes . they use multimodal large language models as data generators and checkers to generate step-wise question-and-answer pairs for document images.
Outcome:	The proposed model can answer step-wise questions without compromising the performance of the original model.

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Ruijie Hou, Yueyang Jiao, Hanxu Hu, Yingming Li, Wai Lam, Huajian Zhang, Hongyuan Lu

Enhancing Hate Speech Classifiers through a Gradient-assisted Counterfactual Text Generation Strategy (2025.findings-emnlp)

Challenge:	a problem of data contamination is now almost inevitable during the development of large language models, with the training data often integrating evaluation benchmarks even unintentionally.
Approach:	They propose a framework to restore model performance prior to data contamination on potentially leaked datasets by using contamination detection and disruption operation.
Outcome:	The proposed framework restores model performance prior to contamination on potentially leaked datasets.

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Michael Van Supranes, Shaowen Peng, Shoko Wakamiya, Eiji Aramaki

Learning SQL Like a Human: Structure-Aware Curriculum Learning for Text-to-SQL Generation (2025.findings-emnlp)

Challenge:	Strong attribute control can distort meaning, while prioritizing semantic preservation may weaken attribute alignment.
Approach:	They propose a method that restricts accepted samples to text meeting a minimum BERTScore threshold and applies gradient-assisted proposal generation to improve attribute alignment.
Outcome:	a new method for counterfactual text generation improves attribute alignment and semantic preservation . the proposed method achieved the best macro F1-score in two of three test sets .

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Xiaohu Zhu, Qian Li, Lizhen Cui, Yuntao Du

Chain-of-Interactions: Multi-step Iterative ICL Framework for Abstractive Task-Oriented Dialogue Summarization of Conversational AI Interactions (2025.findings-emnlp)

Challenge:	Existing models struggle with complex queries, especially multi-table joins and reasoning.
Approach:	They propose to build a model with synthetic training samples and a structure-aware curriculum learning framework for enhancing SQL generation.
Outcome:	The proposed model improves on the existing model on the Spider and Bird benchmarks.

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Jason S Lucas, Ali Al Lawati, Mahjabin Nahar, John Chen, Mahnoosh Mehrabani

Your Semantic-Independent Watermark is Fragile: A Semantic Perturbation Attack against EaaS Watermark (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have introduced paradigm-shifting approaches in natural language processing, yet their transformative in-context learning (ICL) capabilities remain underutilized, especially in customer service dialogue summarization.
Approach:	They propose a single-instance, multi-step framework that orchestrates information extraction, self-correction, and evaluation through sequential interactive generation chains.
Outcome:	The proposed framework outperforms existing models and prompts in the customer service dialogue summarization domain.

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Zekun Fei, Biao Yi, Jianing Geng, He Ruiqi, Lihai Nie, Zheli Liu

Query Optimization for Parametric Knowledge Refinement in Retrieval-Augmented Large Language Models (2025.findings-emnlp)

Challenge:	Embedding-as-a-Service (EaaS) is a successful business pattern but faces significant challenges related to various forms of copyright infringement.
Approach:	They propose a semantic-independent watermarking scheme that exploits semantic perturbation tests to bypass verification.
Outcome:	The proposed watermarking schemes possess semantic-independent characteristics and exploit semantic perturbation tests to bypass verification.

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Youan Cong, Pritom Saha Akash, Cheng Wang, Kevin Chen-Chuan Chang

SKA-Bench: A Fine-Grained Benchmark for Evaluating Structured Knowledge Understanding of LLMs (2025.findings-emnlp)

Challenge:	Extract-Refine-Retrieve-Read is a query optimization framework for large language models . it is designed to bridge the pre-retrieval information gap in Retriev-Augmented Generation systems .
Approach:	They propose a framework to extract parametric knowledge from Large Language Models and refine them using a specialized query optimizer.
Outcome:	The extract-refine-retrieve-read framework outperforms baselines on QA datasets . it is designed to meet the knowledge requirements of large language models (LLMs)

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Zhiqiang Liu, Enpei Niu, Yin Hua, Mengshu Sun, Lei Liang, Huajun Chen, Wen Zhang

PD3F: A Pluggable and Dynamic DoS-Defense Framework against resource consumption attacks targeting Large Language Models (2025.findings-emnlp)

Challenge:	Existing evaluations for Structured Knowledge (SK) understanding are non-rigorous and focus on a single type of SK.
Approach:	They propose a structured knowledge understanding benchmark that includes four widely used structured knowledge forms.
Outcome:	The proposed benchmark is based on four widely used structured knowledge forms . it includes a question, an answer, positive knowledge units, and noisy knowledge units .

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Yuanhe Zhang, Xinyue Wang, Haoran Gao, Zhenhong Zhou, Fanyu Meng, Yuyao Zhang, Sen Su

From Implicit Exploration to Structured Reasoning: Guideline and Refinement for LLMs (2025.findings-emnlp)

Challenge:	Existing work lacks mitigation strategies against resource consumption attacks . existing work does not provide mitigation strategies for real-world LLM deployments .
Approach:	They propose a pluggable and dynamic doS-Defense framework which employs a two-stage approach to defend against resource consumption attacks from both the input and output sides.
Outcome:	The proposed framework significantly mitigates resource consumption attacks, improving users’ access capacity by up to 500% during adversarial load.

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Jiaxiang Chen, Zhuo Wang, Mingxi Zou, Zhucong Li, Zhijian Zhou, Song Wang, Zenglin Xu

PIP: Perturbation-based Iterative Pruning for Large Language Models (2025.findings-emnlp)

Challenge:	Existing models rely on implicit exploration, which leads to unstable reasoning paths and lack of error correction.
Approach:	They propose a framework that shifts from implicit exploration to structured reasoning through guideline and refinement.
Outcome:	The proposed model outperforms strong baselines on the Big-Bench Hard benchmark.

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Yi Cao, Wei-Jie Xu, Yucheng Shen, Weijie Shi, Chi-Min Chan, Jianfeng Qu, Jiajie Xu

Convolutional LoRA Aggregation for Unseen Tasks Adaptation (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) are growing in size and complexity, causing significant challenges for their practical deployment in resource-constrained environments.
Approach:	They propose a double-view structured pruning method that combines information from two different views to iteratively prune those that struggle to distinguish between them.
Outcome:	The proposed method reduces the parameter count by approximately 20% while retaining over 85% of the original model’s accuracy across varied benchmarks.

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Xinhao Wu, Jialin Liu, Yutai Duan, Jie Liu

Challenge:	Existing LoRA selection methods rely on a few task samples, making it difficult to capture full scope of task-relevant information.
Approach:	They propose a framework that selects appropriate LoRA modules and aggregates them using a convolutional LoRA aggregator.
Outcome:	The proposed framework bridges the knowledge gap between selected modules and target task . it ensures comprehensive coverage of task-relevant LoRA modules .

CDT: A Comprehensive Capability Framework for Large Language Models Across Cognition, Domain, and Task (2025.findings-emnlp)

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Haosi Mo, Xinyu Ma, Xuebo Liu, Derek F. Wong, Yu Li, Jie Liu, Min Zhang

Multilingual Collaborative Defense for Large Language Models (2025.findings-emnlp)

Challenge:	Existing benchmarks focus on isolated abilities, lacking a holistic framework for assessing LLM capabilities.
Approach:	They propose a Cognition-Domain-Task framework which measures a model’s capabilities across three dimensions.
Outcome:	The proposed framework improves performance on dataset evaluation and data selection, while achieving higher scores on general and specific benchmarks.

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Hongliang Li, Jinan Xu, Gengping Cui, Changhao Guan, Fengran Mo, Kaiyu Huang

Role-Guided Annotation and Prototype-Aligned Representation Learning for Historical Literature Sentiment Classification (2025.findings-emnlp)

Challenge:	Existing safeguards for Large Language Models are vulnerable to "jailbreaking" harmful queries.
Approach:	They propose a learning method that optimizes a continuous soft safety prompt automatically to facilitate multilingual safeguarding of LLMs.
Outcome:	The proposed method outperforms previous approaches in multilingual jailbreak defense while exhibiting strong cross-lingual generalization.

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Hongfei Du, Jiacheng Shi, Jacobo Myerston, Sidi Lu, Gang Zhou, Ashley Gao

MetaMixSpeech: Meta Task Augmentation for Low-Resource Speech Recognition (2025.findings-emnlp)

Challenge:	Prior work focused on using sentiment lexicons or leveraging large language models for annotation . lexiconics are often unavailable for historical texts due to limited linguistic resources .
Approach:	They propose a role-guided annotation strategy that prompts LLMs to simulate historical perspectives when labeling sentiment.
Outcome:	The proposed method outperforms state-of-the-art baselines across historical literature datasets.

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Yaqi Chen, Hao Zhang, Wenlin Zhang, XuKui Yang, Dan Qu, Yunpeng Liu

RECAST: Retrieval-Augmented Contextual ASR via Decoder-State Keyword Spotting (2025.findings-emnlp)

Challenge:	Meta-learning has proven to be a powerful paradigm for improving speech recognition performance . however, multilingual meta learning also faces challenges such as task overfitting and learner overfit .
Approach:	a new method is proposed to augment meta-training tasks with "more data" the method incorporates both support and query augmentations .
Outcome:	The proposed method achieves a 6.35% improvement in the word error rate on FLEURS and Common Voice datasets.

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Ashish Mittal, Sunita Sarawagi, Preethi Jyothi

PREE: Towards Harmless and Adaptive Fingerprint Editing in Large Language Models via Knowledge Prefix Enhancement (2025.findings-emnlp)

Challenge:	RECAST is a lightweight retrieval-augmented approach for contextual ASR . it repurposes decoder states of a pretrained ASR model to retrieve relevant keywords .
Approach:	RECAST is a retrieval-augmented approach that repurposes decoder states of a pretrained ASR model to retrieve relevant keywords without requiring audio exemplars.
Outcome:	RECAST outperforms full-list prompt biasing and strong phonetic/text baselines on 4,000 keywords across diverse domains.

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Xubin Yue, Zhenhua Xu, Wenpeng Xing, Jiahui Yu, Mohan Li, Meng Han

Beyond Spurious Signals: Debiasing Multimodal Large Language Models via Counterfactual Inference and Adaptive Expert Routing (2025.findings-emnlp)

Challenge:	Existing black-box fingerprinting techniques rely on overfitting high-perplexity trigger patterns . experimental results show that model editing in the fingerprint domain exhibits unique advantages .
Approach:	They propose a prefix-enhanced fingerprint editing framework that encodes copyright information into parameter offsets through dual-channel knowledge edit to achieve covert embedding of fingerprint features.
Outcome:	The proposed model editing framework achieves 90% trigger precision in mainstream architectures . the proposed model editor achieves the 90% accuracy in mainstream models .

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Zichen Wu, Hsiu-Yuan Huang, Yunfang Wu

Text-centric Alignment for Bridging Test-time Unseen Modality (2025.findings-emnlp)

Challenge:	Multimodal Large Language Models (MLLMs) often rely on spurious correlations, undermining their robustness and generalization.
Approach:	They propose a causal mediation-based debiasing framework to address correlation bias in MLLMs . they distinguish core semantics from spurious textual and visual contexts using counterfactual examples .
Outcome:	The proposed framework surpasses existing state-of-the-art models on sarcasm detection and sentiment analysis tasks.

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Yun-Da Tsai, Ting-Yu Yen, Pei-Fu Guo, Zhe-Yan Li, Shou-De Lin

Challenge:	a text-centric alignment method is used to handle unseen modalities and dynamic modality combinations at test time.
Approach:	They propose a text-centric alignment method that unifies different input modalities into a single semantic text representation by leveraging in-context learning with Large Language Models and uni-modal foundation models.
Outcome:	The proposed method unifies input modalities into a single semantic representation . it significantly improves the ability to manage unseen, diverse, and unpredictable modality combinations .

HierPrompt: Zero-Shot Hierarchical Text Classification with LLM-Enhanced Prototypes (2025.findings-emnlp)

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Qian Zhang, Qinliang Su, Wei Zhu, Pang Yachun

RouterEval: A Comprehensive Benchmark for Routing LLMs to Explore Model-level Scaling Up in LLMs (2025.findings-emnlp)

Challenge:	Existing methods for Hierarchical Text Classification are based on prototypes, but do not perform well due to ambiguity and impreciseness of category names.
Approach:	They propose a method that leverages hierarchy-aware prompts to instruct LLM to produce more representative and informative prototypes.
Outcome:	The proposed method outperforms existing methods on three benchmark datasets.

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Zhongzhan Huang, Guoming Ling, Yupei Lin, Yandong Chen, Shanshan Zhong, Hefeng Wu, Liang Lin

Can We Steer Reasoning Direction by Thinking Intervention? (2025.findings-emnlp)

Challenge:	a lack of comprehensive benchmarks for Routing large language models has hindered the development of routers.
Approach:	They propose a router-based benchmark to evaluate Routing large language models . the benchmark includes performance records for 12 popular LLM evaluations .
Outcome:	The proposed model-level scaling up phenomenon can surpass the best single model in the pool and many existing strong LLMs.

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Xingsheng Zhang, Luxi Xing, Chen Zhang, Yanbing Liu, Yifan Deng, Yunpeng Li, Yue Hu, Chenxu Niu

MPO: Boosting LLM Agents with Meta Plan Optimization (2025.findings-emnlp)

Challenge:	Large Reason Models suffer from overthinking and erroneous reasoning problems due to the lack of fine-grained control over their reasoning behaviors.
Approach:	They propose a paradigm to enable fine-grained control over LRMs’ reasoning behaviors by aligning reasoning trajectories with specific cognitive patterns.
Outcome:	The proposed paradigm achieves integration intervention throughout model reasoning processes.

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Weimin Xiong, Yifan Song, Qingxiu Dong, Bingchan Zhao, Feifan Song, null XWang, Sujian Li

Exploring the Generalizability of Factual Hallucination Mitigation via Enhancing Precise Knowledge Utilization (2025.findings-emnlp)

Challenge:	Existing methods for interactive planning tasks suffer from planning hallucinations and require retraining for each new agent.
Approach:	They propose a framework that leverages explicit guidance through meta plans to assist agent planning and enables continuous optimization based on feedback from the agent’s task execution.
Outcome:	The proposed framework outperforms existing baselines on two representative tasks and significantly improves task completion efficiency and generalization capabilities.

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Siyuan Zhang, Yichi Zhang, Yinpeng Dong, Hang Su

Learning What to Remember: Adaptive Probabilistic Memory Retention for Memory-Efficient Language Models (2025.findings-emnlp)

Challenge:	Existing methods to improve LLMs' ability to align their responses with objective facts suffer from poor generalization and trade-offs in other different capabilities.
Approach:	They propose to introduce PKUE (Precise Knowledge Utilization Enhancement) which fine-tunes the model on self-generated responses to precise and simple factual questions through preference optimization.
Outcome:	The proposed enhancements improve LLM’s ability to precisely leverage its knowledge and improve FactualBench, a comprehensive and precise factual QA dataset containing 181k Chinese data spanning 21 domains.

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S M Rafiuddin, Muntaha Nujat Khan

Unlocking Smarter Device Control: Foresighted Planning with a World Model-Driven Code Execution Approach (2025.findings-emnlp)

Challenge:	Adaptive Retention is a probabilistic, layer-wise token selection mechanism that learns which representations to keep under a strict global budget M.
Approach:	They propose a probabilistic token selection mechanism that learns which representations to keep under a strict global budget M.
Outcome:	The proposed method reduces memory usage by 35–45% while improving throughput by 1.8.

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Xiaoran Yin, Xu Luo, Hao Wu, Lianli Gao, Jingkuan Song

Challenge:	Current approaches to automating complex tasks focus on reactive policies and focus on visual observations.
Approach:	They propose a framework that prioritizes natural language understanding and structured reasoning to enhance the agent’s global understanding of the environment by developing a task-oriented, refinable world model at the outset of the task.
Outcome:	The proposed framework outperforms existing approaches in simulated environments and on real mobile devices.

RGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering (2025.findings-emnlp)

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Sichu Liang, Linhai Zhang, Hongyu Zhu, Wenwen Wang, Yulan He, Deyu Zhou

EcoSafeRAG: Efficient Security through Context Analysis in Retrieval-Augmented Generation (2025.findings-emnlp)

Challenge:	Existing retrieval approaches often overlook patient-specific factual knowledge embedded in EHRs . existing retrieval frameworks often overlook this factual information, limiting its effectiveness in clinical decision-making.
Approach:	They propose a recurrence generation-augmented retrieval framework that synergizes factual and conceptual knowledge from dual sources.
Outcome:	The proposed framework improves on factual-aware medical QA benchmarks.

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Ruobing Yao, Yifei Zhang, Shuang Song, Neng Gao, Chenyang Tu

StereoDetect: Detecting Stereotypes and Anti-stereotypes the Correct Way Using Social Psychological Underpinnings (2025.findings-emnlp)

Challenge:	Existing defense methods rely on internal knowledge of the model, which conflicts with the design concept of Retrieval-Augmented Generation (RAG).
Approach:	EcoSafeRAG uses sentence-level processing and bait-guided context diversity detection to identify malicious content .
Outcome:	EcoSafeRAG uses sentence-level processing and bait-guided context diversity detection to identify malicious content.

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Kaustubh Shivshankar Shejole, Pushpak Bhattacharyya

Sparkle: Mastering Basic Spatial Capabilities in Vision Language Models Elicits Generalization to Spatial Reasoning (2025.findings-emnlp)

Challenge:	Stereotypes are known to have harmful effects, making their detection critical . current research focuses on detecting and evaluating stereotypical biases .
Approach:	They propose a five-tuple definition and provide precise terminologies disentangling stereotypes, antistereotypes, stereotypical bias, and general bias.
Outcome:	The proposed framework disentangles stereotypes, antistereotypes, stereotypical bias, and general bias.

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Yihong Tang, Ao Qu, Zhaokai Wang, Dingyi Zhuang, Zhaofeng Wu, Wei Ma, Shenhao Wang, Yunhan Zheng, Zhan Zhao, Jinhua Zhao

How Does Knowledge Selection Help Retrieval Augmented Generation? (2025.findings-emnlp)

Challenge:	Currently, vision-language models excel in many downstream tasks but struggle with spatial reasoning, which is crucial for navigation and interaction with physical environments.
Approach:	They propose a framework that generates synthetic data to provide targeted supervision for VLMs across these basic spatial capabilities.
Outcome:	The proposed framework disentangles 2D spatial reasoning into three core components: direction comprehension, distance estimation, and localization.

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Xiangci Li, Jessica Ouyang

UPLex: Fine-Grained Personality Control in Large Language Models via Unsupervised Lexical Modulation (2025.findings-emnlp)

Challenge:	Retrieval-augmented generation (RAG) is a powerful method for enhancing natural language generation by integrating external knowledge into a model’s output.
Approach:	They empirically analyze how knowledge selection influences downstream generation performance in RAG systems by simulating different retrieval and selection conditions through a controlled mixture of gold and distractor knowledge.
Outcome:	The proposed model is based on a controlled mixture of gold and distractor knowledge and simulated with a gold and distractors.

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Tianlong Li, Wenhao Liu, Muling Wu, Shihan Dou, Zhenghua Wang, Changze Lv, Xiaohua Wang, Xiaoqing Zheng, Xuanjing Huang

ParetoRAG: Leveraging Sentence-Context Attention for Robust and Efficient Retrieval-Augmented Generation (2025.findings-emnlp)

Challenge:	Personality is a crucial factor that shapes human communication patterns, thereby regulating the personalities of large language models (LLMs).
Approach:	They propose a method that uses an Unsupervisedly-Built Personalized Lexicon (UPL) during the decoding phase to manipulate LLM’s personality traits.
Outcome:	The proposed method can modulate the personality expression of large language models by dynamically altering their predicted probability of upcoming words in a pluggable fashion.

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Ruobing Yao, Yifei Zhang, Shuang Song, Yuhan Liu, Neng Gao, Chenyang Tu

FlexQuant: A Flexible and Efficient Dynamic Precision Switching Framework for LLM Quantization (2025.findings-emnlp)

Challenge:	Retrieval-augmented generation systems face persistent challenges in retrieval inefficiency and the inability of LLMs to filter out irrelevant information.
Approach:	They propose an unsupervised framework that optimizes RAG systems through sentence-level refinement guided by the Pareto principle.
Outcome:	The proposed framework achieves dual improvements in retrieval precision and generation quality without additional training or API resources while using only 40% of the tokens compared to traditional approaches.

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Fangxin Liu, Zongwu Wang, Jinhong Xia, Junping Zhao, Shouren Zhao, Jinjin Li, Jian Liu, Li Jiang, Haibing Guan

ReLoop: “Seeing Twice and Thinking Backwards” via Closed-loop Training to Mitigate Hallucinations in Multimodal understanding (2025.findings-emnlp)

Challenge:	Existing methods for quantization of large language models struggle to adapt to dynamic workloads.
Approach:	a new framework optimizes the trade-off between inference speed and accuracy . FlexQuant enables fine-grained, layer-wise mixed-precision quantization .
Outcome:	a new framework optimizes the trade-off between inference speed and accuracy . it achieves a 1.3 speedup across diverse language tasks with negligible accuracy loss .

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Jianjiang Yang, Yanshu Li, Ziyan Huang

Sequence Structure Aware Retriever for Procedural Document Retrieval: A New Dataset and Baseline (2025.findings-emnlp)

Challenge:	Existing methods for hallucination mitigation rely on external verification or post-hoc correction, lacking internal mechanism to validate outputs directly during training.
Approach:	They propose a unified closed-loop training framework that encourages multimodal consistency for cross-modal understanding in MLLMs.
Outcome:	The proposed framework encourages multimodal consistency for cross-modal understanding in MLLMs.

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Zhenqi Ye, HaoPeng Ren, Yi Cai, Qingbao Huang, Jing Qin, Pinli Zhu, Songwen Gong

The Effect of Language Diversity When Fine-Tuning Large Language Models for Translation (2025.findings-emnlp)

Challenge:	Existing retrieval methods neglect the execution sequence structures inherent in procedural documents.
Approach:	They propose a retrieval model which integrates procedural graphs with document representations.
Outcome:	The proposed model integrates procedural graphs with document representations to improve document retrieval.

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David Stap, Christof Monz

David vs. Goliath: Cost-Efficient Financial QA via Cascaded Multi-Agent Reasoning (2025.findings-emnlp)

Challenge:	Prior research on language diversity in LLM fine-tuning has reported benefits while others find no benefits.
Approach:	They find that expanding language diversity during fine-tuning improves translation quality . they also show that increased language diversity creates more language-agnostic representations .
Outcome:	The proposed model improves translation quality for unsupervised and supervised pairs . the results plateau or decrease beyond a certain diversity threshold.

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Chenghao Liu, Qian Liu, Ziqin Zhu, Hao Fei, Aniket Mahanti

Benchmarking Uncertainty Metrics for LLM Target-Aware Search (2025.findings-emnlp)

Challenge:	Large language models have demonstrated remarkable reasoning capabilities, but performance in FQA remains limited.
Approach:	They propose a low-cost yet effective framework that enables small LLMs to perform complex reasoning tasks without expensive models.
Outcome:	The proposed framework outperforms the best open-source model on BizBench by 10.46% and achieves competitive performance to GPT-3.5 using significantly fewer parameters.

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Pei-Fu Guo, Yun-Da Tsai, Shou-De Lin

ZOGRASCOPE: A New Benchmark for Semantic Parsing over Property Graphs (2025.findings-emnlp)

Challenge:	Existing uncertainty metrics for LLM search methods do not capture the diverse types of uncertainty needed to guide different optimization goals.
Approach:	They propose a framework for uncertainty benchmarking that captures four different uncertainty types . the uncertainty types Answer, Correctness, Aleatoric, and Epistemic serve different optimization goals .
Outcome:	The proposed framework identifies four different uncertainty types . the uncertainty types serve different optimization goals in LLM search .

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Francesco Cazzaro, Justin Kleindienst, Sofia Márquez Gomez, Ariadna Quattoni

FG-PRM: Fine-grained Hallucination Detection and Mitigation in Language Model Mathematical Reasoning (2025.findings-emnlp)

Challenge:	PGs are increasingly used in knowledge graphs, but they are underrepresented in research . a benchmark is designed specifically for PG and queries written in Cypher.
Approach:	They propose a benchmark specifically for PGs and queries written in Cypher.
Outcome:	The proposed benchmark is designed specifically for PGs and queries written in Cypher.

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Ruosen Li, Ziming Luo, Xinya Du

Recipe2Plan: Evaluating Planning Abilities of LLMs for Efficient and Feasible Multitasking with Time Constraints Between Actions (2025.findings-emnlp)

Challenge:	Existing methods to detect hallucinations in large language models lack nuanced understanding of their types and manifestations.
Approach:	They propose a taxonomy that categorizes hallucinations into six types . they propose an augmented model to detect and mitigate hallucinosity in a fine-grained manner .
Outcome:	The proposed model detects and mitigates hallucinations in a fine-grained manner . it significantly boosts the performance of LLMs on GSM8K and MATH benchmarks.

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Zirui Wu, Xiao Liu, Jiayi Li, Lingpeng Kong, Yansong Feng

Unlocking the Effectiveness of LoRA-FP for Seamless Transfer Implantation of Fingerprints in Downstream Models (2025.findings-emnlp)

Challenge:	Existing evaluation benchmarks focus on single task performance, ignoring multitask planning and execution efficiency.
Approach:	They propose a benchmark framework based on real-world cooking scenarios . recipe2plan challenges agents to optimize cooking time through parallel task execution .
Outcome:	The proposed benchmarks highlight the need for improved temporal awareness and global multitasking capabilities in large language models.

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Zhenhua Xu, Zhaokun Yan, Binhan Xu, Xin Tong, Haitao Xu, Yourong Chen, Meng Han

AELC: Adaptive Entity Linking with LLM-Driven Contextualization (2025.findings-emnlp)

Challenge:	lightweight plug-and-play framework that encodes backdoor fingerprints into LoRA adapters .
Approach:	proposed framework encodes backdoor fingerprints into LoRA adapters via constrained fine-tuning . enables seamless fingerprint transplantation through parameter fusion, eliminating full-parameter updates while maintaining integrity.
Outcome:	The proposed framework achieves superior robustness against various scenarios while reducing computational overhead compared to traditional approaches.

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Fang Wang, Zhengwei Tao, Ming Wang, Minghao Hu, Xiaoying Bai

MetaLadder: Ascending Mathematical Solution Quality via Analogical-Problem Reasoning Transfer (2025.findings-emnlp)

Challenge:	Entity linking (EL) focuses on associating ambiguous mentions in text with corresponding entities in a knowledge graph.
Approach:	Entity linking (EL) focuses on associating ambiguous mentions in text with corresponding entities in a knowledge graph.
Outcome:	Experiments on four public benchmark datasets show that AELC achieves state-of-the-art performance.

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Honglin Lin, Zhuoshi Pan, Qizhi Pei, Xin Gao, Yu Li, Mengzhang Cai, Conghui He, Lijun Wu

GLProtein: Global-and-Local Structure Aware Protein Representation Learning (2025.findings-emnlp)

Challenge:	Current paradigms generate CoT and answers directly for a given problem, diverging from human problem-solving strategies to some extent.
Approach:	They propose a framework that explicitly prompts LLMs to recall and reflect on meta-problems alongside their CoT solutions before addressing the target problem.
Outcome:	The proposed framework outperforms standard CoT-based methods on mathematical benchmarks and significantly improves their reasoning accuracy.

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Yunqing Liu, Wenqi Fan, Xiaoyong Wei, Li Qing

Reward Mixology: Crafting Hybrid Signals for Reinforcement Learning Driven In-Context Learning (2025.findings-emnlp)

Challenge:	Despite advances in protein sequence analysis, there remains potential for further exploration in integrating protein structural information.
Approach:	They propose a framework that integrates global structural similarity and local amino acid details to enhance protein pre-training.
Outcome:	The proposed framework outperforms existing methods in several bioinformatics tasks.

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Changshuo Zhang, Ang Gao, Xiao Zhang, Yong Liu, Deyang Li, Fangchao Liu, Xinyu Zhang

Can Multimodal LLMs See Materials Clearly? A Multimodal Benchmark on Materials Characterization (2025.findings-emnlp)

Challenge:	Existing methods for in-context learning (ICL) performance rely on quality and ordering of demonstrations.
Approach:	They propose a method that models iterative demonstration selection as a Markov Decision Process and craft hybrid reward signals.
Outcome:	The proposed method combines outcome-based accuracy signals with process-oriented signals like stepwise influence and label entropy improvement.

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Zhengzhao Lai, Youbin Zheng, Zhenyang Cai, Haonan Lyu, Jingpu Yang, Hong-Qing Liang, Yan Hu, Benyou Wang

GRADE: Generating multi-hop QA and fine-gRAined Difficulty matrix for RAG Evaluation (2025.findings-emnlp)

Challenge:	characterization imaging data is fundamental to acquiring materials information.
Approach:	a team of researchers develop a benchmark for materials characterization imaging data . the goal is to bridge this gap by addressing 1,500 questions that require expert-level expertise.
Outcome:	a new benchmark for materials characterization imaging data is presented . the benchmark reveals that MLLMs perform poorly when addressing higher-level questions .

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Jeongsoo Lee, Daeyong Kwon, Kyohoon Jin

FusionDTI: Fine-grained Binding Discovery with Token-level Fusion for Drug-Target Interaction (2025.findings-emnlp)

Challenge:	Current evaluations of RAG systems overlook structural complexity and multi-step reasoning . GRADE model enables fine-grained analysis of Ragging performance .
Approach:	They propose a framework that models retrieval difficulty along two orthogonal dimensions . they extract knowledge graphs and augment them through semantic clustering to recover missing links .
Outcome:	The proposed framework models retrieval difficulty along two orthogonal dimensions . error rates correlate with the framework, and it validates its diagnostic utility.

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Zhaohan Meng, Zaiqiao Meng, Ke Yuan, Iadh Ounis

A Survey on Training-free Alignment of Large Language Models (2025.findings-emnlp)

Challenge:	despite advances in DTI models, models often struggle to capture fine-grained interactions between drugs and proteins.
Approach:	They propose a novel drug-target interaction model that uses a token-level module to learn fine-grained information for drug-target interactions.
Outcome:	The proposed model learns fine-grained information for drug-target interaction . it mitigates sequence fragment invalidation and incorporates the structure-aware vocabulary of target proteins .

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Birong Pan, Yongqi Li, Weiyu Zhang, Wenpeng Lu, Mayi Xu, Shen Zhou, Yuanyuan Zhu, Ming Zhong, Tieyun Qian

CIVET: Systematic Evaluation of Understanding in VLMs (2025.findings-emnlp)

Challenge:	a survey of large language models (LLMs) aims to ensure outputs adhere to human values, ethical standards, and legal norms.
Approach:	They present the first systematic review of TF alignment methods . they categorize them by stages of pre-decoding, in-decoder and post-decoration .
Outcome:	The proposed methods are based on training-free (TF) alignment techniques . they are able to be used in open-source and closed-source environments without retraining .

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Massimo Rizzoli, Simone Alghisi, Olha Khomyn, Gabriel Roccabruna, Seyed Mahed Mousavi, Giuseppe Riccardi

Challenge:	Current Vision-Language Models can accurately recognize only a limited set of basic object properties; 3) they struggle to understand basic relations among objects.
Approach:	They propose a framework that evaluates VLMs on exhaustive sets of stimuli, free from annotation noise, dataset-specific biases, and uncontrolled scene complexity.
Outcome:	The proposed framework addresses the lack of standardized systematic evaluation for assessing VLMs’ understanding, enabling researchers to test hypotheses with statistical rigor.

How Does Cognitive Bias Affect Large Language Models? A Case Study on the Anchoring Effect in Price Negotiation Simulations (2025.findings-emnlp)

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Yoshiki Takenami, Yin Jou Huang, Yugo Murawaki, Chenhui Chu

Enhancing Speech-to-Speech Dialogue Modeling with End-to-End Retrieval-Augmented Generation (2025.findings-emnlp)

Challenge:	Cognitive biases can be observed in LLMs, affecting their reliability in real-world applications.
Approach:	They investigate the anchoring effect in LLM-driven price negotiations . reasoning models are less prone to the anchor effect, they find .
Outcome:	The proposed study shows that LLMs are influenced by the anchoring effect like humans . reasoning models are less prone to the anchor effect, but personality traits are not affected .

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Pengchao Feng, Ziyang Ma, Wenxi Chen, Yao Li, Sheng Wang, Kai Yu, Xie Chen

Backdoor-Powered Prompt Injection Attacks Nullify Defense Methods (2025.findings-emnlp)

Challenge:	End-to-end speech-to speech (S2S) dialogue systems face key challenges in incorporating external knowledge into their models.
Approach:	They propose a framework that directly retrieves relevant textual knowledge from speech queries.
Outcome:	The proposed framework improves the performance of end-to-end speech-tospeech dialogue systems while achieving higher retrieval efficiency.

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Yulin Chen, Haoran Li, Yuan Sui, Yangqiu Song, Bryan Hooi

Path-enhanced Pre-trained Language Model for Knowledge Graph Completion (2025.findings-emnlp)

Challenge:	Recent studies have shown that LLMs are vulnerable to prompt injection attacks because of their instruction-following abilities and inability to distinguish the instructions in the data content.
Approach:	They propose backdoor-powered prompt injection attacks that trick LLMs into deviating from the original input instruction and executing the attackers’ target instruction.
Outcome:	The proposed attacks trick the LLMs into deviating from the input instruction and executing the attackers’ target instruction.

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Hao Wang, Dandan Song, Zhijing Wu, Yuhang Tian, Pan Yang

Zero-shot Cross-lingual NER via Mitigating Language Difference: An Entity-aligned Translation Perspective (2025.findings-emnlp)

Challenge:	Pre-trained language models have achieved remarkable knowledge graph completion (KGC) success.
Approach:	They propose a path-enhanced pre-trained language model-based knowledge graph completion method which uses multi-view generation to infer missing facts in triple-level and path-level simultaneously.
Outcome:	The proposed method significantly improves the performance of the knowledge graph completion task.

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Zhihao Zhang, Sophia Yat Mei Lee, Dong Zhang, Shoushan Li, Guodong Zhou

Zero-Shot Cross-Domain Aspect-Based Sentiment Analysis via Domain-Contextualized Chain-of-Thought Reasoning (2025.findings-emnlp)

Challenge:	Existing approaches to cross-lingual Named Entity Recognition focus on Latin script language (LSL) for non-Latin script language, performance often degrades due to deep structural differences.
Approach:	They propose an entity-aligned translation approach to align entities between NSL and English .
Outcome:	The proposed approach aims to transfer knowledge from high-resource languages to low-resourced languages.

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Chuming Shen, Wei Wei, Dong Wang, Zhong-Hao Wang

Tree of Agents: Improving Long-Context Capabilities of Large Language Models through Multi-Perspective Reasoning (2025.findings-emnlp)

Challenge:	Cross-domain aspect-based sentiment analysis (ABSA) aims to learn specific knowledge from a source domain to perform various tasks on a target domain.
Approach:	a new framework is proposed to learn specific knowledge from a source domain . the framework uses domain adaptation techniques to transfer domain-agnostic features .
Outcome:	a new learning framework for cross-domain aspect-based sentiment analysis is proposed . it effectively eliminates dependency on target-domain annotations, authors say .

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Song Yu, Xiaofei Xu, Ke Deng, Li Li, Lin Tian

Cross-Cultural Transfer of Commonsense Reasoning in LLMs: Evidence from the Arab World (2025.findings-emnlp)

Challenge:	Large language models face persistent challenges when handling long-context tasks . existing methods that reduce input have the risk of discarding key information .
Approach:	To address this issue, we propose a multi-agent reasoning framework called Tree of Agents . the framework segments input into chunks processed by independent agents .
Outcome:	The proposed model outperforms baseline models on long-context tasks.

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Saeed Almheiri, Rania Elbadry, Mena Attia, Chenxi Wang, Preslav Nakov, Timothy Baldwin, Fajri Koto

Enhancing Partially Relevant Video Retrieval with Robust Alignment Learning (2025.findings-emnlp)

Challenge:	a recent study examined the potential for cross-cultural transfer of commonsense reasoning . merely 12 culture-specific examples from one country can improve performance in others by 10% on average .
Approach:	They evaluate cross-cultural transfer of commonsense reasoning within the arab world . they use in-context learning and demonstration-based reinforcement to evaluate alignment methods .
Outcome:	The proposed model can improve performance in cultures with cultural similarities in the Arab world by 10% on average.

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Long Zhang, Peipei Song, Jianfeng Dong, Kun Li, Xun Yang

Multi-level Diagnosis and Evaluation for Robust Tabular Feature Engineering with Large Language Models (2025.findings-emnlp)

Challenge:	Existing methods focus on enhancing multi-scale clip representations but lack robust data alignment . inherent data uncertainty renders PRVR vulnerable to distractor videos with spurious similarities .
Approach:	proposed framework for partially relevant video retrieval aims to retrieve untrimmed videos partially relevant to a given query.
Outcome:	The proposed framework can be seamlessly integrated into existing architectures.

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Yebin Lim, Susik Yoon

Prejudge-Before-Think: Enhancing Large Language Models at Test-Time by Process Prejudge Reasoning (2025.findings-emnlp)

Challenge:	Recent advances in large language models have shown promise in feature engineering for tabular data, but reliability concerns persist due to variability in generated outputs.
Approach:	They propose a multi-level diagnosis and evaluation framework to assess the robustness of large language models in feature engineering across diverse domains.
Outcome:	The proposed framework assesses the robustness of large language models across domains.

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Jianing Wang, Jin Jiang, Yang Liu, Mengdi Zhang, Xunliang Cai

FroM: Frobenius Norm-Based Data-Free Adaptive Model Merging (2025.findings-emnlp)

Challenge:	Experimental results from competition-level complex reasoning demonstrate that bootstrapping with process prejudge can significantly enhance the reasoning ability of LLMs.
Approach:	They propose a new process prejudge strategy for LLM reasoning that bootstraps with process prejudgment .
Outcome:	The proposed method can be bootstrapped with process prejudge in LLM reasoning . it allows the model to anticipate errors rather than relying on trial and error.

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Zijian Li, Xiaocheng Feng, Huixin Liu, Yichong Huang, Ting Liu, Bing Qin

Dynamic Simulation Framework for Disinformation Dissemination and Correction With Social Bots (2025.findings-emnlp)

Challenge:	a new adaptive merging method is proposed to improve fine-tuning performance . traditional methods often encounter task interference when merging full fine-uning models .
Approach:	They propose an adaptive merging method that directly measures model parameters using the Frobenius norm .
Outcome:	The proposed method outperforms baseline methods in various fine-tuning scenarios.

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Boyu Qiao, Kun Li, Wei Zhou, Songlin Hu

Beyond the First Error: Process Reward Models for Reflective Mathematical Reasoning (2025.findings-emnlp)

Challenge:	Current studies rely on simplistic user and network modeling and neglect dynamic behavior of bots.
Approach:	They propose a multi-agent-based framework for disinformation dissemination . it incorporates both malicious and legitimate bots and allows quantitative evaluation of correction strategies.
Outcome:	The proposed framework incorporates both malicious and legitimate bots and their controlled dynamic participation allows for quantitative analysis of correction strategies.

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Zhaohui Yang, Chenghua He, Xiaowen Shi, Shihong Deng, Linjing Li, Qiyue Yin, Daxin Jiang

PrAd: Prompt Adaptive Tuning for Decoder-only Language Models (2025.findings-emnlp)

Challenge:	Existing methods for training effective PRMs focus on the first incorrect step and all preceding steps, assuming that all subsequent steps are incorrect.
Approach:	They propose a data annotation method specifically designed to score the long CoT reasoning process by using an LLM-based judger for annotation.
Outcome:	The proposed method improves PRMs' ability to identify effective self-correction behaviors and reasoning based on erroneous steps.

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Youneng Ma, Junyi He, Haojun Fei

Personalized Question Answering with User Profile Generation and Compression (2025.findings-emnlp)

Challenge:	Prompt-based methods suffer from increased input lengths and sensitivity to weight initialization . adapter-based approaches can substantially increase inference time .
Approach:	a new paradigm for prompt-based tuning addresses the problem of fine tuning pretrained models . prompt--based methods suffer from increased input lengths and sensitivity to weight initialization . a prompt-oriented approach employs adapters for flexible input transformation .
Outcome:	a proposed framework can achieve comparable or better performance and higher inference efficiency even in multi-task scenarios.

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Hang Su, Yun Yang, Tianyang Liu, Xin Liu, Peng Pu, Xuesong Lu

Dream to Chat: Model-based Reinforcement Learning on Dialogues with User Belief Modeling (2025.findings-emnlp)

Challenge:	Large language models are prone to providing “midguy” answers regardless of users’ knowledge background, thereby failing to meet each user’s personalized needs.
Approach:	They propose to generate personalized answers with LLMs based on users’ past question-answering records.
Outcome:	The proposed method generates personalized answers based on user's past question-answering records.

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Yue Zhao, Xiaoyu Wang, Dan Wang, Zhonglin Jiang, Qingqing Gu, Teng Chen, Ningyuan Xi, Jinxian Qu, Yong Chen, Luo Ji

FakeSV-VLM: Taming VLM for Detecting Fake Short-Video News via Progressive Mixture-Of-Experts Adapter (2025.findings-emnlp)

Challenge:	a framework for constructing dialogue world models for natural language tasks is currently lacking.
Approach:	They propose a framework that can be used to train a dialogue world model.
Outcome:	The proposed framework can predict future utterances and user beliefs . it can achieve state-of-the-art performance on emotion classification and sentiment identification .

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JunXi Wang, Yaxiong Wang, Lechao Cheng, Zhun Zhong

Beyond Inherent Cognition Biases in LLM-Based Event Forecasting: A Multi-Cognition Agentic Framework (2025.findings-emnlp)

Challenge:	Existing methods for detecting fake news videos fall short due to lack of knowledge to verify the news is real or not.
Approach:	They propose a VLM-based framework for detecting fake news on short video platforms . they design four experts tailored to handle each scenario and integrate them into VLM .
Outcome:	The proposed framework outperforms current state-of-the-art models on two benchmark datasets.

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Zhen Wang, Xi Zhou, Yating Yang, Bo Ma, Lei Wang, Rui Dong, Azmat Anwar

Challenge:	Large Language Models exhibit human-like cognitive biases in event forecasting . a human-curated dataset reveals significant cognitive bias in LLMs .
Approach:	They propose a human-curated dataset to explore LLMs' cognitive biases . they leverage LLM participants to act as multi-cognition event participants .
Outcome:	The proposed framework alleviates cognitive biases in LLMs and offers diverse perspectives.

Breaking the Reviewer: Assessing the Vulnerability of Large Language Models in Automated Peer Review Under Textual Adversarial Attacks (2025.findings-emnlp)

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Tzu-Ling Lin, Wei-Chih Chen, Teng-Fang Hsiao, Hou-I Liu, Ya-Hsin Yeh, Yu-Kai Chan, Wen-Sheng Lien, Po-Yen Kuo, Philip S. Yu, Hong-Han Shuai

Watermarking with Low-Entropy POS-Guided Token Partitioning and Z-Score-Driven Dynamic Bias for Large Language Models (2025.findings-emnlp)

Challenge:	Large language models (LLMs) are used to review academic papers, but are susceptible to textual adversarial attacks.
Approach:	They evaluate the robustness of large language models as automated reviewers in the presence of adversarial attacks.
Outcome:	The proposed model is robust against textual adversarial attacks, the authors argue . their findings highlight the importance of addressing adversarials to ensure integrity of scholarly communication.

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He Li, Xiaojun Chen, Zhendong Zhao, Yunfei Yang, Xin Zhao, Jingcheng He

Knowledge Graph-Driven Memory Editing with Directional Interventions (2025.findings-emnlp)

Challenge:	Existing watermarking methods reduce the fidelity of semantics in LLMs .
Approach:	They propose a low-entropy token partitioning mechanism and z-score-driven dynamic bias mechanism to enhance semantics.
Outcome:	The proposed framework improves semantic fidelity and robustness against bias sparsity attacks.

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Jinhu Fu, Kun Wang, Chongye Guo, Junfeng Fang, Wentao Zhang, Sen Su

DTDES-KGE: Dual-Teacher Knowledge Distillation with Distinct Embedding Spaces for Knowledge Graph Embeddings (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) are hampered by inaccuracies and outdated information.
Approach:	They propose a framework that constructs knowledge graphs using available information to guide the direction of knowledge editing.
Outcome:	The proposed framework allows consistent, aligned, and stable information during large-scale editing scenarios.

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Bofan Wei, Hongyuan Xu, Yuhang Niu, Jiarui Ren, Yanlong Wen, Xiaojie Yuan

LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation (2025.findings-emnlp)

Challenge:	Existing knowledge distillation methods rely on a single teacher embedding space . existing methods overlook valuable complementary knowledge from teachers in distinct embeddable spaces.
Approach:	They propose a knowledge distillation framework that leverages dual teachers in embedding spaces to enhance performance.
Outcome:	The proposed framework significantly improves knowledge distillation performance by leveraging dual teachers in distinct embedding spaces.

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Ming Zhang, Yujiong Shen, Zelin Li, Huayu Sha, Binze Hu, Yuhui Wang, Chenhao Huang, Shichun Liu, Jingqi Tong, Changhao Jiang, Mingxu Chai, Zhiheng Xi, Shihan Dou, Tao Gui, Qi Zhang, Xuanjing Huang

Watermark Smoothing Attacks against Language Models (2025.findings-emnlp)

Challenge:	Current medical benchmarks have limitations in question design, data sources and evaluation methods.
Approach:	They propose a new benchmark covering five core medical areas . it includes 2,996 questions created from real-world electronic health records .
Outcome:	The proposed model covers five core medical areas and includes 2,996 questions created from real-world electronic health records and expert-designed clinical scenarios.

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Hongyan Chang, Hamed Hassani, Reza Shokri

PICD-Instruct: A Generative Instruction Learning Framework for Few-Shot Multi-Intent Spoken Language Understanding (2025.findings-emnlp)

Challenge:	Watermarking is a key technique for detecting AI-generated text.
Approach:	They propose a method to selectively smooth watermarks by leveraging the relationship between the model’s confidence and detectability.
Outcome:	The proposed method selectively smoothes watermark traces while preserving text quality.

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Wenbin Hua, Rui Fan, Tingting He, Ming Dong

Forewarned is Forearmed: Pre-Synthesizing Jailbreak-like Instructions to Enhance LLM Safety Guardrail to Potential Attacks (2025.findings-emnlp)

Challenge:	Recent advances in large language models (LLMs) have utilized instruction learning frameworks to model intent-slot interdependencies, typically requiring abundant data for effective training.
Approach:	They propose a generative framework based on Basic Instructions (BI), Pairwise Interaction Instructions and Contrastive Distinct Instructions to solve these challenges.
Outcome:	The proposed framework achieves state-of-the-art performance on public datasets.

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Sheng Liu, Qiang Sheng, Danding Wang, Yang Li, Guang Yang, Juan Cao

Are Knowledge and Reference in Multilingual Language Models Cross-Lingually Consistent? (2025.findings-emnlp)

Challenge:	Despite advances in improving large language model (LLM) to refuse to answer malicious instructions, LLMs remain vulnerable to jailbreak attacks where attackers generate instructions with distributions differing from safety alignment corpora.
Approach:	They propose a framework that leverages embedding space distribution analysis to generate jailbreak-like instructions.
Outcome:	The proposed framework shows significant decreases in attack success rate on Qwen2.5, Llama3.1, and Llma3.2 without compromising their utility.

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Xi Ai, Mahardika Krisna Ihsani, Min-Yen Kan

Krikri: Advancing Open Large Language Models for Greek (2025.findings-emnlp)

Challenge:	Cross-lingual consistency should be considered to assess cross-lingual transferability, maintain factuality of model knowledge across languages, and preserve parity of language model performance.
Approach:	They examine pretrained and tuned models with code-mixed coreferential statements that convey identical knowledge across languages.
Outcome:	The proposed model shows different levels of consistency in multilingual models, subject to language families, linguistic factors, scripts, and bottlenecks on a particular layer.

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Dimitris Roussis, Leon Voukoutis, Georgios Paraskevopoulos, Sokratis Sofianopoulos, Prokopis Prokopidis, Vassilis Papavassileiou, Athanasios Katsamanis, Stelios Piperidis, Vassilis Katsouros

Beyond the Scientific Document: A Citation-Aware Multi-Granular Summarization Approach with Heterogeneous Graphs (2025.findings-emnlp)

Challenge:	Llama-Krikri-8B is a cutting-edge Large Language Model for the Greek language based on Meta's Llma 3.1-8B.
Approach:	They propose to use Llama-Krikri-8B to train Greek language models . it has 8 billion parameters and is capable of handling polytonic text and Ancient Greek .
Outcome:	The proposed model is based on Meta's Llama 3.1-8B and has 8 billion parameters and is capable of handling polytonic text and Ancient Greek.

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Quoc-An Nguyen, Xuan-Hung Le, Thi-Minh-Thu Vu, Hoang-Quynh Le

Detecting Continuously Evolving Scam Calls under Limited Annotation: A LLM-Augmented Expert Rule Framework (2025.findings-emnlp)

Challenge:	Experimental results demonstrate that our model outperforms existing approaches for summarizing documents.
Approach:	proposed model constructs a heterogeneous graph to represent a document and its relevant external citations.
Outcome:	The proposed model outperforms existing models in three different scenarios.

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Haoyu Ma, Qinliang Su, Minhua Huang, Wu Kai

An Empirical Study of Position Bias in Modern Information Retrieval (2025.findings-emnlp)

Challenge:	Existing methods to detect scam calls rely on labeled data and assume static distribution of scam narratives.
Approach:	They propose a method leveraging large language models to detect continuously evolving scam calls . scammers continuously evolve their tactics, making these methods less effective .
Outcome:	The proposed approach is based on large language models to detect continuously evolving scam calls.

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Ziyang Zeng, Dun Zhang, Jiacheng Li, null Zoupanxiang, Yudong Zhou, Yuqing Yang

GenPoE: Generative Passage-level Mixture of Experts for Knowledge Enhancement of LLMs (2025.findings-emnlp)

Challenge:	a new evaluation framework is used to assess the extent and impact of position bias in information retrieval.
Approach:	They introduce a position-aware retrieval benchmark and a diagnostic metric to quantify position bias . they compare models with BM25, dense embedding models, ColBERT-style late-interaction models .
Outcome:	The proposed framework evaluates retrieval models for position bias from a worst-case perspective.

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Xuebing Liu, Shanbao Qiao, Seung-Hoon Na

CoRanking: Collaborative Ranking with Small and Large Ranking Agents (2025.findings-emnlp)

Challenge:	GenPoE is a passage-level mixture of experts for enhancing knowledge of large language models.
Approach:	They propose a novel “generative” passage-level mixture of experts (MoEs) that takes in-context retrieved passages and generates their “expert” parameters.
Outcome:	The proposed system is based on a novel hypernetwork which takes in-context retrieved passages and generates their "expert'' parameters.

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Wenhan Liu, Xinyu Ma, Yutao Zhu, Lixin Su, Shuaiqiang Wang, Dawei Yin, Zhicheng Dou

HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation (2025.findings-emnlp)

Challenge:	Listwise ranking based on Large Language Models (LLMs) has achieved state-of-the-art performance in Information Retrieval (IR) however, their effectiveness often depends on LLMs with massive parameter scales and computationally expensive sliding window processing, leading to substantial efficiency bottlenecks.
Approach:	They propose a Collaborative Ranking framework (CoRanking) for LLM-based listwise ranking based on large language models with massive parameter scales and computationally expensive sliding window processing.
Outcome:	The proposed framework reduces ranking latency by approximately 70% while improving effectiveness compared to the standalone large reranker.

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Yihan Jiao, Zhehao Tan, Dan Yang, Duolin Sun, Jie Feng, Yue Shen, Jian Wang, Peng Wei

Towards Personalized Conversational Sales Agents: Contextual User Profiling for Strategic Action (2025.findings-emnlp)

Challenge:	In-depth research on the specific capabilities needed by the RAG generation model is lacking, leading to inconsistent document quality and retrieval system imperfections.
Approach:	They propose that RAG models should possess three progressively hierarchical abilities: (1) Filtering: the ability to select relevant information; (2) Combination: the capability to combine semantic information across paragraphs; (3) RAG-specific reasoning: the capacity to further process external knowledge using internal knowledge.
Outcome:	Experiments show that the proposed method significantly improves the model’s open-book examination capability on datasets such as RGB, PopQA, MuSiQue, HotpotQA, and PubmedQA.

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Tongyoung Kim, Jeongeun Lee, SooJin Yoon, SungHwan Kim, Dongha Lee

WebCoT: Enhancing Web Agent Reasoning by Reconstructing Chain-of-Thought in Reflection, Branching, and Rollback (2025.findings-emnlp)

Challenge:	Conversational Recommender Systems (CRSs) aim to engage users in dialogue to provide tailored recommendations.
Approach:	They propose a task that integrates preference elicitation, recommendation and persuasion . they propose CSALES, a conversational sales agent that infers contextual user profiles .
Outcome:	The proposed task integrates preference elicitation, recommendation, and persuasion within a conversational framework.

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Minda Hu, Tianqing Fang, Jianshu Zhang, Jun-Yu Ma, Zhisong Zhang, Jingyan Zhou, Hongming Zhang, Haitao Mi, Dong Yu, Irwin King

Interesting Culture: Social Relation Recognition from Videos via Culture De-confounding (2025.findings-emnlp)

Challenge:	Web agents powered by Large Language Models lack the ability to perform in uncertain web environments.
Approach:	They propose to reconstruct web agents' reasoning skills into chain-of-thought rationales by fine-tuning their LLM backbone into a web-based model.
Outcome:	The proposed approach significantly improves the agent self-improving benchmark OpenWebVoyager, demonstrating that it can be used to improve the agent's reasoning skills.

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Yuxuan Zhang, Yangfu Zhu, Haorui Wang, Bin Wu

ThinkSwitcher: When to Think Hard, When to Think Fast (2025.findings-emnlp)

Challenge:	a culturally-specific cultural context can be used to train relationship recognition models . cultural confounding factors can be learned, limiting ability to recognize social relationships in different cultures.
Approach:	They propose a culturally-based model that mitigates the influence of culture . they also construct a video social relation recognition dataset to facilitate discussion .
Outcome:	The proposed model surpasses state-of-the-art methods on several datasets.

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Guosheng Liang, Longguang Zhong, Ziyi Yang, Xiaojun Quan

MaGiX: A Multi-Granular Adaptive Graph Intelligence Framework for Enhancing Cross-Lingual RAG (2025.findings-emnlp)

Challenge:	Large reasoning models excel at solving complex tasks by leveraging long chain-of-thought (CoT) reasoning.
Approach:	They propose a framework that enables a single LRM to dynamically switch between short and long CoT modes based on task complexity.
Outcome:	The proposed framework reduces computational cost by 20-30% while maintaining high accuracy on complex tasks.

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Nguyen Manh Hieu, Vu Lam Anh, Hung Pham Van, Nam Le Hai, Linh Ngo Van, Nguyen Thi Ngoc Diep, Thien Huu Nguyen

LexTime: A Benchmark for Temporal Ordering of Legal Events (2025.findings-emnlp)

Challenge:	Recent advances in Graph-based RAG (GRAG) frameworks focus on knowledge graphs for cross-lingual retrieval.
Approach:	They propose a new GRAG framework for cross-lingual question answering . MaGiX constructs a multi-granular cross-linguistic knowledge graph using fine-grained attribute descriptions and cross-synonym edges.
Outcome:	The proposed framework outperforms prior GRAG systems in retrieval accuracy and generation quality.

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Claire Barale, Leslie Barrett, Vikram Sunil Bajaj, Michael Rovatsos

Challenge:	Existing benchmarks lack specialized language evaluation for LLMs on event ordering in legal contexts.
Approach:	They propose to use a dataset to evaluate LLMs' event ordering capabilities in legal language to evaluate their temporal relations with legal events.
Outcome:	The proposed model improves on a dataset of 512 instances from U.S. Federal Complaints with annotated event pairs and their temporal relations.

Beyond the Surface: A Solution-Aware Retrieval Model for Competition-level Code Generation (2025.findings-emnlp)

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Shiwen Zhang, Lingxiang Wang, Hainan Zhang, Ziwei Wang, Sijia Wen, Zhiming Zheng

X-Boundary: Establishing Exact Safety Boundary to Shield LLMs from Jailbreak Attacks without Compromising Usability (2025.findings-emnlp)

Challenge:	Existing retrieval models emphasize surface-level semantic similarity, neglecting deeper solution-level logical similarities.
Approach:	They propose a solution-aware ranking model empowered by synthetic data for competitive programming tasks.
Outcome:	The proposed ranking model outperforms existing retrieval models in precision and recall metrics.

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Xiaoya Lu, Dongrui Liu, Yi Yu, Luxin Xu, Jing Shao

Tag&Tab: Pretraining Data Detection in Large Language Models Using Keyword-Based Membership Inference Attack (2025.findings-emnlp)

Challenge:	Existing methods for enhancing LLM security compromise usability, study finds . boundary-safe representations close to harmful representations are disrupted, resulting in usability decline .
Approach:	They propose a method to push harmful representations away from boundary-safe representations and obtain an exact distinction boundary.
Outcome:	The proposed method reduces over-refusal rate and maintains general capability . it pushes harmful representations away from boundary-safe representations, thereby reducing usability.

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Sagiv Antebi, Edan Habler, Asaf Shabtai, Yuval Elovici

EcoLANG: Efficient and Effective Agent Communication Language Induction for Social Simulation (2025.findings-emnlp)

Challenge:	Recent studies on detecting pretraining data in large language models have focused on sentence-level membership inference attacks (MIAs) but these methods often exhibit poor accuracy, failing to account for the semantic importance of textual content and word significance.
Approach:	They propose a method that leverages established natural language processing techniques to tag keywords in input text and then uses them to obtain probabilities and calculate their average log-likelihood to determine input text membership.
Outcome:	The proposed method exploits established natural language processing techniques to tag keywords in input text and calculate their average log-likelihood to determine input text membership.

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Xinyi Mou, Chen Qian, Wei Liu, Ling Yan, Yao Hu, Xuanjing Huang, Zhongyu Wei

Revealing the Inherent Instructability of Pre-Trained Language Models (2025.findings-emnlp)

Challenge:	Large language models (LLMs) have demonstrated impressive ability to role-play humans and replicate complex social dynamics.
Approach:	They propose an efficient agent communication language induction for social simulations that reduces token consumption by over 20%.
Outcome:	The proposed model reduces token consumption by over 20% while preserving human language.

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Seokhyun An, Minji Kim, Hyounghun Kim

What Media Frames Reveal About Stance: A Dataset and Study about Memes in Climate Change Discourse (2025.findings-emnlp)

Challenge:	Pre-trained large language models perform multitask learning during their pre-training . a new technique, Response Tuning, removes the instruction and its corresponding mapping to the response from instruction tuning.
Approach:	They propose a method which removes the instruction and its mapping to the response from instruction tuning.
Outcome:	The proposed model can respond to a wide range of instructions . it can recognize and reject unsafe queries after learning from response data.

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Shijia Zhou, Siyao Peng, Simon M. Luebke, Jörg Haßler, Mario Haim, Saif M. Mohammad, Barbara Plank

Rethinking Personality Assessment from Human-Agent Dialogues: Fewer Rounds May Be Better Than More (2025.findings-emnlp)

Challenge:	Media framing is a method of shaping public perceptions of issues, but the interaction between stance and media frame remains unexplored.
Approach:	They propose to use a dataset of climate-change memes annotated with stance and media frames to conceptualize and computationally explore this interaction.
Outcome:	The proposed dataset includes 1,184 climate-change memes sourced from 47 subreddits and enables analysis of frame prominence over time and communities.

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Baiqiao Zhang, Zhifeng Liao, Xiangxian Li, Chao Zhou, Juan Liu, Xiaojuan Ma, Yulong Bian

TailorRPA: A Retrieval-Based Framework for Eliciting Personalized and Coherent Role-Playing Agents in General Domain (2025.findings-emnlp)

Challenge:	Existing personality assessment datasets based on natural language do not consider interactivity.
Approach:	They propose to use a Chinese dataset to study the effects of different interaction rounds and agent personalities on personality assessment.
Outcome:	The proposed dataset contains 1260 interaction rounds between humans and agents with different personalities.

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Zhenpeng Gao, Xiaofen Xing, Xiangmin Xu

SCE: Semantic Consistency Enhanced Reinforcement Learning for Multi-Hop Knowledge Graph Reasoning (2025.findings-emnlp)

Challenge:	a recent study has shown that general domain oriented role-playing agents can maintain character properties in a wide range of tasks beyond scenario based chit-chatting.
Approach:	They propose a retrieval-based framework to harvest tailored general domain instructions . they use general-domain protective queries to shape character-wise knowledge boundary .
Outcome:	The proposed framework improves integration of fine-grained memories and protects character knowledge boundary . it also improves character hallucination in general domain, compared to baseline methods .

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Yanwen Huang, Yao Liu, Qiao Liu, Rui Hou, Tingting Dai

ReGraphRAG: Reorganizing Fragmented Knowledge Graphs for Multi-Perspective Retrieval-Augmented Generation (2025.findings-emnlp)

Challenge:	Existing approaches to multihop reasoning fail to address the problem of spurious paths . existing approaches neglect the internal semantic consistency of the reward function .
Approach:	They propose a framework that incorporates semantic consistency into the reward function to guide multi-hop reasoning.
Outcome:	The proposed framework outperforms baseline methods and facilitates more interpretable reasoning paths.

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Soohyeong Kim, Seok Jun Hwang, JungHyoun Kim, Jeonghyeon Park, Yong Suk Choi

GASE: Generatively Augmented Sentence Encoding (2025.findings-emnlp)

Challenge:	Graph-based RAG systems have been promising for enabling multi-hop reasoning . but when knowledge graphs are constructed from unstructured documents, they suffer from fragmentation .
Approach:	They propose a framework to reconstruct and enrich fragmented knowledge graphs . they propose three core components: Graph Reorganization, Perspective Expansion, and Query-aware Reranking.
Outcome:	The proposed framework outperforms state-of-the-art benchmarks on four benchmarks . it achieves over 80% diversity win rate and enables multi-hop reasoning .

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Manuel Frank, Haithem Afli

The “r” in “woman” stands for rights. Auditing LLMs in Uncovering Social Dynamics in Implicit Misogyny (2025.findings-emnlp)

Challenge:	Generatively Augmented Sentence Encoding variates the input text by paraphrasing, summarizing, or extracting keywords, followed by pooling the original and synthetic embeddings.
Approach:	They propose a training-free approach to improve sentence embeddings by applying generative text models for data augmentation at inference time.
Outcome:	The proposed approach does not require access to model parameters or computational resources typically required for fine-tuning state-of-the-art models.

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Arianna Muti, Chris Emmery, Debora Nozza, Alberto Barrón-Cedeño, Tommaso Caselli

Challenge:	a recent study examined misogynistic expressions in English and Italian . a taxonomy of social dynamics is used to identify misogorical expressions .
Approach:	They examine misogynistic expressions in English and Italian using a taxonomy of social dynamics . they find that LLMs struggle to follow instructions and reason in all settings .
Outcome:	The results show that misogynistic expressions are more often implicit than openly hostile . the authors show that LLMs struggle to follow instructions and reason in all settings .

Fact Verification on Knowledge Graph via Programmatic Graph Reasoning (2025.findings-emnlp)

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Yuanzhen Hao, Desheng Wu

Agent Trading Arena: A Study on Numerical Understanding in LLM-Based Agents (2025.findings-emnlp)

Challenge:	Existing methods for fact verification on knowledge graphs use implicit reasoning to predict entailment between claims and KG triples.
Approach:	They propose a framework that integrates large language models for fact verification on knowledge graphs.
Outcome:	The proposed framework outperforms existing methods on knowledge graphs with 86.82% accuracy.

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Tianmi Ma, Jiawei Du, Wenxin Huang, Wenjie Wang, Liang Xie, Xian Zhong, Joey Tianyi Zhou

Why We Feel What We Feel: Joint Detection of Emotions and Their Opinion Triggers in E-commerce (2025.findings-emnlp)

Challenge:	Existing approaches to large language models are limited to historical backtesting and static data.
Approach:	a new large-language model is developed to simulate real-time trading in a virtual stock market . the agent trading arena simulates real-world bid-ask interactions and provides real-life trading scenarios .
Outcome:	The Agent Trading Arena simulates real-world market conditions and directly impacts price dynamics.

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Arnav Attri, Anuj Attri, Suman Banerjee, Amey Patil, Muthusamy Chelliah, Nikesh Garera, Pushpak Bhattacharyya

Use Random Selection for Now: Investigation of Few-Shot Selection Strategies in LLM-based Text Augmentation (2025.findings-emnlp)

Challenge:	Existing research has not explored the joint task of emotion detection and explanatory span identification in e-commerce reviews.
Approach:	They propose a joint task unifying Emotion detection and Opinion Trigger extraction (EOT) which explicitly models the relationship between causal text spans (opinion triggers) and affective dimensions (emotion categories).
Outcome:	The proposed framework surpasses zero-shot and chain-of-thought techniques across e-commerce domains.

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Jan Cegin, Branislav Pecher, Jakub Simko, Ivan Srba, Maria Bielikova, Peter Brusilovsky

BanglaByT5: Byte-Level Modelling for Bangla (2025.findings-emnlp)

Challenge:	generative large language models are increasingly used for data augmentation tasks . text samples are mostly selected randomly and a comprehensive overview of other sample selection strategies is lacking.
Approach:	They compare random sample selection strategies and random sample sampling strategies to evaluate their effects in a low-resource setting.
Outcome:	The proposed model performance improvements are compared with other sample selection strategies.

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Pramit Bhattacharyya, Arnab Bhattacharya

XTRA: Cross-Lingual Topic Modeling with Topic and Representation Alignments (2025.findings-emnlp)

Challenge:	Large language models (LLMs) have achievedremarkable success across various natural lan-guage processing tasks.
Approach:	They propose a byte-level encoder-decoder model specifically tailored for Bangla.
Outcome:	The proposed model outperforms existing models in gen-erative and classification tasks and surpasses several multilingual and larger models.

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Tien Phat Nguyen, Ngo Vu Minh, Tung Nguyen, Linh Ngo Van, Duc Anh Nguyen, Dinh Viet Sang, Trung Le

CodeContests+: High-Quality Test Case Generation for Competitive Programming (2025.findings-emnlp)

Challenge:	XTRA aims to uncover shared semantic themes across languages . previous methods have achieved improvements in topic diversity but struggle to ensure high topic coherence and consistent alignment across languages.
Approach:	a new framework unifies Bag-of-Words modeling with multilingual embeddings is proposed to address this problem . XTRA introduces two core components: (1) representation alignment and (2) topic alignment to enforce cross-lingual consistency.
Outcome:	XTRA outperforms baselines in topic coherence, diversity, and alignment quality on multilingual corpora.

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Zihan Wang, Siyao Liu, Yang Sun, Ming Ding, Hongyan Li

SPO: Self Preference Optimization with Self Regularization (2025.findings-emnlp)

Challenge:	Competitive programming has become a key task for training and evaluating large language models . but test cases of competitive programming problems are often difficult to obtain .
Approach:	They propose an LLM-based agent system that creates high-quality test cases for competitive programming problems.
Outcome:	The proposed system improves code tests on a CodeContests dataset with pass/fail labels.

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Yuhao Sun, Yifan Zhang, Quandong Wang, Qinzhuo Wu, Wei Liu, Jian Luan

Long-context Language Models Fail in Basic Retrieval Tasks Without Sufficient Reasoning Steps (2025.findings-emnlp)

Challenge:	Existing reference-free preference optimization methods exhibit higher training efficiency but are prone to overoptimization, leading to performance degradation.
Approach:	They propose a reference-free preference optimization method that replaces the logsigmoid loss function with a SiLU function to improve the model's performance.
Outcome:	The proposed method achieves 7% improvement over SimPO on AlpacaEval 2 and MT-Bench.

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Yijiong Yu, Zhixiao Qi, Yongfeng Huang, Wei Wang, null Weifeng.liu, Ran Chen, Ji Pei

Benchmarking Critical Questions Generation: A Challenging Reasoning Task for Large Language Models (2025.findings-emnlp)

Challenge:	despite their extensive context window, long-context language models fail in some basic cases . a recent study shows that long-cot methods are not necessary for long-constituency tasks .
Approach:	a new study evaluates long-context language models with a large context window . the authors propose a method that can be well addressed with arbitrary reasoning steps .
Outcome:	The proposed methods are well addressed with a sufficient number of reasoning steps, guided by specific CoT prompts.

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Blanca Calvo Figueras, Rodrigo Agerri

ResearchArena: Benchmarking Large Language Models’ Ability to Collect and Organize Information as Research Agents (2025.findings-emnlp)

Challenge:	Progress in the task of Critical Questions Generation has been hindered by the lack of suitable datasets and automatic evaluation standards.
Approach:	They propose a comprehensive approach to support the development and benchmarking of systems for this task.
Outcome:	The proposed approach supports the development and benchmarking of systems for this task.

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Hao Kang, Chenyan Xiong

LLMs are Privacy Erasable (2025.findings-emnlp)

Challenge:	Large language models excel across many natural language processing tasks but face challenges in domain-specific, analytical tasks such as conducting research surveys.
Approach:	They propose a benchmark to evaluate LLMs' capabilities in conducting research surveys.
Outcome:	The proposed benchmark is designed to evaluate LLMs' capabilities in conducting research surveys.

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Zipeng Ye, Wenjian Luo

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models (2025.findings-emnlp)

Challenge:	a new study examines the privacy of large language models and their capabilities . the study aims to address the balance between the convenience of LLMs and user privacy concerns .
Approach:	They propose a strategy that safeguards user prompt while accessing LLM cloud services . they evaluate the efficacy of their method across prominent LLM benchmarks .
Outcome:	The proposed method thwarts reconstruction attacks and improves model performance . it also surpasses the results reported in official model cards .

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Abdelrahman Abdallah, Bhawna Piryani, Jamshid Mozafari, Mohammed Ali, Adam Jatowt

DeAR: Dual-Stage Document Reranking with Reasoning Agents via LLM Distillation (2025.findings-emnlp)

Challenge:	a systematic and comprehensive empirical evaluation of state-of-the-art reranking methods is presented.
Approach:	They evaluate 22 reranking methods including 40 variants across established benchmarks . primary goal is to determine whether performance disparity exists between LLM-based reranters and lightweight counterparts based on novel queries .
Outcome:	The proposed methods perform better on familiar queries than lightweight models, the authors show .

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Abdelrahman Abdallah, Jamshid Mozafari, Bhawna Piryani, Adam Jatowt

CANDY: Benchmarking LLMs’ Limitations and Assistive Potential in Chinese Misinformation Fact-Checking (2025.findings-emnlp)

Challenge:	DeAR is an open-source framework that decouples the tasks of LLMs with holistic cross-document analysis.
Approach:	They propose an open-source framework that decouples relevance scoring with holistic cross-document analysis.
Outcome:	The proposed framework outperforms open-source frameworks in QA and open-domain QA.

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Ruiling Guo, Xinwei Yang, Chen Huang, Tong Zhang, Yong Hu

E-Verify: A Paradigm Shift to Scalable Embedding-based Factuality Verification (2025.findings-emnlp)

Challenge:	CANDY is a benchmark to evaluate the capabilities and limitations of large language models (LLMs) for fact-checking misinformation.
Approach:	a team of researchers develop a benchmark to evaluate the capabilities and limitations of large language models in fact-checking misinformation in Chinese.
Outcome:	CANDY is a benchmark to evaluate the capabilities and limitations of large language models in fact-checking misinformation in China.

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Zeyang Liu, Jingfeng Xue, Xiuqi Yang, Wenbiao Du, Jiarun Fu, Junbao Chen, Wenjie Guo, Yong Wang

LLM Jailbreak Detection for (Almost) Free! (2025.findings-emnlp)

Challenge:	Existing factuality verification methods follow a Decompose-Then-Verify paradigm, which improves granularity but suffers from poor scalability and efficiency.
Approach:	They propose a Decompose-Embed-Interact paradigm that shifts factuality verification from costly text-level reasoning to efficient alignment in embedding space.
Outcome:	The proposed paradigm shifts factuality verification from costly text-level reasoning to efficient alignment in embedding space .

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Guorui Chen, Yifan Xia, Xiaojun Jia, Zhijiang Li, Philip Torr, Jindong Gu

When to Continue Thinking: Adaptive Thinking Mode Switching for Efficient Reasoning (2025.findings-emnlp)

Challenge:	Existing methods for detecting jailbreak prompts entail significant computational costs .
Approach:	They propose a free jailbreak detection method which scales logits by temperature to detect jailbreak prompts .
Outcome:	The proposed method detects jailbreak prompts with no additional computational costs.

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Xiaoyun Zhang, Jingqing Ruan, Xing Ma, Yawen Zhu, Haodong Zhao, Hao Li, Jiansong Chen, Ke Zeng, Xunliang Cai

Plugging Schema Graph into Multi-Table QA: A Human-Guided Framework for Reducing LLM Reliance (2025.findings-emnlp)

Challenge:	Large reasoning models (LRMs) incur excessive computational overhead due to redundant reasoning, especially on simple tasks.
Approach:	They propose an Adaptive Self-Recovery Reasoning framework that suppresses unnecessary reasoning and enables implicit recovery.
Outcome:	The proposed framework suppresses unnecessary reasoning and enables implicit recovery.

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Xixi Wang, Miguel Costa, Jordanka Kovaceva, Shuai Wang, Francisco C. Pereira

Evolution in Simulation: AI-Agent School with Dual Memory for High-Fidelity Educational Dynamics (2025.findings-emnlp)

Challenge:	Existing methods based on semantic similarity work well only on simplified datasets . Existing approaches based only on semantic similarities struggle to handle complex tables .
Approach:	They propose a graph-based framework that leverages human-curated relational knowledge to explicitly encode schema links and join paths.
Outcome:	The proposed framework leverages human-curated relational knowledge to encode schema links and join paths.

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Sheng Jin, Haoming Wang, Zhiqi Gao, Yongbo Yang, Bao Chunjia, Chengliang Wang

Challenge:	Large language models (LLMs) based Agents are increasingly pivotal in simulating complex human systems and interactions.
Approach:	They propose an AI-Agent School system that leverages agents for simulating educational dynamics.
Outcome:	The proposed system can simulate complex educational dynamics in simulated schools.

Retrieval-Augmented Machine Translation with Unstructured Knowledge (2025.findings-emnlp)

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Jiaan Wang, Fandong Meng, Yingxue Zhang, Jie Zhou

MARS-Bench: A Multi-turn Athletic Real-world Scenario Benchmark for Dialogue Evaluation (2025.findings-emnlp)

Challenge:	Retrieval-augmented generation (RAG) is a new approach to enhance large language models (LLMs).
Approach:	They propose a multi-task training method to teach LLMs how to use information from multilingual documents during their translation.
Outcome:	The proposed method improves LLMs by 1.6-3.1 BLEU and 1.0-2.0 COMET scores in En-Zh, and 1.7-2.9 BLUE and 2.1-2.7 COMET score in En de.

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Chenghao Yang, Yinbo Luo, Zhoufutu Wen, Qi Chu, Tao Gong, Longxiang Liu, Kaiyuan Zhang, Jianpeng Jiao, Ge Zhang, Wenhao Huang, Nenghai Yu

UTMath: A Benchmark for Math Evaluation with Unit Test (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have been widely adopted in real-world dialogue applications, but their robustness is criticized all along.
Approach:	They propose to use play-by-play text commentary to build a multi-turn athletic real-world scenario dialogue benchmark to evaluate three critical aspects of multi-turned conversations: ultra multi- turn, interactive multi-twist, and cross-turn tasks.
Outcome:	The proposed benchmarks outperform open-source LLMs on three critical aspects of multi-turn conversations: ultra multi-turned, interactive multi- turn, and cross-turn tasks.

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Bo Yang, Qingping Yang, Yingwei Ma, Runtao Liu

The Green KNIGHT: Green Machine Translation with Knowledge-Distilled, Narrow, Inexpensive, Greedy, Hybrid Transformers (2025.findings-emnlp)

Challenge:	Prevailing benchmarks for mathematical reasoning include MATH and AIME . predicated on single-instantiation problems with fixed numbers, these models leave generalization on isomorphic problem variants untested.
Approach:	They propose a mathematical reasoning benchmark that quantifies solution accuracy and solution space generality.
Outcome:	The proposed model solves 1,053 problems spanning 9 mathematical domains . the best-performing model solved only 32.57% of the problems .

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Andreas Guta, Frithjof Petrick, Peter Polák

Constructing Your Model’s Value Distinction: Towards LLM Alignment with Anchor Words Tuning (2025.findings-emnlp)

Challenge:	State-of-the-art neural machine translation models deliver high-quality translations at the expense of high inference latency and energy consumption.
Approach:	They propose a hardware-agnostic collection of recipes to optimize translation speed and energy consumption.
Outcome:	The Green KNIGHT optimizes translation speed and energy consumption with a moderate trade-off in quality.

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Zhen Yang, Ping Jian, Chengzhi Li, Chenxu Wang, Xinyue Zhang, Wenpeng Lu

Challenge:	a study of large language models (LLMs) shows that they can generate outputs that are honest, positive, harmless, etc.
Approach:	They propose a method that amplifies logits difference between positive and negative tokens . they propose to use the logits gap to generate positive and positive tokens after alignment .
Outcome:	The proposed method achieves effective alignment, but requires fewer computational resources compared to training-time alignment methods.

MCiteBench: A Multimodal Benchmark for Generating Text with Citations (2025.findings-emnlp)

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Caiyu Hu, Yikai Zhang, Tinghui Zhu, Yiwei Ye, Yanghua Xiao

Do LLMs Know and Understand Domain Conceptual Knowledge? (2025.findings-emnlp)

Challenge:	Existing work focuses on generating citations for text-only content . experimental results reveal MLLMs struggle to ground outputs reliably when handling multimodal input .
Approach:	They propose a benchmark to assess the ability of MLLMs to generate text with citations in multimodal contexts.
Outcome:	The proposed benchmark assesses the ability of MLLMs to generate text with citations in multimodal contexts.

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Sijia Shen, Feiyan Jiang, Peiyan Wang, Yubo Feng, Yuchen Jiang, Chang Liu

Agent Laboratory: Using LLM Agents as Research Assistants (2025.findings-emnlp)

Challenge:	Concept sememe tree is a hierarchical structure that represents lexical meaning by combining sememes and their relationships.
Approach:	They introduce a Neighbor Semantic Structure (NSS) and a Chain-of-Thought prompting method to evaluate the effectiveness of various Large Language Models (LLMs) in generating concept sememe trees.
Outcome:	The proposed method guides LLMs through an analysis of a term’s intrinsic core concepts, essential attributes, and semantic relationships, enabling the generation of concept sememe trees.

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Samuel Schmidgall, Yusheng Su, Ze Wang, Ximeng Sun, Jialian Wu, Xiaodong Yu, Jiang Liu, Michael Moor, Zicheng Liu, Emad Barsoum

Retrieval-Augmented Generation with Hierarchical Knowledge (2025.findings-emnlp)

Challenge:	Agent Laboratory is an autonomous LLM-based framework that can complete the entire research process.
Approach:	Agent Laboratory is an autonomous LLM-based framework that can complete the entire research process.
Outcome:	Agent Laboratory is an autonomous LLM-based framework that can complete the entire research process.

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Haoyu Huang, Yongfeng Huang, Yang Junjie, Zhenyu Pan, Yongqiang Chen, Kaili Ma, Hongzhi Chen, James Cheng

Regularized Contrastive Decoding with Hard Negative Samples for LLM Hallucination Mitigation (2025.findings-emnlp)

Challenge:	Existing RAG methods do not utilize hierarchical knowledge in human cognition, which limits the capabilities of RAG systems.
Approach:	They propose a graph-based approach that utilizes hierarchical knowledge to enhance the semantic understanding and structure capturing capabilities of RAG systems.
Outcome:	The proposed approach achieves significant performance improvements over the state-of-the-art methods.

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Haonan Sheng, Dou Hu, Lingwei Wei, Wei Zhou, Songlin Hu

CharacterCraft: Bridging the Literature-Reality Dialogue Gap for Practical Role-Playing Agents (2025.findings-emnlp)

Challenge:	Large language models are prone to generate hallucinations, which can undermine their reliability in high-stakes applications.
Approach:	They propose a method to capture hallucination signals for mitigating hallucis in large language models by regularizing the model's internal signals to a weaker model .
Outcome:	The proposed method achieves better hallucination mitigation performance on four benchmarks.

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Xuyan Yin, Xinran Yang, Zihao Li, Lixin Zou, Chenliang Li

Drift: Decoding-time Personalized Alignments with Implicit User Preferences (2025.findings-emnlp)

Challenge:	Existing dialogue datasets have a bias between query distributions and real-world user language usage.
Approach:	They propose a framework for Chinese role-playing and a robust evaluation method . they propose specialized Chinese dialogue extraction model and specialized memory retrieval module .
Outcome:	The proposed framework extracts character dialogue from novels and ensures high data quality.

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Minbeom Kim, Kang-il Lee, Seongho Joo, Hwaran Lee, Thibaut Thonet, Kyomin Jung

Discovering Semantic Subdimensions through Disentangled Conceptual Representations (2025.findings-emnlp)

Challenge:	Drift personalizes large language models at decoding time with implicit user preferences . Unlike traditional Reinforcement Learning from Human Feedback, Drift operates in a training-free manner .
Approach:	They propose a framework that personalizes large language models at decoding time with implicit user preferences.
Outcome:	The proposed framework personalizes large language models at decoding time with implicit user preferences.

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Yunhao Zhang, Shaonan Wang, Nan Lin, Xinyi Dong, Chong Li, Chengqing Zong

Identifying Aspects in Peer Reviews (2025.findings-emnlp)

Challenge:	Existing approaches focus on predefined dimensions that overlook finer conceptual distinctions . a new framework is proposed to investigate the subdimensions underlying coarse-grained semantic dimensions .
Approach:	They propose a framework that decomposes word embeddings into multiple sub-embeddings . they propose to map these subdimensions to brain activation to assess their plausibility .
Outcome:	The proposed framework decomposes word embeddings from large language models into sub-embeddings, each encoding specific semantic information.

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Sheng Lu, Ilia Kuznetsov, Iryna Gurevych

Challenge:	Existing approaches to peer review are limited in how they identify aspects . a growing volume of peer review submissions is straining the process .
Approach:	They propose a data-driven schema for deriving aspects from peer reviews . they propose augmented peer reviews and show how it can be used for community-level review analysis.
Outcome:	The proposed approach can be used to support peer review, but lacks formal definition of aspect . it also shows that the choice of aspects can impact downstream applications .

Tree-Structured Non-Autoregressive Decoding for Sequence-to-Sequence Text Generation (2025.findings-emnlp)

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Pengyu Ji, Yufei Liu, Xiang Hu, Kewei Tu

Towards More Efficient Post-training via Fourier Domain Adapter Framework (2025.findings-emnlp)

Challenge:	Autoregressive Transformers suffer from high inference latency due to sequential token generation.
Approach:	They propose a tree-structured non-autoregressive decoding paradigm that bridges autoregressive and non-automatic decoding.
Outcome:	The proposed paradigm outperforms autoregressive and non-autoregressive decoding in machine translation and paraphrase generation.

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Yijia Fan, Jusheng Zhang, Keze Wang

KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering (2025.findings-emnlp)

Challenge:	FDA reparameterizes the core projection operation of the adapter module directly in the Fourier domain.
Approach:	They propose a framework that reparameterizes the core projection operation of the adapter module directly in the Fourier domain.
Outcome:	The proposed framework outperforms existing parameter-efficient fine-tuning methods on GLUE, E2E NLG, and instruction tuning benchmarks.

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Yushi Sun, Kai Sun, Yifan Ethan Xu, Xiao Yang, Xin Luna Dong, Nan Tang, Lei Chen

Not All Features Deserve Attention: Graph-Guided Dependency Learning for Tabular Data Generation with Language Models (2025.findings-emnlp)

Challenge:	Traditional Knowledge Graph Question Answering (KGQA) methods rely on semantic parsing to retrieve knowledge strictly necessary for answer generation.
Approach:	They propose a retrieval-filtering-summarization pipeline that enhances QA coverage by retrieving a broader subgraph likely to contain relevant information.
Outcome:	The proposed pipeline surpasses state-of-the-art solutions by about 7% in quality and exceeds GPT-4o (Tool) by 10-21%.

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Zheyu Zhang, Shuo Yang, Bardh Prenkaj, Gjergji Kasneci

CCG: Rare-Label Prediction via Neural SEM–Driven Causal Game (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have shown strong potential for tabular data generation by modeling textualized feature-value pairs.
Approach:	They propose a method that explicitly integrates sparse dependency graphs into LLMs’ attention mechanism.
Outcome:	The proposed method outperforms existing LLM-based approaches by up to 12% on complex datasets while achieving competitive results with state-of-the-art approaches in synthetic data quality.

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Yijia Fan, Jusheng Zhang, Kaitong Cai, Jing Yang, Keze Wang

Multimodal Emotion Recognition in Conversations: A Survey of Methods, Trends, Challenges and Prospects (2025.findings-emnlp)

Challenge:	Multi-label classification (MLC) faces persistent challenges from label imbalance, spurious correlations, distribution shifts, especially in rare label prediction.
Approach:	They propose a Causal Cooperative Game framework that models multi-player cooperative process for multi-label classification.
Outcome:	The proposed framework improves rare label prediction and overall robustness compared to baselines.

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ChengYan Wu, Yiqiang Cai, Yang Liu, Pengxu Zhu, Yun Xue, Ziwei Gong, Julia Hirschberg, Bolei Ma

When Allies Turn Foes: Exploring Group Characteristics of LLM-Based Multi-Agent Collaborative Systems Under Adversarial Attacks (2025.findings-emnlp)

Challenge:	Multimodal Emotion Recognition in Conversations (MERC) is a new way to enhance human-computer interaction.
Approach:	This survey offers a systematic overview of Multimodal Emotion Recognition in Conversations . it examines motivations, core tasks, representative methods, and evaluation strategies .
Outcome:	The survey examines the effectiveness of MERC and its evaluation strategies.

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Jiahao Zhang, Baoshuo Kan, Tao Gong, Fu Lee Wang, Tianyong Hao

EditID: Training-Free Editable ID Customization for Text-to-Image Generation (2025.findings-emnlp)

Challenge:	a new study examines the group characteristics of adversarial agents in multi-agent collaborative systems . collaborative agents are tasked with generating counterfactual answers to a given collaborative problem .
Approach:	They evaluate collaborative systems under adversarial attacks and propose methods to mitigate them . they also introduce a new metric to quantify the robustness of collaborative systems against such attacks .
Outcome:	The proposed method has been proven effective against adversarial attacks.

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Guandong Li, Zhaobin Chu

OSC: Cognitive Orchestration through Dynamic Knowledge Alignment in Multi-Agent LLM Collaboration (2025.findings-emnlp)

Challenge:	Existing text-to-image models for customized IDs focus on ID consistency while neglecting editability.
Approach:	They propose a training-free approach to editable customized IDs based on the DiT architecture . EditID deconstructs existing text-to-image models into image generation branch and character feature branch .
Outcome:	The proposed solution achieves high-quality images with editable IDs while maintaining ID consistency.

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Jusheng Zhang, Yijia Fan, Kaitong Cai, Xiaofei Sun, Keze Wang

VideoLLM Knows When to Speak: Enhancing Time-Sensitive Video Comprehension with Video-Text Duet Interaction Format (2025.findings-emnlp)

Challenge:	Prior work has advanced agent selection and result aggregation, efficient linguistic interactions for deep collaboration among expert agents remain a critical bottleneck.
Approach:	They propose a knowledge-aware adaptive collaboration framework to enhance cognitive synergy in multi-agent systems with large language models.
Outcome:	The proposed framework improves synergy between agents and language models by enabling agents to dynamically perceive their collaborators’ cognitive states.

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Yueqian Wang, Xiaojun Meng, Yuxuan Wang, Jianxin Liang, Jiansheng Wei, Huishuai Zhang, Dongyan Zhao

To Answer or Not to Answer (TAONA): A Robust Textual Graph Understanding and Question Answering Approach (2025.findings-emnlp)

Challenge:	Recent studies on video large language models focus on model architectures and training datasets . interaction format between user and model is unsatisfactory for time-sensitive tasks .
Approach:	They propose a video-text duet interaction format that allows for continuous playback of the video . when a text message ends, the video continues to play, similar to the alternative of two performers in a duet.
Outcome:	The proposed format improves performance on time-sensitive tasks with minimal training efforts.

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Yuchen Yan, Aakash Kolekar, Sahika Genc, Wenju Xu, Edward W Huang, Anirudh Srinivasan, Mukesh Jain, Qi He, Hanghang Tong

Understanding Refusal in Language Models with Sparse Autoencoders (2025.findings-emnlp)

Challenge:	Existing studies assume that generated answers integrate all relevant information from the textual graph.
Approach:	They propose a novel GraphRAG model that integrates all relevant information from the textual graph into the generated answer.
Outcome:	Extensive experiments validate TAONA’s superior performance for both A-side and B-side tasks.

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Wei Jie Yeo, Nirmalendu Prakash, Clement Neo, Ranjan Satapathy, Roy Ka-Wei Lee, Erik Cambria

Where Did That Come From? Sentence-Level Error-Tolerant Attribution (2025.findings-emnlp)

Challenge:	a study of refusal in instruction-tuned language models identifies latent features that causally mediate refusal behaviors.
Approach:	They conduct a mechanistic study of refusal in instruction-tuned LLMs using sparse autoencoders . they identify latent features that causally mediate refusal behaviors using sparsed autoencoding .
Outcome:	The proposed method validates refusal-related features across multiple datasets.

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Ori Ernst, Aviv Slobodkin, Meng Cao, Sihui Wei, Jackie CK Cheung

Alleviating Performance Degradation Caused by Out-of-Distribution Issues in Embedding-Based Retrieval (2025.findings-emnlp)

Challenge:	Existing task definitions exclude unsupported or hallucinated content leaving them unattributed . authors propose a new definition for sentence-level error-tolerant attribution .
Approach:	They propose a new definition for sentence-level error-tolerant attribution that extends attribution to include incorrect or hallucinated content.
Outcome:	The proposed approach reduces annotation time and facilitates hallucination fixing.

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Haotong Bao, Jianjin Zhang, Qi Chen, Weihao Han, Zhengxin Zeng, Ruiheng Chang, Mingzheng Li, Hao Sun, Weiwei Deng, Feng Sun, Qi Zhang

Can LLMs Find a Needle in a Haystack? A Look at Anomaly Detection Language Modeling (2025.findings-emnlp)

Challenge:	Recent studies reveal query out-of-distribution issues degrading ANN performance . a distribution regularizer is introduced into the encoder training objective to encourage alignment between query and base embeddings.
Approach:	They introduce a distribution regularizer into the encoder training objective to encourage alignment between query and base embeddings.
Outcome:	The proposed method consistently improves retrieval performance across multiple datasets.

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Leslie Barrett, Vikram Sunil Bajaj, Robert John Kingan

Beyond Single Frames: Can LMMs Comprehend Implicit Narratives in Comic Strip? (2025.findings-emnlp)

Challenge:	Anomaly detection (AD) is a problem in machine learning, but it is not always competitive on certain datasets.
Approach:	They propose a new approach to Anomaly detection based on large pre-trained language models in three modalities.
Outcome:	The proposed model beats baselines on anomaly detection when presented as imbalanced classification problem regardless of the concentration of anomalous samples.

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Xiaochen Wang, Heming Xia, Jialin Song, Longyu Guan, Qingxiu Dong, Rui Li, Yixin Yang, Yifan Pu, Weiyao Luo, Yiru Wang, Xiangdi Meng, Wenjie Li, Zhifang Sui

Enhancing Multi-Agent Debate System Performance via Confidence Expression (2025.findings-emnlp)

Challenge:	Large Multimodal Models have demonstrated strong performance on vision-language benchmarks, yet current evaluations focus on single-image reasoning.
Approach:	STRIPCIPHER is a benchmark designed to evaluate model ability on understanding implicit narratives in silent comics.
Outcome:	STRIPCIPHER is a high-quality, human-annotated dataset featuring fine-grained annotations and comprehensive coverage of varying difficulty levels.

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Zijie Lin, Bryan Hooi

Challenge:	Multi-Agent Debate systems leverage multiple LLMs to improve task performance.
Approach:	They propose to integrate confidence expression into MAD systems to help LLMs communicate their confidence levels.
Outcome:	The proposed approach improves debate effectiveness and overall system performance by integrating confidence expression into MAD systems.

The Face of Persuasion: Analyzing Bias and Generating Culture-Aware Ads (2025.findings-emnlp)

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Aysan Aghazadeh, Adriana Kovashka

Challenge:	Text-to-image models are appealing for customizing visual ads and targeting specific populations.
Approach:	We examine the disparate level of persuasiveness of ads that are identical except for gender/race of the people portrayed.
Outcome:	The proposed technique is based on a demographic bias analysis of ads for different topics and a disparate level of persuasiveness of ads that are identical except for gender/race of the people portrayed.

SIFT: Grounding LLM Reasoning in Contexts via Stickers (2025.findings-emnlp)

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Zihao Zeng, Xuyao Huang, Boxiu Li, Zhijie Deng

When Inverse Data Outperforms: Exploring the Pitfalls of Mixed Data in Multi-Stage Fine-Tuning (2025.findings-emnlp)

Challenge:	Using a new approach, we can improve the pass@1 accuracy of LLM reasoning in large language models.
Approach:	They propose a method that leverages increasing inference-time compute to ground LLM reasoning in contexts.
Outcome:	The proposed approach improves pass@1 accuracy of DeepSeek-R1 on AIME2024 from 78.33% to 85.67% and that on Aime2025 from 69.8% to 77.33%.

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Mengyi Deng, Xin Li, Tingyu Zhu, Zhicheng Yang, Zhijiang Guo, Wei Wang

LUME: LLM Unlearning with Multitask Evaluations (2025.findings-emnlp)

Challenge:	Existing methods for o1-level performance focus on unidirectional supervised fine-tuning (SFT), overlooking the intricate interplay between diverse reasoning patterns.
Approach:	They construct a reverse reasoning dataset and examine how it is supervised . they find that naively mixing forward and reverse data during SFT weakens the directional distinction .
Outcome:	The proposed model improves accuracy by 1.6%–6.8% over a standard model.

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Anil Ramakrishna, Yixin Wan, Xiaomeng Jin, Kai-Wei Chang, Zhiqi Bu, Bhanukiran Vinzamuri, Volkan Cevher, Mingyi Hong, Rahul Gupta

Challenge:	Unlearning aims to remove copyrighted, sensitive, or private content from large language models without a full retraining.
Approach:	They propose a multi-task unlearning benchmark LUME that unlearns short novels, biographies and public biographie .
Outcome:	The proposed benchmark unlearns short novels, biographies and public biographie . it also releases fine-tuned models with 1B and 7B parameter sizes as targets .

How do Language Models Generate Slang: A Systematic Comparison between Human and Machine-Generated Slang Usages (2025.findings-emnlp)

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Siyang Wu, Zhewei Sun

Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning (2025.findings-emnlp)

Challenge:	Slang is a commonly used type of informal language that poses a daunting challenge to NLP systems.
Approach:	They compare human-attested slang and swiss-generated slurs with machine-generated ones . they find that LLMs have significant knowledge about the creative aspects of sling .
Outcome:	The proposed model compares human and machine-generated slang usages to find biases in human perceptions of sling . the results suggest that human-attested slms have significant knowledge about the creative aspects of a language .

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Siqu Ou, Hongcheng Liu, Pingjie Wang, Yusheng Liao, Chuan Xuan, Yanfeng Wang, Yu Wang

MedCOD: Enhancing English-to-Spanish Medical Translation of Large Language Models Using Enriched Chain-of-Dictionary Framework (2025.findings-emnlp)

Challenge:	Existing methods for dynamic spatial reasoning are limited to text or static visual domains .
Approach:	They propose a framework that augments textual reasoning chains with dynamic visual drafts .
Outcome:	The proposed framework outperforms existing methods in dynamic spatial reasoning tasks.

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Md Shahidul Salim, Lian Fu, Arav Adikesh Ramakrishnan, Zonghai Yao, Hong Yu

Chatbot To Help Patients Understand Their Health (2025.findings-emnlp)

Challenge:	MedCOD integrates domain-specific structured knowledge into large language models . evaluators evaluated four open-source LLMs with structured prompts .
Approach:	They propose a framework that integrates domain-specific structured knowledge into large language models . they constructed a parallel corpus of 2,999 English-Spanish MedlinePlus articles .
Outcome:	The proposed framework improves translation quality across four open-source LLMs.

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Won Seok Jang, Hieu Tran, Manav Shaileshkumar Mistry, Sai Kiran Gandluri, Yifan Zhang, Sharmin Sultana, Sunjae Kwon, Yuan Zhang, Zonghai Yao, Hong Yu

A Knapsack by Any Other Name: Presentation impacts LLM performance on NP-hard problems (2025.findings-emnlp)

Challenge:	NoteAid-Chatbot is a conversational AI designed to help patients better understand their health .
Approach:	They propose a new learning paradigm that leverages a multi-agent large language model and reinforcement learning framework without relying on costly human-generated training data.
Outcome:	The proposed framework surpasses non-expert human training methods.

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Alex Duchnowski, Ellie Pavlick, Alexander Koller

Is Safety Standard Same for Everyone? User-Specific Safety Evaluation of Large Language Models (2025.findings-emnlp)

Challenge:	EHOP is a collection of NP-hard optimization problems expressed in natural language . state-of-the-art LLMs solve textbook problems more accurately than their real-life counterparts, but they lack a truly robust reasoning mechanism.
Approach:	They introduce a dataset of everyday hard optimization problems (EHOP) which includes problem formulations found in computer science textbooks, versions dressed up as problems that could arise in real life, and variants with inverted rules.
Outcome:	The proposed dataset includes problem formulations found in computer science textbooks, versions dressed up as problems that could arise in real life, and variants with inverted rules.

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Yeonjun In, Wonjoong Kim, Kanghoon Yoon, Sungchul Kim, Mehrab Tanjim, Sangwu Park, Kibum Kim, Chanyoung Park

Jailbreak Attack Initializations as Extractors of Compliance Directions (2025.findings-emnlp)

Challenge:	Extensive benchmarks evaluate LLM safety relying heavily on general standards . no benchmark datasets exist to evaluate the user-specific safety of LLMs .
Approach:	a new benchmark is designed to assess user-specific aspect of LLM safety . authors propose a simple remedy based on chain-of-thought to improve user-specified safety.
Outcome:	a new benchmark assesses the user-specific aspect of LLM safety . the proposed solution improves user-specified safety by chain-of-thought .

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Amit LeVi, Rom Himelstein, Yaniv Nemcovsky, Avi Mendelson, Chaim Baskin

Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction (2025.findings-emnlp)

Challenge:	Safety-aligned LLMs respond to prompts with compliance or refusal, each corresponding to distinct directions in the model’s activation space.
Approach:	They propose an initialization framework that aims to project unseen prompts further along compliance directions.
Outcome:	The proposed initialization framework achieves an increased attack success rate and reduced computational overhead, highlighting the fragility of safety-aligned LLMs.

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Xinmeng Hou, Lingyue Fu, Chenhao Meng, Kounianhua Du, Hai Hu

A Comprehensive Survey on the Trustworthiness of Large Language Models in Healthcare (2025.findings-emnlp)

Challenge:	Existing approaches to extract aspects and opinions independently, optionally adding pairwise relations, often lead to error propagation and high time complexity.
Approach:	They propose a transition-based model that performs aspect and opinion extraction jointly and integrates contrastive-augmented optimization.
Outcome:	The proposed model outperforms previous models on two out of four datasets when trained on a single dataset.

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Manar Aljohani, Jun Hou, Sindhura Kommu, Xuan Wang

Self-Correction Makes LLMs Better Parsers (2025.findings-emnlp)

Challenge:	a survey of large language models in healthcare raises critical concerns around trustworthiness . trustworthy of LLMs in healthcare remains underexplored, lacking a systematic review .
Approach:	a new survey examines the trustworthiness of large language models in healthcare . a review examines how each dimension affects reliability and ethical deployment of LLMs .
Outcome:	The present study examines the trustworthiness of large language models in healthcare . it identifies key gaps in existing approaches and challenges posed by evolving paradigms .

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Ziyan Zhang, Yang Hou, Chen Gong, Zhenghua Li

Explaining Length Bias in LLM-Based Preference Evaluations (2025.findings-emnlp)

Challenge:	Large language models (LLMs) have achieved remarkable success across various natural language processing tasks, but they still face challenges in performing fundamental NLP tasks, such as syntactic parsing.
Approach:	They propose a method that leverages grammar rules from existing treebanks to guide LLMs in correcting previous errors.
Outcome:	The proposed method significantly improves performance on in-domain and cross-domain datasets.

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Zhengyu Hu, Linxin Song, Jieyu Zhang, Zheyuan Xiao, Tianfu Wang, Zhengyu Chen, Nicholas Jing Yuan, Jianxun Lian, Kaize Ding, Hui Xiong

Investigating Controversy Framing across Topics on Social Media (2025.findings-emnlp)

Challenge:	a preference evaluation metric is often biased towards longer responses, revealing a reliability problem . a decomposition of the preference evaluation into two components is needed to understand this bias.
Approach:	They propose to decompose the preference evaluation metric into two key components . the first component is length-dependent and related to trustworthiness .
Outcome:	The proposed evaluation metric is based on two components: desirability and information mass.

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Maxwell Weinzierl, Sanda M. Harabagiu

Challenge:	a novel method for discovering framings of controversial problems is proposed . framers of controversial issues can be explored across topics, the paper argues .
Approach:	This paper proposes a method for discovering and articulating framing of controversial problems . framers offer valuable insights into how and why controversial problems are discussed online .
Outcome:	The proposed method enables the investigation of how controversy is framed across topics.

HEAL: Hybrid Enhancement with LLM-based Agents for Text-attributed Hypergraph Self-supervised Representation Learning (2025.findings-emnlp)

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Ruochang Li, Xiao Luo, Zhiping Xiao, Wei Ju, Ming Zhang

ReMamba: Equip Mamba with Effective Long-Sequence Modeling (2025.findings-emnlp)

Challenge:	Existing approaches to enhance text-attributed hypergraph self-supervised learning are limited by label scarcity.
Approach:	They propose a data-centric approach that leverages large language models to enhance hypergraph self-supervised learning by integrating hyperedges into a self-representation framework.
Outcome:	The proposed approach generates informative nodes and hyperedges through multi-round interaction with LLM-based agents.

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Danlong Yuan, Jiahao Liu, Bei Li, Huishuai Zhang, Jingang Wang, Xunliang Cai, Dongyan Zhao

QUITO-X: A New Perspective on Context Compression from the Information Bottleneck Theory (2025.findings-emnlp)

Challenge:	Mamba models demonstrate superior inference efficiency and competitive performance on short-context tasks, but their capacity to comprehend long contexts is limited compared to transformer-based models.
Approach:	They propose a model which incorporates selective compression and adaptation techniques within a two-stage re-forward process, incurring minimal additional inference costs overhead.
Outcome:	The proposed model improves on the LongBench and L-Eval benchmarks by 3.2 and 1.6 points and attains performance almost on par with same-size transformer models.

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Yihang Wang, Xu Huang, Bowen Tian, Yueyang Su, Lei Yu, Huaming Liao, Yixing Fan, Jiafeng Guo, Xueqi Cheng

Linguistic Theories

Challenge:	Existing methods for compressing context by removing redundant tokens are inconsistent with the objective of retaining the most important tokens when conditioning on a given query.
Approach:	They propose a method that uses information bottleneck theory to compress context . they propose to remove redundant tokens using metrics such as self-information or perplexity .
Outcome:	The proposed method achieves a 25% increase in compression rate compared to the state-of-the-art .

Conv-Basis: A New Paradigm for Efficient Attention Inference and Gradient Computation in Transformers (2025.findings-emnlp)

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Yingyu Liang, Heshan Liu, Zhenmei Shi, Zhao Song, Zhuoyan Xu, Jiale Zhao, Zhen Zhuang

Mitigating Gender Bias via Fostering Exploratory Thinking in LLMs (2025.findings-emnlp)

Challenge:	a large computational cost for attention computation in large language models is a major obstacle .
Approach:	They propose a convolution-like structure for attention computation using convolution matrices . they then propose an efficient approximation method to approximate the attention matrix .
Outcome:	The proposed method achieves nearly linear time complexity in n1+o(1) time.

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Kangda Wei, Hasnat Md Abdullah, Ruihong Huang

Beyond the Textual: Generating Coherent Visual Options for MCQs (2025.findings-emnlp)

Challenge:	Large Language Models often exhibit gender bias, resulting in unequal treatment of male and female subjects across contexts.
Approach:	They propose a framework that encourages exploratory thinking in large language models . the framework generates story pairs featuring male and female protagonists in structurally identical scenarios .
Outcome:	The proposed framework reduces gender bias while preserving or even enhancing general model capabilities.

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Wanqiang Wang, Longzhu He, Wei Zheng

SafeSwitch: Steering Unsafe LLM Behavior via Internal Activation Signals (2025.findings-emnlp)

Challenge:	Multiple choice questions (MCQs) are crucial for deep thinking and knowledge integration in education.
Approach:	They propose a cross-modal options synthesis framework for generating MCQs with visual options.
Outcome:	The proposed framework produces a plausible and visually similar answer and distractor . it also includes a discrimination module to identify content suitable for visual options .

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Peixuan Han, Cheng Qian, Xiusi Chen, Yuji Zhang, Heng Ji, Denghui Zhang

MADD: Multi-Agent Drug Discovery Orchestra (2025.findings-emnlp)

Challenge:	Existing safety mechanisms for large language models (LLMs) are inadequate to fully leverage their internal cognitive processes.
Approach:	They propose a framework that regulates unsafe outputs by utilizing the prober-based internal state monitor that actively detects harmful intentions.
Outcome:	The proposed framework reduces harmful outputs by approximately 80% while maintaining strong utility.

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Gleb Vitalevich Solovev, Alina Borisovna Zhidkovskaya, Anastasia Orlova, Nina Gubina, Anastasia Vepreva, Rodion Golovinskii, Ilya Tonkii, Ivan Dubrovsky, Ivan Gurev, Dmitry Gilemkhanov, Denis Chistiakov, Timur A. Aliev, Ivan Poddiakov, Galina Zubkova, Ekaterina V. Skorb, Vladimir Vinogradov, Alexander Boukhanovsky, Nikolay Nikitin, Andrei Dmitrenko, Anna Kalyuzhnaya, Andrey Savchenko

Challenge:	Recent advances in artificial intelligence have limited access to wet-lab tools for hit identification . multi-agent systems combine interpretability of LLMs with precision of specialized models and tools .
Approach:	They propose a multi-agent system that builds and executes customized hit identification pipelines from natural language queries.
Outcome:	The proposed system reduces the complexity of traditional screening methods and improves efficiency.

PersonaGym: Evaluating Persona Agents and LLMs (2025.findings-emnlp)

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Vinay Samuel, Henry Peng Zou, Yue Zhou, Shreyas Chaudhari, Ashwin Kalyan, Tanmay Rajpurohit, Ameet Deshpande, Karthik R Narasimhan, Vishvak Murahari

Challenge:	Persona agents are LLM agents conditioned to act according to an assigned persona . evaluating how faithfully these agents adhere to their personas remains a challenge .
Approach:	a new study evaluates persona agents' ability to act according to an assigned persona . a persona agent's person score is a human-aligned automatic metric that can be used to evaluate a model .
Outcome:	a new evaluation framework and a human-aligned automatic metric show that persona agents can perform better.

LM2Protein: A Structure-to-Token Protein Large Language Model (2025.findings-emnlp)

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Chang Zhou, Yuheng Shan, Pengan Chen, Xiangyu Shi, Zikang Wang, Yanting Li, Jiyue Jiang

How Well Can Reasoning Models Identify and Recover from Unhelpful Thoughts? (2025.findings-emnlp)

Challenge:	RNA-binding proteins are critical for various molecular functions, relying on their precise tertiary structures.
Approach:	They propose a method to integrate protein 3D structural data within a sequence processing framework.
Outcome:	The proposed method achieves high sequence recovery in inverse folding and protein-conditioned RNA design.

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Sohee Yang, Sang-Woo Lee, Nora Kassner, Daniela Gottesman, Sebastian Riedel, Mor Geva

From Token to Action: State Machine Reasoning to Mitigate Overthinking in Information Retrieval (2025.findings-emnlp)

Challenge:	Recent reasoning models show the ability to reflect, backtrack, and self-validate their reasoning .
Approach:	They investigate how well models identify and recover from unhelpful thoughts . they show that models are effective at identifying most unhelpfully thoughts - but struggle to recover from the same thoughts when they are injected into their thinking process .
Outcome:	The proposed model can identify unhelpful thoughts but struggle to recover from them . larger models struggle more than smaller models to recover short irrelevant thoughts .

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Dohyeon Lee, Yeonseok Jeong, Seung-won Hwang

Locate-then-Merge: Neuron-Level Parameter Fusion for Mitigating Catastrophic Forgetting in Multimodal LLMs (2025.findings-emnlp)

Challenge:	Chain-of-Thought (CoT) prompting often leads to overthinking in large language models . redundant trajectories that revisit similar states and misguided reasoning that diverges from user intent are two key challenges in information retrieval.
Approach:	They propose a transition-based reasoning framework that supports early stopping and fine-grained control.
Outcome:	The proposed framework improves retrieval performance by 3.4% while reducing token usage by 74.4%.

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Zeping Yu, Sophia Ananiadou

Improving Influence-based Instruction Tuning Data Selection for Balanced Learning of Diverse Capabilities (2025.findings-emnlp)

Challenge:	Multimodal instruction tuning often causes catastrophic forgetting of the base LLM’s language ability, even in strong models like Llama3.
Approach:	They propose a training-free parameter fusion framework that locates important parameters and selectively merges them.
Outcome:	The proposed framework preserves the influence of neurons with large parameter shifts while attenuating the influence . of neurons likely responsible for newly acquired visual capabilities while mitigating language degradation.

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Qirun Dai, Dylan Zhang, Jiaqi W. Ma, Hao Peng

Diagnosing Moral Reasoning Acquisition in Language Models: Pragmatics and Generalization (2025.findings-emnlp)

Challenge:	Influence-based methods show promise in achieving (1), but often struggle with (2) . data selection is often biased towards high-influence tasks, harming performance on them .
Approach:	They propose a Balanced and Influential Data Selection algorithm that normalizes influence scores of training data and iteratively chooses the training example with the highest influence on the most underrepresented task.
Outcome:	The proposed model outperforms both state-of-the-art influence-based methods and non-influence-based frameworks on seven benchmarks spanning five diverse capabilities.

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Guangliang Liu, Zimo Qi, Xitong Zhang, Lei Jiang, Kristen Johnson

Discourse Heuristics For Paradoxically Moral Self-Correction (2025.findings-emnlp)

Challenge:	Prior research has shown that LLMs fail to perform satisfactorily on moral cognizance tasks .
Approach:	They propose to use curated datasets to improve LLMs' moral cognizance . they find pragmatic dilemma constrains generalization ability of current learning paradigms .
Outcome:	The proposed learning paradigms fail to perform on moral cognizance tasks, the authors show . they show that the pragmatic dilemma is the primary bottleneck for moral reasoning acquisition .

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Guangliang Liu, Zimo Qi, Xitong Zhang, Kristen Johnson

Invisible Prompts, Visible Threats: Malicious Font Injection in External Resources for Large Language Models (2025.findings-emnlp)

Challenge:	moral self-correction is a promising approach for aligning output of Large Language Models with human moral values . authors show that moral self correction relies on discourse constructions that reflect heuristic shortcuts .
Approach:	a new method is proposed to strengthen moral self-correction using heuristics extracted from curated datasets.
Outcome:	a new method to strengthen moral self-correction is proposed . the proposed method is based on heuristics extracted from curated datasets.

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Junjie Xiong, Changjia Zhu, Shuhang Lin, Chong Zhang, Yongfeng Zhang, Yao Liu, Lingyao Li

Turning the Tide: Repository-based Code Reflection (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) are increasingly equipped with capabilities of real-time web search and integrated with protocols like the Model Context Protocol (MCP).
Approach:	They investigate the vulnerability of Large Language Models to hidden adversarial prompts . they evaluate two critical attack scenarios: malicious content relay and sensitive data leakage .
Outcome:	The proposed extension could introduce new security vulnerabilities.

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Wei Zhang, Jian Yang, Jiaxi Yang, Ya Wang, Zhoujun Li, Zeyu Cui, Binyuan Hui, Junyang Lin

Reinforcement Learning with Supervised Alignment (2025.findings-emnlp)

Challenge:	Code large language models (LLMs) enhance programming by understanding and generating code across languages.
Approach:	a new benchmark evaluates code understanding and generation in repositories using code large language models.
Outcome:	The proposed model improves code understanding and generation in repositories by evaluating 1,888 test cases across 6 programming languages.

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João Luís Lins, Jia Xu

EmByte: Decomposition and Compression Learning for Small yet Private NLP (2025.findings-emnlp)

Challenge:	Supervised fine-tuning (SFT) is a widely used method for adapting Large Language Models to specific tasks.
Approach:	They propose a method that uses supervised fine-tuning to train a reward model for reinforcement learning.
Outcome:	The proposed method outperforms existing methods on in-domain benchmarks but surpasses them 50 times on out-of-domain and cross-task evaluations.

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Shenglan Li, Jia Xu, Mengjiao Zhang

GUARD: Glocal Uncertainty-Aware Robust Decoding for Effective and Efficient Open-Ended Text Generation (2025.findings-emnlp)

Challenge:	EMBYTE is a byte-level tokenization model that reduces embedding parameters by up to 94% . it is also resilient to privacy threats such as gradient inversion attacks .
Approach:	EMBYTE is a byte-level tokenization model that decomposes subwords into fine-grained byte embeddings and then compresses them via neural projection.
Outcome:	EMBYTE achieves substantial embedding compression while preserving accuracy and enhancing privacy.

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Yuanhao Ding, Esteban Garces Arias, Meimingwei Li, Julian Rodemann, Matthias Aßenmacher, Danlu Chen, Gaojuan Fan, Christian Heumann, Chongsheng Zhang

Efficiently Editing Mixture-of-Experts Models with Compressed Experts (2025.findings-emnlp)

Challenge:	GUARD is a self-adaptive decoding method that balances coherence with diversity in open-ended text generation.
Approach:	They propose a self-adaptive decoding method that balances coherence and diversity . they combine global entropy estimates with local entropic deviations to integrate uncertainty .
Outcome:	GUARD achieves a good balance between diversity and coherence while exhibiting significant improvements in generation speed.

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Yifei He, Yang Liu, Chen Liang, Hany Hassan Awadalla

FinGEAR: Financial Mapping-Guided Enhanced Answer Retrieval (2025.findings-emnlp)

Challenge:	Mixture-of-Experts models allow for efficient scaling of large language models . fewer experts reduce computational costs, while more experts improve performance .
Approach:	They propose to activate only a subset of experts during training and inference . they propose compressed experts that preserve the most important experts .
Outcome:	The proposed approach preserves the most important experts while replacing other auxiliary activated experts with compressed experts.

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Ying Li, Mengyu Wang, Miguel de Carvalho, Sotirios Sabanis, Tiejun Ma

FM2DS: Few-Shot Multimodal Multihop Data Synthesis with Knowledge Distillation for Question Answering (2025.findings-emnlp)

Challenge:	FinGEAR provides a retrieval framework tailored to financial documents . standard retrieval-augmented generation models underuse financial disclosures .
Approach:	FinGEAR combines a finance lexicon for Item-level guidance and hierarchical indices for within-Item search.
Outcome:	FinGEAR improves accuracy and accuracy on 10-Ks with a FinQA dataset.

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Amirhossein Abaskohi, Spandana Gella, Giuseppe Carenini, Issam H. Laradji

SQUARE: Unsupervised Retrieval Adaptation via Synthetic Data (2025.findings-emnlp)

Challenge:	Existing methods focus on single-hop, single-modality, or short texts, limiting real-world applications . despite advances in visual question answering, this multihop setting remains underexplored due to a lack of quality datasets.
Approach:	They propose a framework for creating a high-quality dataset for multimodal multihop question answering . they use a 5-stage pipeline to acquire relevant multimodal documents from Wikipedia .
Outcome:	The proposed framework outperforms existing methods on multimodal multihop question answering datasets.

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Jinsung Yoon, Junhao Zeng, Sercan O Arik

Knowledge-enhanced Multimodal ECG Representation Learning with Arbitrary-Lead Inputs (2025.findings-emnlp)

Challenge:	Pre-trained retrieval models often face challenges in zero-shot retrieval for knowledge-based question answering .
Approach:	SQUARE is a method for corpus-specific unsupervised retrieval customization . it generates synthetic question-answer pairs from the corpus and fine-tunes it .
Outcome:	SQUARE is a new method for corpus-specific unsupervised retrieval customization.

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Che Liu, Cheng Ouyang, Zhongwei Wan, Haozhe Wang, Wenjia Bai, Rossella Arcucci

Seeing Race, Feeling Bias: Emotion Stereotyping in Multimodal Language Models (2025.findings-emnlp)

Challenge:	Current methods for multimodal representation learning for electrocardiograms often result in suboptimal alignment of ECG signals with their corresponding text reports.
Approach:	They propose a framework to learn ECG representations by aligning ECG signals with paired free-text reports.
Outcome:	The proposed framework outperforms existing methods in zero-shot classification and linear probing tasks using 12 leads.

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Mahammed Kamruzzaman, Amanda Cercas Curry, Alba Cercas Curry, Flor Miriam Plaza-del-Arco

AdaptMerge: Inference Time Adaptive Visual and Language-Guided Token Merging for Efficient Large Multimodal Models (2025.findings-emnlp)

Challenge:	Emotion stereotypes are also tightly tied to race and skin tone, but previous studies have overlooked this dimension.
Approach:	They propose a multimodal study of racial, gender, and skin-tone bias in emotion attribution . they evaluate four open-source MLLMs using 2.1K emotion-related events .
Outcome:	The proposed study examines four open-source MLLMs using 2.1K emotion-related events paired with 400 neutral face images across three different prompt strategies.

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Zahidul Islam, Mrigank Rochan

Federated Retrieval-Augmented Generation: A Systematic Mapping Study (2025.findings-emnlp)

Challenge:	Existing token reduction methods ignore image complexity and vision-language interactions, ignoring image complexity.
Approach:	They propose a training-free, inference-time token merging strategy that adaptively reduces visual tokens by leveraging feature diversity and language-guided relevance.
Outcome:	The proposed approach outperforms state-of-the-art token reduction methods on Google’s Gemma 3 models while achieving reduced computational costs and improved performance.

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Abhijit Chakraborty, Chahana Dahal, Vivek Gupta

A Survey of Pun Generation: Datasets, Evaluations and Methodologies (2025.findings-emnlp)

Challenge:	Federated Retrieval-Augmented Generation (Federated RAG) combines Federated Learning (FL) with Retrieleval-augment Generation (RAG)
Approach:	They propose to map literature on Federated Retrieval-Augmented Generation (Federated RAG) this mapping study examines architectural patterns, temporal trends, and key challenges .
Outcome:	The proposed framework improves the factual accuracy of language models by grounding outputs in external knowledge.

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Yuchen Su, Yonghua Zhu, Ruofan Wang, Zijian Huang, Diana Benavides-Prado, Michael J. Witbrock

Evaluating the Robustness and Accuracy of Text Watermarking Under Real-World Cross-Lingual Manipulations (2025.findings-emnlp)

Challenge:	Pun generation aims to modify linguistic elements in text to produce humour or evoke double meanings.
Approach:	They propose to review pun generation datasets and methods across different stages . pun generation aims to produce humour or evoke double meanings .
Outcome:	This paper summarises both automated and human evaluation metrics used to assess the quality of pun generation.

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Mansour Al Ghanim, Jiaqi Xue, Rochana Prih Hastuti, Mengxin Zheng, Yan Solihin, Qian Lou

Cross-Lingual Transfer

Challenge:	a study focuses on evaluating watermarking methods for the English language . the literature for evaluating cross-lingual watermarks is scarce .
Approach:	They evaluate representative watermarking methods in four different languages . they examine the quality of text under different watermark procedures .
Outcome:	The proposed method is compared with other evaluation methods in four different languages.

HDiff: Confidence-Guided Denoising Diffusion for Robust Hyper-relational Link Prediction (2025.findings-emnlp)

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Xiangfeng Luo, Ruoxin Zheng, Jianqiang Huang, Hang Yu

Spotlighter: Revisiting Prompt Tuning from a Representative Mining View (2025.findings-emnlp)

Challenge:	Hyper-relational Knowledge Graph Completion (HKGC) is more sensitive to inherent noise, particularly struggling with two prevalent HKG-specific noise types: Intra-fact Inconsistency and Cross-fact Association Noise.
Approach:	They propose a conditional denoising diffusion framework that learns to reverse structured noise corruption.
Outcome:	The proposed framework outperforms state-of-the-art HKGC methods in a variety of noisy conditions.

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Yutong Gao, Maoyuan Shao, Xinyang Huang, Chuang Zhu, Yu Weng, Xuan Liu, Lijuan Sun, Guoshun Nan

Offloaded Reasoning: Efficient Inference for Large Language Models via Modular Reasoning and Refinement (2025.findings-emnlp)

Challenge:	Spotlighter is a lightweight token-selection framework that enhances accuracy and efficiency in prompt tuning.
Approach:	They propose a token-selection framework that enhances accuracy and efficiency in prompt tuning by preserving only the top-scoring tokens for downstream prediction.
Outcome:	The proposed framework outperforms CLIP by up to 11.19% in harmonic mean accuracy and achieves 0.8K additional FPS, with only 21 extra parameters.

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Ishan Jindal, Jayant Taneja, Badrinath Chandana, Vikas Kapur, Sachin Dev Sharma

Wait, We Don’t Need to “Wait”! Removing Thinking Tokens Improves Reasoning Efficiency (2025.findings-emnlp)

Challenge:	Large language models (LLMs) demonstrate strong reasoning capabilities but are expensive to run at inference time, limiting their practical deployment.
Approach:	They propose Offloaded Reasoning, a modular strategy where a lightweight model generates intermediate reasoning traces that are then used by a larger model to produce the final answer.
Outcome:	The proposed approach achieves faster inferences than full large-model reasoning with minimal accuracy loss while recovering or exceeding full accuracy at substantially lower cost.

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Chenlong Wang, Yuanning Feng, Dongping Chen, Zhaoyang Chu, Ranjay Krishna, Tianyi Zhou

Towards Reverse Engineering of Language Models: A Survey (2025.findings-emnlp)

Challenge:	Recent advances in large reasoning models often introduce significant overthinking . this leads to verbose and redundant outputs that hinder efficiency.
Approach:	They propose a plug-and-play solution that disables explicit self-reflection . it suppresses tokens such as "Wait" and "Hmm" during inference .
Outcome:	The proposed approach reduces chain-of-thought trajectory length by up to 27%–51% in five R1-style model series without compromising model utility.

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Xinpeng Ti, Wentao Ye, Zhifang Zhang, Junbo Zhao, Chang Yao, Lei Feng, Haobo Wang

LIFTED: Multimodal Clinical Trial Outcome Prediction via Large Language Models and Mixture-of-Experts (2025.findings-emnlp)

Challenge:	Due to the vast amounts of data and computational resources required for model development, protecting the model’s parameters and training data has become an urgent and crucial concern.
Approach:	They define "reverse engineering" techniques as attacks on large language models and provide an in-depth analysis of them.
Outcome:	The proposed attacks are described as “reverse engineering” techniques on LMs and provide an introduction to existing protective strategies.

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Wenhao Zheng, Liaoyaqi Wang, Dongshen Peng, Hongxia Xu, Yun Li, Hongtu Zhu, Tianfan Fu, Huaxiu Yao

Addition in Four Movements: Mapping Layer-wise Information Trajectories in LLMs (2025.findings-emnlp)

Challenge:	Clinical trials are costly and pivotal processes that require substantial expenses . a new approach to integrate multimodal data for clinical outcome prediction is needed .
Approach:	a proposed framework transforms modality-specific data into natural language descriptions . a sparse Mixture-of-Experts mechanism then identifies shared patterns across modalities .
Outcome:	a proposed framework outperforms baseline methods in predicting clinical trial outcomes . it transforms modality-specific data into natural language descriptions, encoded via unified encoders .

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Yao Yan

Challenge:	arithmetic offers a compact test of whether large language models compute or memorize.
Approach:	They study multi-digit addition in LLaMA-3-8B-Instruct using linear probes and the Logit Lens . they find a consistent four-stage, layer-wise ordering of probe-decodable signal types across depth .
Outcome:	The proposed method shows that multi-digit addition is linearly decodable with high accuracy across layers.

CoMoE: Contrastive Representation for Mixture-of-Experts in Parameter-Efficient Fine-tuning (2025.findings-emnlp)

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Jinyuan Feng, ChaoPeng Wei, Tenghai Qiu, Tianyi Hu, Zhiqiang Pu

GuiLoMo: Allocating Experts and Ranks for LoRA-MoE via Bilevel Optimization with GuidedSelection Vectors (2025.findings-emnlp)

Challenge:	Currently, mixture-of-experts (MoE) is underutilized on heterogeneous datasets, ignoring the fact that experts may learn similar knowledge.
Approach:	They propose a method to promote modularization and specialization in MoE by specializing functionalities into different experts and sparsely activating them appropriately.
Outcome:	The proposed method improves the capacity and specialization of mixture-of-experts (MoE) by sampling from activated and inactivated experts in top-k routing.

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Xinrong Chen, Hengyuan Zhang, Yingmin Qiu, Xiao Liang, Ziyue Li, Guanyu Wang, Weiping Li, Tong Mo, Hayden Kwok-Hay So, Ngai Wong

Rotate, Clip, and Partition: Towards W2A4KV4 Quantization by Integrating Rotation and Learnable Non-uniform Quantizer (2025.findings-emnlp)

Challenge:	Low-Rank Adaptation (LoRA) methods are efficient for a large language model with reduced computational costs.
Approach:	They propose a layer-wise expert numbers and ranks allocation strategy with GuidedSelection Vectors.
Outcome:	The proposed method achieves superior or comparable performance to all baselines on three backbone models.

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Euntae Choi, Sumin Song, Woosang Lim, Sungjoo Yoo

Challenge:	RCP can compress LLaMA-2-7B to W2A4KV4 with a loss of only 2.84 WikiText2 PPL and 5.29 times reduced memory footprint.
Approach:	They propose a Quantization-Aware Training approach that uses extreme compression of LLMs . they propose 'rotate, clip, and partition' to learn non-uniform intervals and a GPU kernel supporting GEMV .
Outcome:	The proposed method can compress LLaMA-2-7B to W2A4KV4 with a loss of only 2.84 WikiText2 PPL and 5.29 times reduced memory footprint.

Decoding in Latent Spaces for Efficient Inference in LLM-based Recommendation (2025.findings-emnlp)

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Chengbing Wang, Yang Zhang, Zhicheng Wang, Tianhao Shi, Keqin Bao, Fuli Feng, Tat-Seng Chua

Forget for Get: A Lightweight Two-phase Gradient Method for Knowledge Editing in Large Language Models (2025.findings-emnlp)

Challenge:	Light Latent-space Decoding (L2D) is an efficient and efficient latent- space decoding method.
Approach:	They propose to bypass language-space decoding by matching candidate items with LLM's internal thought representations in the latent space.
Outcome:	The proposed method is 10x faster than language-space decoding while maintaining or enhancing performance.

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Yanhong Li, Min Yang, Xiping Hu, Chengming Li

AutoEvolve: Automatically Evolving Queries for Applicable and Scalable Retrieval-Augmented Generation Benchmarking (2025.findings-emnlp)

Challenge:	Existing knowledge editing methodologies often encounter parameter conflict during knowledge overwriting and excessive computational overhead.
Approach:	They propose a method that erases outdated knowledge and inserts new knowledge at the location that corresponds to the target knowledge.
Outcome:	The proposed method achieves more effective knowledge editing at a lower cost compared to previous methods across various base models.

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Ding-Chu Zhang, Xiaowen Zhang, Yue Fei, Renjun Hu, Xiao-Wen Yang, Zhi Zhou, Baixuan Li, Yu-Feng Li, Xing Shi, Wei Lin

Temporal Alignment of Time Sensitive Facts with Activation Engineering (2025.findings-emnlp)

Challenge:	Existing automated generation methods exhibit Weak Applicability and Weak Scalability . existing methods are limited by their reliance on metadata from specific corpora .
Approach:	They propose an approach to generate scalable RAG benchmarks using corpus-agnostic methods . they propose a difficulty-guided metric that directs query evolution process .
Outcome:	The proposed approach evolves queries significantly more challenging than existing methods . it is able to dynamically increase difficulty, limiting scalability of the query .

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Sanjay Govindan, Maurice Pagnucco, Yang Song

ChronoBias: A Benchmark for Evaluating Temporal Group Bias in the Time-sensitive Knowledge of Large Language Models (2025.findings-emnlp)

Challenge:	Large Language Models are trained on diverse and conflicting knowledge spanning multiple domains and time periods.
Approach:	They propose a method for temporally aligning large language models to improve factual recall without training.
Outcome:	The proposed method improves factual recall without training.

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Kyungmin Kim, Youngbin Choi, Hyounghun Kim, Dongwoo Kim, Sangdon Park

MC2: A Minimum-Coverage and Dataset-Agnostic Framework for Compositional Generalization of LLMs on Semantic Parsing (2025.findings-emnlp)

Challenge:	Using a template-based semi-automated generation method, we evaluate time-conditional group bias in time-sensitive knowledge of large language models (LLMs).
Approach:	They propose a template-based semi-automated generation method to construct a time-conditional group bias benchmark.
Outcome:	The proposed method balancing quality-quantity trade-off in existing benchmark curation approaches.

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Ziyao Xu, Zhe Yang, Houfeng Wang

Learning to Instruct: Fine-Tuning a Task-Aware Instruction Optimizer for Black-Box LLMs (2025.findings-emnlp)

Challenge:	Existing research relies on dataset-specific designs or a large number of samples to improve compositional generalization of large language models (LLMs) .
Approach:	They propose a minimum-coverage framework that can help LLMs achieve compositional generalization by selecting and organizing samples that satisfy the primitive coverage.
Outcome:	The proposed framework can improve compositional generalization on different parsing datasets in the minimum-coverage setting.

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Yunzhe Qi, Jinjin Tian, Tianci Liu, Ruirui Li, Tianxin Wei, Hui Liu, Xianfeng Tang, Monica Xiao Cheng, Jingrui He

Enriching Patent Claim Generation with European Patent Dataset (2025.findings-emnlp)

Challenge:	Learning to Instruct is a new paradigm for black-box LLMs with inaccessible internal states.
Approach:	They propose a new paradigm that formulates instruction optimization as an LLM fine-tuning objective for a white-box “instruction engineer” LLM.
Outcome:	The proposed framework outperforms strong baselines in performance and efficiency.

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Lekang Jiang, Chengzu Li, Stefan Goetz

Challenge:	Existing work on large language models to assist inventors in writing patent claims relies on datasets from the United States Patent and Trademark Office.
Approach:	They propose a European patent dataset that provides rich textual data and structured metadata to support multiple patent-related tasks.
Outcome:	The proposed dataset outperforms existing datasets and GPT-4o in claim quality and cross-domain generalization.

StepKE: Stepwise Knowledge Editing for Multi-Hop Question Answering (2025.findings-emnlp)

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Jaewook Lee, Dahyun Jung, Heuiseok Lim

AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark (2025.findings-emnlp)

Challenge:	Existing knowledge editing methods overlook interplay with pre-existing knowledge, leading to inconsistent edit propagation.
Approach:	stepKE integrates edited and existing knowledge for coherent multi-hop reasoning . stepKE decomposes multi-step questions into sequential single-hop sub-questions .
Outcome:	Experiments show that StepKE generates more accurate and consistent responses than baselines.

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Lan Li, Liri Fang, Bertram Ludäscher, Vetle I Torvik

Hidden Ghost Hand: Unveiling Backdoor Vulnerabilities in MLLM-Powered Mobile GUI Agents (2025.findings-emnlp)

Challenge:	Data cleaning is a time-consuming and error-prone manual process even with modern workflow tools like OpenRefine.
Approach:	AutoDCWorkflow generates a table with a data analysis purpose and generates an open-refine workflow.
Outcome:	The proposed pipeline generates clean, minimal tables for data analysis tasks.

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Pengzhou Cheng, Haowen Hu, Zheng Wu, Zongru Wu, Tianjie Ju, Daizong Ding, Zhuosheng Zhang, Gongshen Liu

Scale Down to Speed Up: Dynamic Data Selection for Reinforcement Learning (2025.findings-emnlp)

Challenge:	MLLM-powered GUI agents expose multiple interaction-level triggers, causing backdoor attacks . backdoor injection maximizes feature difference across sample classes, improving flexibility .
Approach:	They propose a framework for red-teaming backdoor attacks using MLLMs . they construct composite triggers by combining goal and interaction levels .
Outcome:	The proposed framework is effective and stealthy for red-teaming backdoor attacks.

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Zhuoyue Chen, Jihai Zhang, Ben Liu, Fangquan Lin, Wotao Yin

Towards Efficient CoT Distillation: Self-Guided Rationale Selector for Better Performance with Fewer Rationales (2025.findings-emnlp)

Challenge:	Current approaches to Reinforcement Learning (RL) rely on massive static datasets, leading to computational inefficiency and redundant gradient updates.
Approach:	They propose a data-centric RL framework that dynamically selects the most informative training samples to optimize RL for mathematical reasoning.
Outcome:	The proposed framework achieves comparable performance to full-data training methods while requiring only 1.5K samples instead of 220K, reducing training time from 13 days to just 4 hours on 8A800 GPUs.

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JianZhi Yan, Le Liu, Youcheng Pan, Shiwei Chen, Yang Xiang, Buzhou Tang

GeoDANO: Geometric VLM with Domain Agnostic Vision Encoder (2025.findings-emnlp)

Challenge:	Existing work on rationale quality underestimates the importance of CoT distillation, focusing primarily on data quantity, which may result in transferring noisy or incorrect information to the student model.
Approach:	They propose a method which can discern and select high quality rationales for distillation and a Rationale Difficulty metric to measure the ability of the student model to generate the correct answer under a given rationale.
Outcome:	The proposed method achieves 4.6% accuracy improvement over baseline data on seven datasets over three tasks, controlling accuracy, diversity, and difficulty.

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Seunghyuk Cho, Zhenyue Qin, Yang Liu, Youngbin Choi, Seungbeom Lee, Dongwoo Kim

Leveraging 3D Gaussian for Temporal Knowledge Graph Embedding (2025.findings-emnlp)

Challenge:	GeoDANO is a geometric vision-language model with a domain-agnostic vision encoder . it is currently limited to recognizing geometric features in general-purpose VLMs .
Approach:	They propose a geometric vision-language model with a domain-agnostic vision encoder for plane geometry problems.
Outcome:	The proposed model outperforms vision encoders in recognizing geometric features . it outperformed specialized methods for plane geometry problems and GPT-4o on MathVerse .

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Jiang Li, Xiangdong Su, Guanglai Gao

LLMAP: LLM-Assisted Multi-Objective Route Planning with User Preferences (2025.findings-emnlp)

Challenge:	Representation learning in knowledge graphs (KGs) has focused on static data, yet many real-world knowledge graph are inherently dynamic.
Approach:	They propose a temporal embedding method inspired by 3D Gaussian Splatting where entities, relations, and timestamps are modeled as 3D gaussian distributions with learnable structured covariance.
Outcome:	The proposed method outperforms state-of-the-art methods on three benchmark TKG datasets.

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Liangqi Yuan, Dong-Jun Han, Christopher Brinton, Sabine Brunswicker

ZEBRA: Leveraging Model-Behavioral Knowledge for Zero-Annotation Preference Dataset Construction (2025.findings-emnlp)

Challenge:	a recent study shows that large language models (LLMs) are limited in understanding natural language preferences.
Approach:	They propose a novel LLM-as-Parser-based route planning system that utilizes an LLM to comprehend natural language, extract user preferences and recognize task dependencies.
Outcome:	The proposed system achieves superior performance with guarantees across multiple constraints.

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Jeesu Jung, Chanjun Park, Sangkeun Jung

Challenge:	Recent efforts in LLM alignment focus on instance-wise supervision, costing substantial . ZEBRA binarizes response pairs by evaluating the quality and similarity of their origin models .
Approach:	They propose a model behavior-wise zero-annotation framework that binarizes preference data . ZEBRA binarized response pairs by evaluating the quality and similarity of their origin models .
Outcome:	The proposed framework achieves comparable alignment performance to instance-supervised methods .

Token Knowledge: A New Perspective For Knowledge in Large Language Models (2025.findings-emnlp)

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Jieyong Wang, Chunyao Song, Tingjian Ge

Adaptive Schema-aware Event Extraction with Retrieval-Augmented Generation (2025.findings-emnlp)

Challenge:	Predicting the presence and absence of certain knowledge in large language models could aid hallucination avoidance.
Approach:	They propose a token knowledge dataset construction method and use the intermediate states during inference to train probes.
Outcome:	The proposed method increases the model's latent potential by 60% to 90% with strong out-of-distribution generalization by training on just a few dozen prompts.

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Sheng Liang, Hang Lv, Zhihao Wen, Yaxiong Wu, Yongyue Zhang, Hao Wang, Yong Liu

Enhancing Attributed Question Answering using Tailored Progressive Curriculum Learning (2025.findings-emnlp)

Challenge:	Event extraction is a task in natural language processing that involves identifying and extracting event information from unstructured text.
Approach:	They propose a paradigm that combines schema paraphrasing with schema retrieval-augmented generation.
Outcome:	The proposed paradigm retrieves paraphrased schemas and accurately generates targeted structures.

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Yuhan Chen, Bowei Zou, Yifan Fan, Yuchong Chen, Shujun Cao, Yu Hong

REAR: Reinforced Reasoning Optimization for Event Argument Extraction with Relation-Aware Support (2025.findings-emnlp)

Challenge:	Attributed Question Answering models are not yet leveraged to enhance their essential capabilities, including evidence identification, cross-source relation recognition and anti-distraction reasoning.
Approach:	They propose a progressive progressive curriculum learning approach that optimizes both encoder-decoder and decoder-only AQA models.
Outcome:	The proposed approach improves both encoder-decoder and decoder-only AQA models on the quotesum benchmark.

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Jianwen Luo, Yu Hong, Shuai Yang, Jianmin Yao

COMI-LINGUA: Expert Annotated Large-Scale Dataset for Multitask NLP in Hindi-English Code-Mixing (2025.findings-emnlp)

Challenge:	Existing methods for EAE restrict integration of relation-level semantics, thereby overlooking the complementary cues from RE.
Approach:	They propose a Relation-aware EAE Reinforced optimization framework that integrates relation-level cues from RE into the Large Language Model (LLM)
Outcome:	The proposed framework surpasses existing decoder-only methods on the ACE-E, ACE+ and ERE benchmarks.

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Rajvee Sheth, Himanshu Beniwal, Mayank Singh

Nine Ways to Break Copyright Law and Why Our LLM Won’t: A Fair Use Aligned Generation Framework (2025.findings-emnlp)

Challenge:	COMI-LINGUA is the largest manually annotated Hindi-English code-mixed dataset . 125K+ high-quality instances across five core NLP tasks are annotating by three bilingual annotators .
Approach:	COMI-LINGUA is the largest manually annotated Hindi-English code-mixed dataset . 125K+ high-quality instances are annotating by three bilingual annotators .
Outcome:	The dataset covers five core NLP tasks, including Token-level Language Identification, Matrix Language Identification and Named Entity Recognition.

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Aakash Sen Sharma, Debdeep Sanyal, Priyansh Srivastava, Sundar Athreya H, Shirish Karande, Mohan Kankanhalli, Murari Mandal

InteractSpeech: A Speech Dialogue Interaction Corpus for Spoken Dialogue Model (2025.findings-emnlp)

Challenge:	Large language models (LLMs) often risk copyright infringement by reproducing protected content verbatim or with insufficient transformative modifications.
Approach:	They propose a legally-grounded framework to align LLM outputs with fair-use doctrine . LAW-LM uses a dataset containing 18,000 expert-validated examples .
Outcome:	The proposed framework aligns outputs with fair-use doctrine and is validated by 18,000 experts.

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Yifu Chen, Shengpeng Ji, Ziqing Wang, Hanting Wang, Zhou Zhao

Enhancing SQL Table Acquisition with Reverse Engineering for Text-to-SQL (2025.findings-emnlp)

Challenge:	Spoken Dialogue models face challenges in handling nuanced interactional phenomena, such as interruptions and backchannels.
Approach:	They propose to use a 150-hour English speech interaction dialogue dataset to empower spoken dialogue models with nuanced real-time interaction capabilities.
Outcome:	The proposed dataset trains and evaluates a speech understanding model that classifies key interactional events directly from audio.

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Shixin Liu, Haoyu Xu, Yu Hong

DynamicKV: Task-Aware Adaptive KV Cache Compression for Long Context LLMs (2025.findings-emnlp)

Challenge:	Text-to-SQL oriented table acquisition suffers from heterogeneous semantic gap.
Approach:	They propose a Reverse Engineering based table acquisition approach that reversely generates potentially-matched questions conditioned on table schemas instead of forward table search using queries.
Outcome:	The proposed approach achieves competitive performance on two benchmarks, including SpiderUnion and BirdUnion.

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Xiabin Zhou, Wenbin Wang, Minyan Zeng, Jiaxian Guo, Xuebo Liu, Li Shen, Min Zhang, Liang Ding

Challenge:	Existing KV cache compression methods enforce a fixed pattern, neglecting task-specific characteristics, which hampers the effective retention of essential information while discarding less important tokens.
Approach:	They propose a Task-Aware KV cache mechanism that dynamically adjusts the KV caching size across different layers based on the characteristics of the tasks.
Outcome:	The proposed method surpasses state-of-the-art methods by 11% on the LongBench dataset even under extreme compression (0.9%)

ASD-iLLM:An Intervention Large Language Model for Autistic Children based on Real Clinical Dialogue Intervention Dataset (2025.findings-emnlp)

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Shuzhong Lai, Chenxi Li, Junhong Lai, Yucun Zhong, Chenyu Yan, Xiang Li, Haifeng Li, Gang Pan, Lin Yao, Yueming Wang

GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction (2025.findings-emnlp)

Challenge:	Currently, leveraging large language models (LLMs) for autism intervention is a significant yet challenging task, especially when directly employing LLMs as an intervention doctor.
Approach:	They propose a framework for training LLMs to conduct dialogue interventions in accordance with the principles of Applied Behavior Analysis (ABA) they also propose 'role-play' strategy in which LLM act as autistic children to comprehensively evaluate the doctor model's capabilities at the dialogue level.
Outcome:	The proposed framework outperforms existing models in both automatic and human evaluation, with intervention strategies and dialogue style more closely resembling those of clinical intervention doctors.

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Jie Zhao, Wanting Ning, Yuxiao Fei, Yubo Feng, Lishuang Li

More Tokens, Lower Precision: Towards the Optimal Token-Precision Trade-off in KV Cache Compression (2025.findings-emnlp)

Challenge:	Existing approaches to analyzing large language models are limited by their pre-trained knowledge of Small Language Models(SLMs).
Approach:	They propose a global distance-aware modeling approach based on Large Language Models (LLMs) they first present a distance-based graph structure utilizing Graph Attention Network(GAT)
Outcome:	The proposed approach significantly improves minority relation class learning ability on two publicly available datasets, TB-Dense and MATRES.

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Jiebin Zhang, Dawei Zhu, Yifan Song, Wenhao Wu, Chuqiao Kuang, Xiaoguang Li, Lifeng Shang, Qun Liu, Sujian Li

Challenge:	storing more tokens in the KV cache at lower precision can enhance the long-context performance of large language models.
Approach:	They propose a token-precision trade-off strategy to optimize KV cache compression . they also propose storing more tokens in the KV at lower precision .
Outcome:	The proposed method achieves an optimal point within the Information Bottleneck compared to standalone KV pruning or KV quantization.

cAST: Enhancing Code Retrieval-Augmented Generation with Structural Chunking via Abstract Syntax Tree (2025.findings-emnlp)

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Yilin Zhang, Xinran Zhao, Zora Zhiruo Wang, Chenyang Yang, Jiayi Wei, Tongshuang Wu

A Group Fairness Lens for Large Language Models (2025.findings-emnlp)

Challenge:	Existing line-based chunking heuristics often break semantic structures, splitting functions or merging unrelated code.
Approach:	They propose a structure-aware method that breaks large AST nodes into smaller chunks . this method generates self-contained, semantically coherent units across programming languages .
Outcome:	The proposed method boosts Recall@5 by 4.3 points on RepoEval retrieval and Pass@1 by 2.67 points on SWE-bench generation.

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Guanqun Bi, Yuqiang Xie, Lei Shen, Yanan Cao

VLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training (2025.findings-emnlp)

Challenge:	Existing methods focusing on a few groups lack a comprehensive categorical perspective to evaluate LLMs’ potential biases and unfairness.
Approach:	They propose to evaluate LLM biases from a group fairness lens using a hierarchical schema characterizing diverse social groups.
Outcome:	The proposed method mitigates biases in LLMs from a group fairness lens and encapsulates target-attribute combinations across multiple dimensions.

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Zhanpeng Chen, Chengjin Xu, Yiyan Qi, Xuhui Jiang, Jian Guo

Rethinking DPO: The Role of Rejected Responses in Preference Misalignment (2025.findings-emnlp)

Challenge:	a significant drawback of Vision-language Models is their reliance on static training data, leading to outdated information and limited contextual awareness.
Approach:	They propose a framework with knowledge-enhanced reranking and noise-injected training to improve the VLM's ranking ability.
Outcome:	The proposed framework is based on a simple yet effective instruction template and is able to induce its ranking ability and serve it as a reranker to precisely filter the top-k retrieved images.

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Jae Hyeon Cho, JunHyeok Oh, Myunsoo Kim, Byung-Jun Lee

Challenge:	Existing methods that optimize for preferred responses struggle to meet primary objectives . rejected responses dominate the loss function, leading to suboptimal performance .
Approach:	They propose a method that bounds the influence of rejected responses while maintaining the original structure of DPO.
Outcome:	The proposed method outperforms existing algorithms in promoting preferred responses.

Enhancing Recommendation Explanations through User-Centric Refinement (2025.findings-emnlp)

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Jingsen Zhang, Zihang Tian, Xueyang Feng, Xu Chen, Chong Chen

Distributional Surgery for Language Model Activations (2025.findings-emnlp)

Challenge:	Existing explanations for user reviews often fail to meet user-centric aspects, reducing their usefulness to users.
Approach:	They propose a paradigm that refines initial explanations generated by existing models during the inference stage to enhance their quality in multiple aspects.
Outcome:	The proposed model improves explanations generated by existing models during the inference stage to enhance their quality in multiple aspects.

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Bao Nguyen, Binh Nguyen, Duy Nguyen, Viet Anh Nguyen

Improving Alignment in LVLMs with Debiased Self-Judgment (2025.findings-emnlp)

Challenge:	Language models can produce undesirable outputs including harmful or toxic outputs.
Approach:	They propose a method to detect undesirable content using activations . they propose layerwise distributional steering policies that transform the attention heads .
Outcome:	The proposed method outperforms baselines in reducing undesirable output generation.

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Sihan Yang, Chenhang Cui, Zihao Zhao, Yiyang Zhou, Weilong Yan, Ying Wei, Huaxiu Yao

Low-Confidence Gold: Refining Low-Confidence Samples for Efficient Instruction Tuning (2025.findings-emnlp)

Challenge:	Existing methods for aligning LVLMs rely on external datasets, human annotations or complex post-processing.
Approach:	They propose a method that generates a debiased self-judgment score for LVLMs . this self-evaluation metric is created internally by the model without external resources .
Outcome:	The proposed approach outperforms existing methods in reducing hallucinations and safety concerns.

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Hongyi Cai, Jie Li, Mohammad Mahdinur Rahman, Wenzhen Dong

Safeguarding Privacy of Retrieval Data against Membership Inference Attacks: Is This Query Too Close to Home? (2025.findings-emnlp)

Challenge:	Low-Confidence Gold (LCG) is a new filtering framework for Large Language Models that curates high-quality subsets while preserving data diversity.
Approach:	They propose a new filtering framework that employs centroid-based clustering and confidence-guided selection for identifying valuable instruction pairs.
Outcome:	The proposed framework improves performance on a subset of 6K samples while maintaining data diversity.

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Yujin Choi, Youngjoo Park, Junyoung Byun, Jaewook Lee, Jinseong Park

Causal-LLM: A Unified One-Shot Framework for Prompt- and Data-Driven Causal Graph Discovery (2025.findings-emnlp)

Challenge:	delivering private retrieved documents directly to LLMs introduces vulnerability to membership inference attacks .
Approach:	They propose a similarity-based membership inference attack detection framework for RAG . they propose obfuscate attackers, maintain data utility, and remain system-agnostic .
Outcome:	The proposed framework can detect and hide membership inference attacks, while remaining system-agnostic against them.

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Amartya Roy, N Devharish, Shreya Ganguly, Kripabandhu Ghosh

LRPLAN: A Multi-Agent Collaboration of Large Language and Reasoning Models for Planning with Implicit & Explicit Constraints (2025.findings-emnlp)

Challenge:	Current causal discovery methods rely on pairwise or iterative strategies that fail to capture global dependencies, amplify local biases, and reduce overall accuracy.
Approach:	They propose a framework for one-step full causal graph discovery using prompt-based discovery and a data-driven method for settings without metadata.
Outcome:	The proposed framework outperforms state-of-the-art models by approximately 40% in edge accuracy on datasets like Asia and Sachs while maintaining strong performance on more complex graphs.

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T Karthikeyan, Om Dehlan, null Mausam, Manish Gupta

DLPO: Towards a Robust, Efficient, and Generalizable Prompt Optimization Framework from a Deep-Learning Perspective (2025.findings-emnlp)

Challenge:	LRPlan is a language-based multi-agent system for complex planning problems . large language models are often unable to maintain consistency across the planning process .
Approach:	They propose a language-based multi-agent architecture where LLM and LRM agents collaborate at training time to abstract important patterns, heuristics and insights about the domain.
Outcome:	The proposed language-based multi-agent architecture outperforms existing models and makes it publicly available.

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Dengyun Peng, Yuhang Zhou, Qiguang Chen, JinHao Liu, Jingjing Chen, Libo Qin, Wanxiang Che

Towards Robust Few-Shot Relation Classification: Incorporating Relation Description with Agreement (2025.findings-emnlp)

Challenge:	Existing methods for prompt optimization still face challenges in robustness, efficiency, and generalization.
Approach:	They propose 7 new approaches inspired by traditional deep learning paradigms for prompt optimization that integrate text-based gradient optimization.
Outcome:	The proposed methods integrate deep learning paradigms into text-based gradient optimization.

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Mengting Hu, Jianfeng Wu, Ming Jiang, Yalan Xie, Zhunheng Wang, Rui Ying, Xiaoyi Liu, Ruixuan Xu, Hang Gao, Renhong Cheng

For a Fistful of Puns: Evaluating a Puns in Multiword Expressions Identification Algorithm Without Dedicated Dataset (2025.findings-emnlp)

Challenge:	Existing approaches to recognize relational relationships with a few support samples are limited for unlimited queries.
Approach:	They propose a simple but effective framework that uses relation descriptions as external knowledge to enhance the model’s comprehension of the relation semantics.
Outcome:	The proposed framework outperforms strong baselines while being robust against various NOTA rates.

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Julien Bezançon, Gaël Lejeune

Challenge:	a recent study has shown that multiword expressions and wordplays impact their performance and are idiosyncratic and pervasive across languages.
Approach:	They propose an alignment-based PMWE identification and tagging algorithm to identify different types of PMWEs.
Outcome:	The proposed algorithm can identify different types of PMWEs and perform a snowclone detection task in English.

Watermarking for Factuality: Guiding Vision-Language Models Toward Truth via Tri-layer Contrastive Decoding (2025.findings-emnlp)

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Kyungryul Back, Seongbeom Park, Milim Kim, Mincheol Kwon, SangHyeok Lee, Hyunyoung Lee, Junhee Cho, Seunghyun Park, Jinkyu Kim

Are the Reasoning Models Good at Automated Essay Scoring? (2025.findings-emnlp)

Challenge:	Large Vision-Language Models (LVLMs) have shown promising results on multimodal tasks, but remain prone to hallucinations due to their reliance on a single modality or memorizing training data without properly grounding their outputs.
Approach:	They propose a training-free, tri-layer contrastive decoding with watermarking that uses a watermark-related question to identify a pivot layer and apply tri-layered contrastive coding to generate the final output.
Outcome:	The proposed method reduces hallucinations and generates more visually grounded responses.

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Lui Yoshida

Rethinking LLM-Based Recommendations: A Personalized Query-Driven Parallel Integration (2025.findings-emnlp)

Challenge:	o3-mini and o4-mini reasoning models perform poorly on automated essay scoring tasks, despite excellent performance on many benchmarks.
Approach:	They evaluated OpenAI’s o3-mini and o4-mini reasoning models in automated essay scoring tasks by measuring agreement with expert ratings and consistency in repeated evaluations.
Outcome:	The models’ performance on the TOEFL11 dataset is evaluated by measuring agreement with expert ratings and consistency in repeated evaluations.

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Donghee Han, Hwanjun Song, Mun Yong Yi

Challenge:	Query-to-Recommendation framework integrates large langucage models into recommendation systems . but it faces training-induced bias and bottlenecks from serialized architecture .
Approach:	They propose a parallel recommendation framework that decouples LLMs from candidate pre-selection and direct retrieval over the entire item pool.
Outcome:	The proposed framework decouples LLMs from candidate pre-selection and enables direct retrieval over the entire item pool.

RefVNLI: Towards Scalable Evaluation of Subject-driven Text-to-image Generation (2025.findings-emnlp)

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Aviv Slobodkin, Hagai Taitelbaum, Yonatan Bitton, Brian Gordon, Michal Sokolik, Nitzan Bitton Guetta, Almog Gueta, Royi Rassin, Dani Lischinski, Idan Szpektor

What data should I include in my POS tagging training set? (2025.findings-emnlp)

Challenge:	Existing methods assess only one aspect of the task, misalign with human judgments or rely on costly API-based evaluation.
Approach:	RefVNLI evaluates textual alignment and subject preservation in a single run.
Outcome:	RefVNLI outperforms or matches existing baselines across multiple benchmarks and subject categories.

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Zoey Liu, Masoud Jasbi, Christan Grant, Kenji Sagae, Emily Prud’hommeaux

AttnComp: Attention-Guided Adaptive Context Compression for Retrieval-Augmented Generation (2025.findings-emnlp)

Challenge:	POS tagging is a crucial task for descriptive linguistics and language documentation . POS tags are not available in all languages, but are used for training sets for understudied languages .
Approach:	They compare POS tagging with in-context learning, active learning, and random sampling . they find that POS can deliver reasonable results for communities with limited resources .
Outcome:	The proposed training set for Indigenous and endangered languages performs better than random sampling.

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Lvzhou Luo, Yixuan Cao, Ping Luo

SafeInt: Shielding Large Language Models from Jailbreak Attacks via Safety-Aware Representation Intervention (2025.findings-emnlp)

Challenge:	Existing methods for augmented large language models suffer from irrelevant retrieved content . existing methods struggle to adapt compression rates for different context, maintain low latency .
Approach:	We propose an adaptive, efficient and context-aware compression framework to reduce retrieved content . AttnComp uses a top-p compression algorithm to retain the minimal set of documents whose attention weights exceed a threshold.
Outcome:	Experiments show that AttnComp outperforms existing compression methods and uncompressed baselines in achieving higher accuracy with substantial compression rates and lower latency.

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Jiaqi Wu, Chen Chen, Chunyan Hou, Xiaojie Yuan

Staged Knowledge Distillation Through Least-to-Most Prompting: Optimizing Teacher Guidance via Difficulty-Aware Training (2025.findings-emnlp)

Challenge:	Jailbreak attacks exploit vulnerabilities in large language models to induce undesirable behavior . existing defenses cannot dynamically adjust representations based on harmfulness of queries .
Approach:	They propose a representation-aware representation method that shields LLMs from jailbreak attacks . SafeInt relocates jailbreak-related representations into the rejection region .
Outcome:	The proposed method outperforms baseline defenses while maintaining utility . it relocates jailbreak-related representations into the rejection region .

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Mengxiang Zhang, Lingyuan Liu

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering (2025.findings-emnlp)

Challenge:	Knowledge distillation (KD) enables the compression of large language models (LLMs) conventional methods suffer from training-inference mismatches and suboptimal performance due to expensive student-generated outputs.
Approach:	They propose a method that combines a CL strategy and adaptive loss design to reduce training mismatches and suboptimal performance.
Outcome:	L2M-KD outperforms existing white-box KD methods on instruction-following tasks . it outperformed existing methods, achieving superior student model performance with reduced overhead .

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Patrick Sutanto, Joan Santoso, Esther Irawati Setiawan, Aji Prasetya Wibawa

Teaching LLMs to Plan, Not Just Solve: Plan Learning Boosts LLMs Generalization in Reasoning Tasks (2025.findings-emnlp)

Challenge:	Large Language Models excel at few-shot learning but their direct application in real-world scenarios is often hindered by their high computational cost.
Approach:	They propose a framework that uses Large Language Models for data generation and scoring to improve encoder model performance.
Outcome:	The proposed approach improves accuracy from 28.9% to 39.3% on a few-shot MCQA task .

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Tianlong Wang, Junzhe Chen, Weibin Liao, Xueting Han, Jing Bai

FedCoT: Federated Chain-of-Thought Distillation for Large Language Models (2025.findings-emnlp)

Challenge:	Existing methods for reinforcement learning (RL) on self-generated data are limited in many domains.
Approach:	a new framework combines plan-based search with Step-level Advantage Preference Optimization to optimize plan learning.
Outcome:	The proposed framework improves in-domain performance and out-of-domain benchmarks.

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Tao Fan, Weijing Chen, Yan Kang, Guoqiang Ma, Hanlin Gu, Yuanfeng Song, Lixin Fan, Qiang Yang

SalaMAnder: Shapley-based Mathematical Expression Attribution and Metric for Chain-of-Thought Reasoning (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have emerged as a transformative force in artificial intelligence, demonstrating exceptional proficiency across various tasks.
Approach:	They propose a federated framework for the Chain-of-Thought distillation of knowledge from LLMs to SLMs, while adhering to privacy requirements.
Outcome:	The proposed framework ensures secure knowledge transfer from an LLM on a high-powered server to an SLM on resource-constrained client while adhering to privacy requirements.

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Yue Xin, Chen Shen, Shaotian Yan, Xiaosong Yuan, Yaoming Wang, Xiaofeng Zhang, Chenxi Huang, Jieping Ye

Representing LLMs in Prompt Semantic Task Space (2025.findings-emnlp)

Challenge:	Chain-of-Thought prompting improves the math reasoning capability of large language models.
Approach:	They propose a method for attribution of component-level contributions in CoT reasoning using Shapley value and a stratified sampling algorithm that significantly reduces computational complexity.
Outcome:	The proposed method reduces computational complexity and provides robust correlations with model performance.

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Idan Kashani, Avi Mendelson, Yaniv Nemcovsky

PersLLM: A Personified Training Approach for Large Language Models (2025.findings-emnlp)

Challenge:	Large language models (LLMs) achieve impressive results over various tasks, and public repositories contain an abundance of pre-trained models.
Approach:	They propose an efficient, training-free approach to representing LLMs as linear operators within the prompts’ semantic task space.
Outcome:	The proposed representations achieve state-of-the-art results on success prediction and model selection tasks with notable performance in out-of sample scenarios.

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Zheni Zeng, Jiayi Chen, Huimin Chen, Yukun Yan, Yuxuan Chen, Zhenghao Liu, Zhiyuan Liu, Maosong Sun

The Illusion of Randomness: How LLMs Fail to Emulate Stochastic Decision-Making in Rock-Paper-Scissors Games? (2025.findings-emnlp)

Challenge:	Large language models exhibit human-like intelligence, enabling them to simulate human behavior and support various applications that require both humanized communication and extensive knowledge reserves.
Approach:	They propose a framework for better data construction and model tuning to unlock the potential of LLM personification by using Chain-of-Thought prompting and anti-induction.
Outcome:	The proposed framework improves data construction and model tuning for insufficient data usage and rigid behavior patterns.

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Zihao Guo, Hongtao Lv, Chaoli Zhang, Yibowen Zhao, Yixin Zhang, Lizhen Cui

Challenge:	Prior research indicates that large language models articulate the theoretical probability distributions associated with optimal strategic choices, but their actual decision-making diverges from these prescriptions.
Approach:	a systematic evaluation of 20 state-of-the-art LLMs reveals a cognitive bias gap . intrinsic biases inherited from pre-training corpora alone are insufficient to explain deviations . a semantic-free paradigm strips away intrinsic bias to isolate pure positional bias .
Outcome:	a systematic evaluation of 20 state-of-the-art LLMs shows that intrinsic biases are insufficient to explain deviations.

DAPE-BR: Distance-Aware Positional Encoding for Mitigating Object Hallucination in LVLMs (2025.findings-emnlp)

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Mingrui Xie, Tianxiang Xu, Qianhai Tang, Shanming Yao, Xiaofeng Zhang, Junliang Du

From Confidence to Collapse in LLM Factual Robustness (2025.findings-emnlp)

Challenge:	Large Vision–Language Models (LVLMs) suffer from object hallucination, generating descriptions for objects that are absent from the image, which undermines reliability and hinders real-world deployment.
Approach:	They propose a positional-alignment scheme that preserves pretrained weight order while globally—- visual–text distances, embeds an isotropic fused patch-distance metric, and applies a patch-delay causal mask to enforce spatial causality.
Outcome:	Extensive experiments on POPE, MMStar and SQA show that DAPE-BR reduces hallucinations and boosts performance.

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Alina Fastowski, Bardh Prenkaj, Gjergji Kasneci

CtrlNews: LLM-based Multi-Agent Controllable News Writing via Knowledge Gravitational Field (2025.findings-emnlp)

Challenge:	Existing evaluation methods focus on performance-based metrics, often investigating from the perspective of prompt perturbations, which captures only the externally triggered side of knowledge robustness.
Approach:	They propose a method to measure factual robustness from the perspective of the generation process by analyzing token distribution entropy and temperature scaling sensitivity.
Outcome:	The proposed method measures factual robustness from the perspective of the generation process and entropy and temperature scaling sensitivity.

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Yifei Xu, Yingjie Zong, Wang Zhonghua, Sirui Wu, Yuan Rao, Dan Zhang, Shuiguang Deng

Challenge:	Current approaches to news writing rely on superficially retrieved information and oversimplified knowledge enumeration resulting in shallow, repetitive, and unordered outputs.
Approach:	They propose an LLM-based multi-agent controllable news writing framework called CtrlNews . they propose a fine-grained viewpoint control mechanism to regulate bias, emotion, and exaggeration attributes.
Outcome:	The proposed framework simulates expert questioning through automated role assignment and question generation followed by a three-layer hierarchical gravitational graph iteratively refined via expansion-reflection cycles.

Joint Enhancement of Relational Reasoning for Long-Context LLMs (2025.findings-emnlp)

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Zhirui Chen, Wei Shen, Jiashui Huang, Ling Shao

Training Medical QA Models Based on Mixed Rewards from Multiple-Choice and Open-Ended Questions (2025.findings-emnlp)

Challenge:	JERR is a graph-based reasoning framework for large language models . it enables LLMs to handle extended contexts with improved reliability and transparency .
Approach:	They propose a graph-based reasoning framework that integrates synopsis extraction, graph construction, and relational reasoning.
Outcome:	The proposed framework outperforms baselines on ROUGE and F1 metrics and achieves the highest scores on the LLM-Rater evaluation.

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Yue Qiu, Yujan Ting, Pei Dong, Terrence Chen, Weijing Huang

Rethink Rumor Detection in the Era of LLMs: A Review (2025.findings-emnlp)

Challenge:	Reinforcement learning (RL) for large language models typically requires clear reward signals, which are often unavailable for open-ended (OE) questions where answer evaluation is ambiguous without scalable expert labeling.
Approach:	They propose a mixed-data approach to training large language models with varying reward clarity . they combine Multiple-choice questions (MCQs) with OE questions for which they use simpler, potentially noisy rewards such as Jaccard similarity or LLM-based evaluators.
Outcome:	The mixed-data approach improves medical question-answering performance across model scales.

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Chang Yang, Peng Zhang, Jing Zhang, Hui Gao, Changhao Song

ScholarBench: A Bilingual Benchmark for Abstraction, Comprehension, and Reasoning Evaluation in Academic Contexts (2025.findings-emnlp)

Challenge:	rumor detection has been reshaped by large language models (LLMs) this paper proposes a Cognition-Interaction-Behavior (CIB) framework for rumour detection based on collective intelligence .
Approach:	They propose a Cognition-Interaction-Behavior framework for rumor detection based on collective intelligence and explore synergistic relationship between LLMs and collective intelligence in rumour governance.
Outcome:	The proposed framework unifies existing methods and reveals synergistic relationship between LLMs and collective intelligence in rumor governance.

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Dongwon Noh, Donghyeok Koh, Junghun Yuk, Gyuwan Kim, Jae Yong Lee, KyungTae Lim, Cheoneum Park

MAGIC: A Multi-Hop and Graph-Based Benchmark for Inter-Context Conflicts in Retrieval-Augmented Generation (2025.findings-emnlp)

Challenge:	ScholarBench evaluates domain-specific knowledge of large language models (LLMs) prior benchmarks lack the scalability to handle complex academic tasks.
Approach:	ScholarBench evaluates the academic reasoning ability of large language models . the benchmark is constructed through a three-step process .
Outcome:	ScholarBench evaluates the academic reasoning ability of large language models . the benchmark comprises 5,031 examples in Korean and 5,309 examples in English .

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Jungyeon Lee, Lee Kangmin, Taeuk Kim

Align Attention Heads Before Merging Them: An Effective Way for Converting MHA to GQA (2025.findings-emnlp)

Challenge:	Existing benchmarks for investigating knowledge conflict have notable limitations, including a narrow focus on the question answering setup, heavy reliance on entity substitution techniques, and a restricted range of conflict types.
Approach:	They propose a knowledge graph-based framework that generates varied and subtle conflicts between two similar yet distinct contexts while ensuring interpretability through the explicit relational structure of KGs.
Outcome:	The proposed framework generates varied and subtle conflicts between two similar yet distinct contexts while ensuring interpretability through the explicit relational structure of KGs.

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Qingyun Jin, Xiaohui Song, Feng Zhou, Zengchang Qin

Challenge:	Large language models (LLMs) have demonstrated exceptional performance across diverse natural language processing tasks.
Approach:	They propose a method for converting multi-head attention into grouped-query attention with any compression ratio of KV heads.
Outcome:	The proposed method can compress up to 87.5% KV heads of LLaMA2-7B model and 75% Kv heads of Sheared-LLa MA-1.3B with acceptable performance degradation.

DRBO: Mitigating Short Board Effect via Dynamic Reward Balancing in Multi-reward LLM Optimization (2025.findings-emnlp)

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Nuo Chen, Yufei Gao, Yongnan Jin, Yan Hu, Anningzhe Gao, Lingyong Yan, Benyou Wang

Challenge:	a new framework to optimize large language models (LLMs) for evaluation metrics is needed to balance weaker metrics.
Approach:	They propose a Dynamic Reward Balancing Optimization framework to mitigate the "short-board effect" they apply it to single-task and multi-type task scenarios .
Outcome:	The proposed framework improves performance and balances performance across multiple metrics.

Enhancing LLM Knowledge Learning through Generalization (2025.findings-emnlp)

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Mingkang Zhu, Xi Chen, Zhongdao Wang, Bei Yu, Hengshuang Zhao, Jiaya Jia

FastCuRL: Curriculum Reinforcement Learning with Stage-wise Context Scaling for Efficient Training R1-like Reasoning Models (2025.findings-emnlp)

Challenge:	Continued pre-training on paraphrased data has shown empirical promise for enhancing knowledge acquisition, but this approach is costly and unreliable as it relies on external models or manual effort for rewriting.
Approach:	They propose formatting-based data augmentation which diversifies documents conveying the same knowledge by altering document formats rather than their content.
Outcome:	The proposed methods improve generalization to diverse paraphrased contexts and enhance pre-training and instruction tuning.

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Mingyang Song, Mao Zheng, Zheng Li, Wenjie Yang, Xuan Luo

TR-MTEB: A Comprehensive Benchmark and Embedding Model Suite for Turkish Sentence Representations (2025.findings-emnlp)

Challenge:	Improving training efficiency remains a challenge in large-scale Reinforcement Learning (RL).
Approach:	They propose a curriculum RL framework with stage-wise context scaling to improve RL training efficiency.
Outcome:	The proposed framework outperforms state-of-the-art reasoning models on five benchmarks and achieves 49.6% accuracy on AIME 2024.

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Mehmet Selman Baysan, Tunga Gungor

ImpRAG: Retrieval-Augmented Generation with Implicit Queries (2025.findings-emnlp)

Challenge:	TR-MTEB is the first large-scale, task-diverse benchmark for sentence embedding models for Turkish.
Approach:	a new benchmark evaluates sentence embedding models for Turkish . TR-MTEB covers six core tasks and 26 high-quality datasets .
Outcome:	The TR-MTEB benchmark covers six core tasks and includes 26 high-quality datasets . the models achieve competitive performance across most tasks and significantly improve on baseline models.

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Wenzheng Zhang, Xi Victoria Lin, Karl Stratos, Wen-tau Yih, Mingda Chen

HEAL: A Hypothesis-Based Preference-Aware Analysis Framework (2025.findings-emnlp)

Challenge:	Retrieval-Augmented Generation (RAG) systems treat retrieval and generation as separate processes, requiring explicit textual queries to connect them.
Approach:	They propose a query-free RAG system that integrates retrieval and generation into a unified model.
Outcome:	The proposed system can achieve 3.6-11.5 accuracy improvements on unseen tasks . it allows models to express their information needs without human-specified queries .

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Yifu Huo, Chenglong Wang, Qiren Zhu, Shunjie Xing, Tong Xiao, Chunliang Zhang, Tongran Liu, JingBo Zhu

Challenge:	Preference optimization methods like DPO are often evaluated on a single response, overlooking other outputs.
Approach:	They propose a Hypothesis-based PrEference-aware AnaLysis Framework that formulates preference alignment as a re-ranking process within hypothesis spaces.
Outcome:	The proposed evaluation paradigm re-ranks preference alignment as a reranking process within hypothesis spaces.

A Survey of Multilingual Reasoning in Language Models (2025.findings-emnlp)

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Akash Ghosh, Debayan Datta, Sriparna Saha, Chirag Agarwal

CLEAR: A Framework Enabling Large Language Models to Discern Confusing Legal Paragraphs (2025.findings-emnlp)

Challenge:	This survey provides the first in-depth review of multilingual reasoning in Language Models.
Approach:	This survey provides the first in-depth review of multilingual reasoning in LMs.
Outcome:	The present study provides the first in-depth review of multilingual reasoning in LMs.

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Qi Xu, Qian Liu, Hao Fei, Hang Yu, Shuhao Guan, Xiao Wei

NAP2: A Benchmark for Naturalness and Privacy-Preserving Text Rewriting by Learning from Human (2025.findings-emnlp)

Challenge:	Existing work focuses on enabling LLMs to leverage legal rules to tackle complex legal reasoning tasks, but ignores their ability to understand legal rules.
Approach:	They propose a legal paragraph prediction task that aims to predict the legal paragraph given criminal facts and a framework CLEAR to enhance their legal reasoning ability.
Outcome:	The proposed model improves the ability of LLMs to analyze legal cases with the guidance of legal rule insights.

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Shuo Huang, William Maclean, Xiaoxi Kang, Qiongkai Xu, Zhuang Li, Xingliang Yuan, Gholamreza Haffari, Lizhen Qu

Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents (2025.findings-emnlp)

Challenge:	a large number of large language models are being used to protect user privacy . sanitizing sensitive text using two common strategies is the answer .
Approach:	They propose sanitizing sensitive text using deleting expressions and abstracting them . they propose a tool for text rewriting that uses crowdsourcing and large language models .
Outcome:	The proposed approach protects privacy before sending sensitive data to large language models . it combines crowdsourcing and large language modeling to create a text rewrite tool .

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Long Li, Weiwen Xu, Jiayan Guo, Ruochen Zhao, Xingxuan Li, Yuqian Yuan, Boqiang Zhang, Yuming Jiang, Yifei Xin, Ronghao Dang, Yu Rong, Deli Zhao, Tian Feng, Lidong Bing

Challenge:	Existing methods for idea generation either trivially prompt LLMs or expose LLM to extensive literature without indicating useful information.
Approach:	They propose a chain-of-ideas agent that organizes literature in a chains structure . they propose evaluating idea-generation methods from different perspectives .
Outcome:	The proposed agent outperforms existing methods and matches human quality in idea generation.

Unveiling Multimodal Processing: Exploring Activation Patterns in Multimodal LLMs for Interpretability and Efficiency (2025.findings-emnlp)

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Chuan Wu, Meng Su, Youxuan Fang, Shaolin Zhu

Self-Supervised Prompt Optimization (2025.findings-emnlp)

Challenge:	Recent advances in multimodal large language models have remained opaque.
Approach:	They propose a method to convert dense MLLMs into fine-grained Mixture-of-Experts architectures.
Outcome:	The proposed method outperforms random expert pruning and sparse activation and model pruning.

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Jinyu Xiang, Jiayi Zhang, Zhaoyang Yu, Xinbing Liang, Fengwei Teng, Jinhao Tu, Fashen Ren, Xiangru Tang, Sirui Hong, Chenglin Wu, Yuyu Luo

Polish-English medical knowledge transfer: A new benchmark and results (2025.findings-emnlp)

Challenge:	Existing prompt optimization methods rely heavily on external references such as ground truth or by humans, limiting their applicability in real-world scenarios where such data is unavailable or costly to obtain.
Approach:	They propose a cost-efficient framework that discovers effective prompts for both closed and open-ended tasks without external reference.
Outcome:	The proposed framework outperforms state-of-the-art prompt optimization methods with significantly lower costs and fewer samples.

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Łukasz Grzybowski, Jakub Pokrywka, Michał Ciesiółka, Jeremi Ignacy Kaczmarek, Marek Kubis

Hard Negatives, Hard Lessons: Revisiting Training Data Quality for Robust Information Retrieval with LLMs (2025.findings-emnlp)

Challenge:	Large Language Models have demonstrated significant potential in specialized tasks, including medical problem-solving.
Approach:	They propose to use a Polish medical licensing and specialization exam dataset to evaluate LLMs . they use exam questions and parallel Polish-English corpora professionally translated for foreign candidates .
Outcome:	The proposed dataset includes Polish exam questions and parallel Polish-English corpora professionally translated for foreign candidates.

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Nandan Thakur, Crystina Zhang, Xueguang Ma, Jimmy Lin

EventRelBench: A Comprehensive Benchmark for Evaluating Event Relation Understanding in Large Language Models (2025.findings-emnlp)

Challenge:	Using LLMs to identify false negatives improves retrieval and reranker models by 0.7-1.4 points on BEIR and by 1.7-1.8 points on AIR-Bench evaluation.
Approach:	They use a simple, cost-effective approach to identify and relabel false negatives in training datasets.
Outcome:	The proposed approach improves retrieval models by 0.7-1.4 points on BEIR and by 1.7-1.8 points on AIR-Bench evaluation.

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Jie Gong, Biaoshuai Zheng, Qiwang Hu

S2LPP: Small-to-Large Prompt Prediction across LLMs (2025.findings-emnlp)

Challenge:	Existing LLMs fail to capture event relationships, despite advances in NLP . a new benchmark is being developed to assess LLM's ability to extract event relationships .
Approach:	They propose a benchmark to assess LLMs' ability to extract event relations . EventRelBench comprises 35K diverse event relation questions .
Outcome:	The benchmark EventRelBench measures the performance of large language models on event relation extraction tasks.

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Liang Cheng, Tianyi Li, Zhaowei Wang, Mark Steedman

DroidCall: A Dataset for LLM-powered Android Intent Invocation (2025.findings-emnlp)

Challenge:	a small model can be used to select effective prompt templates for a larger model.
Approach:	They propose a method to use a smaller model to select effective prompt templates for a larger model.
Outcome:	The proposed method significantly reduces the cost of prompt engineering while matching performance with optimal prompts among candidates.

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Weikai Xie, Li Zhang, Shihe Wang, Rongjie Yi, Mengwei Xu

Tool Zero: Training Tool-Augmented LLMs via Pure RL from Scratch (2025.findings-emnlp)

Challenge:	We present DroidCall, the first training and testing dataset for accurate Android intent invocation.
Approach:	We introduce DroidCall, the first training and testing dataset for accurate Android intent invocation.
Outcome:	The proposed dataset provides a training and testing pipeline for Android intent invocation.

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Yirong Zeng, Xiao Ding, Yutai Hou, Yuxian Wang, Li Du, Juyi Dai, Qiuyang Ding, Duyu Tang, Dandan Tu, Weiwen Liu, Bing Qin, Ting Liu

INREACT: An Inspire-Then-Reinforce Training Framework For Multimodal GUI Agent (2025.findings-emnlp)

Challenge:	Experimental results demonstrate that our models achieve over 7% performance improvement compared to both SFT and RL-with-SFT models under the same experimental settings.
Approach:	They propose a dynamic generalization-guided reward design for rule-based RL that shifts rewards from exploratory to exploitative tool-use patterns.
Outcome:	The proposed model achieves over 7% performance improvement compared to SFT and RL-with-SFT models under the same experimental settings.

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Yuanlei Wang, Liuzhou Zhang, Haohao Luo, Ying Shen

Facts Fade Fast: Evaluating Memorization of Outdated Medical Knowledge in Large Language Models (2025.findings-emnlp)

Challenge:	Existing multimodal large language models struggle with precise localization of small elements.
Approach:	They propose a multimodal GUI agent framework that unifies observing, thinking, and acting for precise and interpretable decision-making.
Outcome:	The proposed framework unifies observing, thinking, and acting for precise and interpretable decision-making.

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Juraj Vladika, Mahdi Dhaini, Florian Matthes

Zero-Shot Privacy-Aware Text Rewriting via Iterative Tree Search (2025.findings-emnlp)

Challenge:	LLMs encode extensive knowledge within their parameters, but the knowledge in LLM models can become outdated over time.
Approach:	They propose two new LLMs that provide outdated medical advice . they compare the models with a set of QA pairs whose verdict changed through time .
Outcome:	The proposed models exhibit memorization of outdated knowledge to some extent.

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Shuo Huang, Xingliang Yuan, Gholamreza Haffari, Lizhen Qu

KoLEG: On-the-Fly Korean Legal Knowledge Editing with Continuous Retrieval (2025.findings-emnlp)

Challenge:	Existing methods for text anonymization and de-identification struggle to balance privacy preservation with text naturalness and utility.
Approach:	They propose a tree-search-based iterative sentence rewriting algorithm that obfuscates or deletes private information while preserving coherence, relevance, and naturalness.
Outcome:	The proposed algorithm outperforms existing baselines on privacy-sensitive datasets.

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Jaehyung Seo, Dahyun Jung, Jaewook Lee, Yongchan Chun, Dongjun Kim, Hwijung Ryu, Donghoon Shin, Heuiseok Lim

HARE: an entity and relation centric evaluation framework for histopathology reports (2025.findings-emnlp)

Challenge:	a recent study shows that Korean legal knowledge is subject to frequent temporal updates driven by societal needs and government policies.
Approach:	They propose a Korean Legal knowledge editing framework enhanced with continuous retrieval . they employ an Editing-Aware Learning Strategy and a LawEdit Retriever .
Outcome:	a new framework outperforms existing methods for updating legal knowledge in Korean . it maintains robust performance in sequential editing and is qualitatively validated by legal experts.

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Yunsoo Kim, Michal Wen Sheue Ong, Alex Shavick, Honghan Wu, Adam P. Levine

VeriFastScore: Speeding up long-form factuality evaluation (2025.findings-emnlp)

Challenge:	evaluating the clinical quality of medical domain automated text generation remains a challenge.
Approach:	They propose a framework for histopathology automated report evaluation that prioritizes clinically relevant content by aligning critical histo pathology entities and relations between reference and generated reports.
Outcome:	The proposed framework outperforms existing metrics in histopathology report evaluations.

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Rishanth Rajendhran, Amir Zadeh, Matthew Sarte, Chuan Li, Mohit Iyyer

Challenge:	VeriFastScore model can be used to evaluate long-form factuality but requires multiple LLM calls and can take up to 100s to evaluate a single response.
Approach:	They propose a model that leverages synthetic data to fine-tune Llama3.1 8B for extracting and verifying all verifiable claims within a given text based on evidence from Google Search.
Outcome:	The proposed model achieves strong correlation with the original VeriScore pipeline at both the example level and system level while achieving an overall speedup of 6.6 over Veriscore.

B-REASO: A Multi-Level Multi-Faceted Bengali Evaluation Suite for Foundation Models (2025.findings-emnlp)

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Md Tanzib Hosain, Md Kishor Morol

Extracting Conceptual Spaces from LLMs Using Prototype Embeddings (2025.findings-emnlp)

Challenge:	B-REASO is the first inclusive assessment suite for advanced foundation model knowledge and reasoning skills in a Bengali language setup.
Approach:	We provide a Bengali assessment suite to assess advanced foundation model knowledge and reasoning skills in a language setup.
Outcome:	The B-REASO includes multiple-choice questions with four different degrees of difficulty . the questions cover 50 different fields, from science and engineering to the humanities .

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Nitesh Kumar, Usashi Chatterjee, Steven Schockaert

FC-Attack: Jailbreaking Multimodal Large Language Models via Auto-Generated Flowcharts (2025.findings-emnlp)

Challenge:	Conceptual spaces represent entities and concepts using cognitively meaningful dimensions . practical methods for extracting conceptual spaces are currently lacking .
Approach:	They propose a strategy in which features are encoded by embedding a description of a corresponding prototype.
Outcome:	The proposed approach is highly effective.

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Ziyi Zhang, Zhen Sun, Zongmin Zhang, Jihui Guo, Xinlei He

Multilingual Data Filtering using Synthetic Data from Large Language Models (2025.findings-emnlp)

Challenge:	Recent research shows that multimodal large language models are vulnerable to jailbreak attacks .
Approach:	They propose a jailbreak attack method based on auto-generated flowcharts . the flowchartings are then combined with a benign textual prompt to execute the attack .
Outcome:	The proposed method achieves an attack success rate of up to 96% via images and 78% via videos across multiple MLLMs.

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Jonas Waldendorf, Barry Haddow, Alexandra Birch, Mateusz Klimaszewski

SAFE: A Sparse Autoencoder-Based Framework for Robust Query Enrichment and Hallucination Mitigation in LLMs (2025.findings-emnlp)

Challenge:	Recent studies have shown that effective filters can be created by utilising Large Language Models to synthetically label data, which is then used to train smaller neural models for filtering purposes.
Approach:	They extend this approach to languages beyond English to train neural models for filtering purposes.
Outcome:	The proposed approach is effective at filtering parallel text for translation quality and filtering for domain specificity.

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Samir Abdaljalil, Filippo Pallucchini, Andrea Seveso, Hasan Kurban, Fabio Mercorio, Erchin Serpedin

Soteria: Language-Specific Functional Parameter Steering for Multilingual Safety Alignment (2025.findings-emnlp)

Challenge:	Large Language Models suffer from hallucinations, which can undermine their performance in critical applications.
Approach:	They propose a framework for detecting and mitigating hallucinations by leveraging SAEs.
Outcome:	The proposed framework improves query generation accuracy and mitigates hallucinations across datasets.

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Somnath Banerjee, Sayan Layek, Pratyush Chatterjee, Animesh Mukherjee, Rima Hazra

LLMs as a synthesis between symbolic and distributed approaches to language (2025.findings-emnlp)

Challenge:	Soteria locates and minimally adjusts the “functional heads” most responsible for harmful content generation in each language.
Approach:	Soteria locates and minimally adjusts the "functional heads" responsible for harmful content generation in each language.
Outcome:	The proposed approach reduces harmful content generation in languages while preserving model performance.

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Gemma Boleda

Challenge:	a fierce battle is being fought between symbolic and distributed approaches to language and cognition . a recent study shows that morphosyntactic knowledge is encoded in a near-discrete fashion in LLMs .
Approach:	a new position paper examines the role of distributed and distributed approaches in language learning . authors argue that deep learning models represent a synthesis between the two traditions .
Outcome:	a new position paper shows that deep learning models for language represent a synthesis between the two traditions.

MIND: Towards Immersive Psychological Healing with Multi-Agent Inner Dialogue (2025.findings-emnlp)

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Yujia Chen, Changsong Li, Yiming Wang, Tianjie Ju, Qingqing Xiao, Nan Zhang, Zifan Kong, Peng Wang, Binyu Yan

A Monte-Carlo Sampling Framework For Reliable Evaluation of Large Language Models Using Behavioral Analysis (2025.findings-emnlp)

Challenge:	Mental health issues are worsening in today’s competitive society, such as depression and anxiety.
Approach:	They propose a multi-agent inner dialogue paradigm that provides more immersive psychological healing environments.
Outcome:	The proposed paradigm provides more immersive psychological healing environments.

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Davood Wadi, Marc Fredette

Understanding How Value Neurons Shape the Generation of Specified Values in LLMs (2025.findings-emnlp)

Challenge:	Current approaches to evaluation of large language models ignore high entropy of LLM responses.
Approach:	They propose a Monte-Carlo evaluation framework for evaluating large language models . they test multiple LLMs to see if they are susceptible to cognitive biases .
Outcome:	The proposed framework shows that LLMs are more human-like and less rational . it also shows that larger LLM models are more susceptible to cognitive biases .

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Yi Su, Jiayi Zhang, Shu Yang, Xinhai Wang, Lijie Hu, Di Wang

Likelihood Variance as Text Importance for Resampling Texts to Map Language Models (2025.findings-emnlp)

Challenge:	Current approaches to interpret value representations are limited by superficial judgments over mechanistic analysis.
Approach:	They propose a mechanistic interpretability framework that uses the Schwartz Values Survey to interpret value . they use a dataset that operationalizes four dimensions of universal value through behavioral contexts .
Outcome:	The proposed method bridges psychological value frameworks with neuron analysis in large language models.

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Momose Oyama, Ryo Kishino, Hiroaki Yamagiwa, Hidetoshi Shimodaira

Think Twice, Generate Once: Safeguarding by Progressive Self-Reflection (2025.findings-emnlp)

Challenge:	a language model map requires large text sets to be constructed . a resampling method reduces the number of texts needed while preserving accuracy of KL divergence estimates.
Approach:	They propose a method that selects important texts with weights proportional to log-likelihoods across models for each text.
Outcome:	The proposed method reduces the number of required texts while preserving the accuracy of KL divergence estimates.

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Hoang Phan, Victor Li, Qi Lei

Efficient Integration of External Knowledge to LLM-based World Models via Retrieval-Augmented Generation and Reinforcement Learning (2025.findings-emnlp)

Challenge:	Large language models generate coherent and contextually relevant text, but their deployment raises significant concerns about the potential for harmful or inappropriate content.
Approach:	They propose a novel inference-time technique that empowers LLMs to self-monitor and correct their outputs dynamically.
Outcome:	The proposed method reduces the attack success rate from 77.47% to 5.86%, to Llama-3.1-8B base from 89.70% to 5.56%, and to Qwen2.5-7B-Instruct from 44.44% to 3.84%, without additional training.

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Chang Yang, Xinrun Wang, Qinggang Zhang, Qi Jiang, Xiao Huang

Comparing Apples to Oranges: A Dataset & Analysis of LLM Humour Understanding from Traditional Puns to Topical Jokes (2025.findings-emnlp)

Challenge:	Existing attempts to enhance LLM-based world models through prompting or fine-tuning approaches are either requiring human knowledge or computationally extensive.
Approach:	They propose a framework that leverages retrieval-augmented generation to integrate external knowledge to LLM-based world models.
Outcome:	The proposed framework outperforms baseline models and exhibits strong generalizability.

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Tyler Loakman, William Thorne, Chenghua Lin

Modeling, Evaluating, and Embodying Personality in LLMs: A Survey (2025.findings-emnlp)

Challenge:	Existing work on humour explanation has focused on short pun-based jokes, but Large Language Models (LLMs) are not capable of generating adequate explanations of all joke types.
Approach:	They compare the ability of Large Language Models (LLMs) to explain humour from simple puns to complex topical humor that requires esoteric knowledge of real-world entities and events.
Outcome:	The proposed models are incapable of generating adequate explanations of all joke types, highlighting the narrow focus of most existing work on overly simple joke forms.

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Iago Alves Brito, Julia Soares Dollis, Fernanda Bufon Färber, Pedro Schindler Freire Brasil Ribeiro, Rafael Teixeira Sousa, Arlindo Rodrigues Galvão Filho

Challenge:	This survey provides a comprehensive overview of the LLM-driven personality scenario.
Approach:	This survey provides a comprehensive overview of the LLM-driven personality scenario.
Outcome:	The proposed taxonomy analyzes the limitations of existing methods and identifies key research gaps.

Benchmarking the Detection of LLMs-Generated Modern Chinese Poetry (2025.findings-emnlp)

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Shanshan Wang, Junchao Wu, Fengying Ye, Derek F. Wong, Jingming Yao, Lidia S. Chao

Challenge:	Detecting AI-generated poetry is difficult due to distinctive characteristics of modern Chinese poetry.
Approach:	They propose a benchmark for detecting AI-generated modern Chinese poetry . they use a high-quality dataset and systematic performance assessments .
Outcome:	The proposed benchmark is based on a high-quality dataset of 800 poems written by six professional poets and 41,600 poems generated by four mainstream LLMs.

Leveraging the Cross-Domain & Cross-Linguistic Corpus for Low Resource NMT: A Case Study On Bhili-Hindi-English Parallel Corpus (2025.findings-emnlp)

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Pooja Singh, Shashwat Bhardwaj, Vaibhav Sharma, Sandeep Kumar

Creative Preference Optimization (2025.findings-emnlp)

Challenge:	linguistic diversity of India poses significant machine translation challenges, authors say . underrepresented tribal languages like Bhili lack high-quality linguistic resources .
Approach:	They introduce a Bhili-Hindi-English Parallel Corpus, the first and largest parallel corpus worldwide . they evaluated a wide range of proprietary and open-source MLLMs on bidirectional translation tasks .
Outcome:	The proposed corpus spans critical domains such as education, administration, and news.

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Mete Ismayilzada, Antonio Laverghetta Jr., Simone A. Luchini, Reet Patel, Antoine Bosselut, Lonneke Van Der Plas, Roger E. Beaty

Challenge:	Existing methods for enhancing LLM creativity focus on diversity or specific tasks, failing to address creativity’s multifaceted nature in a generalizable way.
Approach:	They propose a method that injects signals from multiple creativity dimensions into the preference optimization objective in a modular fashion.
Outcome:	The proposed method outperforms baseline models on automated and human evaluations while maintaining high output quality.

Assistant-Guided Mitigation of Teacher Preference Bias in LLM-as-a-Judge (2025.findings-emnlp)

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Zhuo Liu, Moxin Li, Xun Deng, Qifan Wang, Fuli Feng

Challenge:	LLM-as-a-Judge uses large language models to evaluate the quality of LLM generated responses, but training proxy judge models using evaluation data generated by powerful teacher models introduces a critical yet previously overlooked issue: teacher preference bias.
Approach:	They propose a new setting that incorporates an additional assistant model, which is not biased toward the teacher model’s responses, to complement the training data.
Outcome:	The proposed model reduces teacher preference bias while maintaining strong performance across six evaluation benchmarks.

Uplift-RAG: Uplift-Driven Knowledge Preference Alignment for Retrieval-Augmented Generation (2025.findings-emnlp)

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Changle Qu, Sunhao Dai, Hengyi Cai, Yiyang Cheng, Jun Xu, Shuaiqiang Wang, Dawei Yin

Sugar-Coated Poison: Benign Generation Unlocks Jailbreaking (2025.findings-emnlp)

Challenge:	Existing efforts to estimate document utility rely on downstream generation performance, which conflates the influence of external documents with the intrinsic knowledge of the LLM.
Approach:	They propose an uplift-based definition of document utility that quantifies each document’s marginal benefit over the LLM’s internal knowledge.
Outcome:	The proposed framework improves the performance of the LLM by incorporating external retrieved documents into the model.

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Yuhang Wu, Yu-Jie Xiong, Hao Zhang, Jia-Chen Zhang, Zheng Zhou

DivScene: Towards Open-Vocabulary Object Navigation with Large Vision Language Models in Diverse Scenes (2025.findings-emnlp)

Challenge:	Existing methods to jailbreak large language models rely on black-box manipulation of prompt templates, resulting in high costs and poor generalizability.
Approach:	They propose a sugar-coated poison attack paradigm that uses a "semantic reversal" strategy to induce the model into a safety response mode.
Outcome:	The proposed attack paradigm outperforms baselines in the study.

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Zhaowei Wang, Hongming Zhang, Tianqing Fang, Ye Tian, Yue Yang, Kaixin Ma, Xiaoman Pan, Yangqiu Song, Dong Yu

Data-scarce Behavior Editing of Language Models (2025.findings-emnlp)

Challenge:	Large Vision-Language Models (LVLMs) have achieved significant progress in tasks like visual question answering and document understanding.
Approach:	They introduce DivScene, a large-scale dataset with 4,614 houses across 81 scene types and 5,707 kinds of target objects.
Outcome:	The proposed dataset provides a much greater diversity of target objects and scene types than existing datasets, enabling a comprehensive task evaluation.

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Joykirat Singh, Subhabrata Dutta, Tanmoy Chakraborty

FIER: Fine-Grained and Efficient KV Cache Retrieval for Long-context LLM Inference (2025.findings-emnlp)

Challenge:	Prior studies show that noisy neural circuitries coexist with generalizable abilities within LLMs.
Approach:	a new method is proposed to improve the generalizability of large-scale web-based text models . a TaRot method is based on learnable rotation matrices optimized for Bayesian optimization .
Outcome:	a new method for task adaptation improves on multiple classification and generation tasks . it improves upon zero- and few-shot performance, with average improvements of 14% and 15% .

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Dongwei Wang, Zijie Liu, Song Wang, Yuxin Ren, Jianing Deng, Jingtong Hu, Tianlong Chen, Huanrui Yang

SVeritas: Benchmark for Robust Speaker Verification under Diverse Conditions (2025.findings-emnlp)

Challenge:	Key-Value (KV) cache reading latency increases with context lengths hindering LLM inference . important tokens are sparsely distributed across the long context, making existing retrieval inaccurate .
Approach:	They propose a method to retain a small fraction of KV cache based on token importance . important tokens are often sparsely distributed across the long context .
Outcome:	The proposed method reduces decoding latency by 1.2 to 1.5.

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Massa Baali, Sarthak Bisht, Francisco Teixeira, Kateryna Shapovalenko, Rita Singh, Bhiksha Raj

CAARMA: Class Augmentation with Adversarial Mixup Regularization (2025.findings-emnlp)

Challenge:	Existing benchmarks only evaluate a subset of potential conditions, missing others entirely.
Approach:	a new benchmark suite evaluates speaker verification models under a variety of stressors . a san francisco-based team evaluates models under natural and background conditions .
Outcome:	a new benchmark suite evaluates speaker verification models under stressors under a variety of conditions . the results show that some models perform better under stress conditions than others .

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Massa Baali, Xiang Li, Hao Chen, Syed Abdul Hannan, Rita Singh, Bhiksha Raj

Bringing Pedagogy into Focus: Evaluating Virtual Teaching Assistants’ Question-Answering in Asynchronous Learning Environments (2025.findings-emnlp)

Challenge:	Speaker verification tasks require inference of unseen classes using specialized losses.
Approach:	They propose a class augmentation framework that generates synthetic classes through data mixing in the embedding space.
Outcome:	The proposed framework improves speaker verification tasks by 8% over baseline models.

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Li Siyan, Zhen Xu, Vethavikashini Chithrra Raghuram, Xuanming Zhang, Renzhe Yu, Zhou Yu

Demystifying Multilingual Reasoning in Process Reward Modeling (2025.findings-emnlp)

Challenge:	Existing assessments rely on surface-level metrics and lack sufficient grounding in educational theory . a new framework is proposed to evaluate VTAs in asynchronous learning environments .
Approach:	They propose a pedagogically-oriented evaluation framework tailored to asynchronous forum discussions . they construct classifiers using expert annotations of VTA responses on a diverse set of forum posts .
Outcome:	The proposed evaluation framework is rooted in learning sciences and tailored to asynchronous forum discussions.

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Weixuan Wang, Minghao Wu, Barry Haddow, Alexandra Birch

BehaviorSFT: Behavioral Token Conditioning for Health Agents Across the Proactivity Spectrum (2025.findings-emnlp)

Challenge:	a recent study focuses on the use of large language models to solve multi-step reasoning tasks.
Approach:	They propose to extend large language models to multilingual settings by extending process reward models to English . they train multilingual PRMs on a dataset spanning seven languages, which is translated from english .
Outcome:	The proposed model improves accuracy and reduces early-stage reasoning errors.

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Yubin Kim, Zhiyuan Hu, Hyewon Jeong, Eugene W Park, Shuyue Stella Li, Chanwoo Park, Shiyun Xiong, MingYu Lu, Hyeonhoon Lee, Xin Liu, Daniel McDuff, Cynthia Breazeal, Samir Tulebaev, Hae Won Park

Challenge:	Large Language Models (LLMs) struggle with proactive engagement, authors say . a blind clinical evaluation confirmed that trained agents exhibit more realistic clinical behavior .
Approach:	They propose a training strategy using behavioral tokens to explicitly condition LLMs for dynamic behavioral selection.
Outcome:	The proposed training strategy boosts performance on both benchmarks.

LaMP-Cap: Personalized Figure Caption Generation With Multimodal Figure Profiles (2025.findings-emnlp)

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Ho Yin Sam Ng, Edward Hsu, Aashish Anantha Ramakrishnan, Branislav Kveton, Nedim Lipka, Franck Dernoncourt, Dongwon Lee, Tong Yu, Sungchul Kim, Ryan A. Rossi, Ting-Hao Kenneth Huang

Efficient Dynamic Clustering-Based Document Compression for Retrieval-Augmented-Generation (2025.findings-emnlp)

Challenge:	Figure captions are crucial for helping readers understand and remember a figure’s key message.
Approach:	They propose a dataset for personalized figure caption generation with multimodal figure profiles that provide inputs and profiles for each figure .
Outcome:	The proposed dataset provides inputs and profiles for personalized figure caption generation with multimodal figure profiles.

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Weitao Li, Xiangyu Zhang, Kaiming Liu, Xuanyu Lei, Weizhi Ma, Yang Liu

HebID: Detecting Social Identities in Hebrew-language Political Text (2025.findings-emnlp)

Challenge:	RAG implementations face challenges in addressing retrieved noise and redundant content . current RAG methods lack the ability to exploit fine-grained inter-document relationships .
Approach:	They propose a retrieval-augmented generation framework that exploits latent inter-document relationships while removing irrelevant information and redundant content.
Outcome:	The proposed framework achieves consistent performance improvements on knowledge-QA and hallucination-Detection datasets.

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Guy Mor-Lan, Naama Rivlin-Angert, Yael R. Kaplan, Tamir Sheafer, Shaul R. Shenhav

Challenge:	Existing NLP datasets focus on coarse-grained identity categories . existing datasets are mostly English-centric and focus on fine-grain categories based on cultural contexts.
Approach:	They introduce the first multilabel Hebrew corpus for social identity detection . they use Hebrew-tuned encoders alongside 2B-9B-parameter decoders .
Outcome:	The proposed classifier is based on a national public survey and uses Hebrew-tuned encoders to analyze political discourse and political speeches.

Dub-S2ST: Textless Speech-to-Speech Translation for Seamless Dubbing (2025.findings-emnlp)

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Jeongsoo Choi, Jaehun Kim, Joon Son Chung

FinGrAct: A Framework for FINe-GRrained Evaluation of ACTionability in Explainable Automatic Fact-Checking (2025.findings-emnlp)

Challenge:	Existing speech translation approaches often overlook the transfer of speech patterns, leading to mismatches with source speech and limiting their suitability for dubbing applications.
Approach:	They propose a diffusion-based speech-to-unit translation model with explicit duration control that enables time-aligned translation.
Outcome:	The proposed system preserves key characteristics such as duration, speaker identity, and speaking speed while maintaining key characteristics.

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Islam Eldifrawi, Shengrui Wang, Amine Trabelsi

What Has Been Lost with Synthetic Evaluation? (2025.findings-emnlp)

Challenge:	despite the importance of actionability, no prior research has evaluated its effectiveness.
Approach:	They propose a fine-grained evaluation framework that can access the web to assess actionability in AFC explanations.
Outcome:	The proposed framework surpasses state-of-the-art evaluators in achieving highest correlation with human judgments while showing lowest egocentricbias.

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Alexander Gill, Abhilasha Ravichander, Ana Marasovic

Bold Claims or Self-Doubt? Factuality Hallucination Type Detection via Belief State (2025.findings-emnlp)

Challenge:	a recent study evaluated the validity and difficulty of large language models for evaluation benchmarks . large language model evaluation benchmarking is challenging and requires specific phenomena to be addressed .
Approach:	They compare LLM-generated reasoning-over-text benchmarks to those generated through crowdsourcing . they find they are less challenging for LLMs than their human-authored counterparts .
Outcome:	The results show that LLMs can generate variants that are valid according to annotation guidelines, but less challenging than human-authored counterparts.

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Dongyu Zhang, Qingqing Hong, Bingxuan Hou, Jiayi Lin, Chenyang Zhang, Jialin Li, Junli Wang

Proxy Barrier: A Hidden Repeater Layer Defense Against System Prompt Leakage and Jailbreaking (2025.findings-emnlp)

Challenge:	Existing studies focus on detecting the presence of hallucinations but lack a systematic classification approach, which hinders deeper exploration of their characteristics.
Approach:	They propose a method to categorize hallucinations into two types: Overconfident and Unaware .
Outcome:	The proposed method categorizes factuality hallucination into two types: Overconfident and Unaware Hallucinations.

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Pedro Schindler Freire Brasil Ribeiro, Iago Alves Brito, Rafael Teixeira Sousa, Fernanda Bufon Färber, Julia Soares Dollis, Arlindo Rodrigues Galvão Filho

AraSafe: Benchmarking Safety in Arabic LLMs (2025.findings-emnlp)

Challenge:	Prompt injection and jailbreak attacks remain a critical vulnerability for large language models . a lightweight defense that interposes a proxy LLM between the user and the target model addresses this vulnerability .
Approach:	a lightweight proxy LLM is interposed between the user and the target model to prevent prompt injection and jailbreak attacks.
Outcome:	ProB outperforms baselines and achieves up to 98.8% defense effectiveness . it is deployable entirely at the API level and requires no access to model weights or prompts .

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Hamdy Mubarak, Abubakr Mohamed, Majd Hawasly

Nested Named Entity Recognition as Single-Pass Sequence Labeling (2025.findings-emnlp)

Challenge:	AraSafe is the first large-scale native Arabic safety benchmark for large language models (LLMs) it addresses the pressing need for culturally and linguistically representative evaluation resources.
Approach:	They propose to use Arabic prompts to annotate harmful and non-harmful prompts into nine fine-grained safety categories to support classifiers for harmful content.
Outcome:	The proposed benchmarks address the need for culturally and linguistically representative evaluation resources.

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Alberto Muñoz-Ortiz, David Vilares, Caio Corro, Carlos Gómez-Rodríguez

DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate Hallucinations (2025.findings-emnlp)

Challenge:	nested named entity recognition is a sequence labeling task that can be trained using any off-the-shelf sequence labelling library.
Approach:	They use prior work that linearizes constituency structures to create a nested named entity recognition task.
Outcome:	The proposed method reduces the complexity of the predicted nested entity recognition problem to a simple token classification task.

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Aryo Pradipta Gema, Chen Jin, Ahmed Abdulaal, Tom Diethe, Philip Alexander Teare, Beatrice Alex, Pasquale Minervini, Amrutha Saseendran

Catch Me If You Can? Not Yet: LLMs Still Struggle to Imitate the Implicit Writing Styles of Everyday Authors (2025.findings-emnlp)

Challenge:	Large Language Models often produce unfaithful or factually incorrect outputs . masking retrieval heads can induce hallucinations, but decoding by contrast can reduce hallucinosity .
Approach:	They propose a training-free decoding strategy that contrasts the outputs of the base LLM and the masked LLM.
Outcome:	The proposed decoding strategy reduces hallucinations by contrasting the outputs of the base and masked LLMs.

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Zhengxiang Wang, Nafis Irtiza Tripto, Solha Park, Zhenzhen Li, Jiawei Zhou

Fine-Tuning Encoder-Decoder Models with Contrastive Learning for In-Context Distractor Generation (2025.findings-emnlp)

Challenge:	Personal style is often subtle and implicit, making it difficult to specify through prompts yet essential for user-aligned generation.
Approach:	They evaluate LLMs' ability to imitate personal writing styles via in-context learning from user-authored samples.
Outcome:	The proposed model can imitate personal writing styles from a small number of user-authored samples.

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Elaf Alhazmi, Quan Z. Sheng, Wei Emma Zhang, Mohammed I. Thanoon, Haojie Zhuang, Behnaz Soltani, Munazza Zaib

Conflicts in Texts: Data, Implications and Challenges (2025.findings-emnlp)

Challenge:	Distractors are used to generate plausible but incorrect options for fill-in-the-blank questions . research studies focus on fine-tuning pre-trained models with data augmentation techniques to generate distractors .
Approach:	They propose a model that trains the model to recognize essential semantic features necessary to generate distractors.
Outcome:	The proposed model outperforms existing models on two public datasets.

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Siyi Liu, Dan Roth

Recognizing Limits: Investigating Infeasibility in Large Language Models (2025.findings-emnlp)

Challenge:	Conflicts in data could reflect complexity of situations, changes that need to be explained and dealt with, difficulties in data annotation, and mistakes in generated outputs.
Approach:	This survey categorizes conflicting information into three key areas . they identify the areas where conflicting data can be ignored and undermine models' reliability and trustworthiness.
Outcome:	The findings highlight key challenges and future directions for developing conflict-aware NLP systems that can reason over and reconcile conflicting information more effectively.

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Wenbo Zhang, Zihang Xu, Hengrui Cai

VQA-Augmented Machine Translation with Cross-Modal Contrastive Learning (2025.findings-emnlp)

Challenge:	Large language models fail to handle queries that exceed their knowledge and capabilities, leading to incorrect or fabricated responses.
Approach:	They conceptualize four main categories of infeasible tasks for LLMs which cover a broad spectrum of hallucination-related challenges identified in prior literature.
Outcome:	The proposed models can handle requests exceeding their knowledge and capabilities and refuse them .

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Zhihui Zhang, Shiliang Sun, Jing Zhao, Tengfei Song, Hao Yang

Learning to Describe Implicit Changes: Noise-robust Pre-training for Image Difference Captioning (2025.findings-emnlp)

Challenge:	Existing multimodal machine translation methods often extract visual features using pre-trained models while learning text features from scratch, leading to representation imbalance.
Approach:	They propose a cross-modal VQA-augmented multimodal machine translation method . it aligns image-source text pairs and image-question text pairs through dual-text contrastive learning .
Outcome:	The proposed method outperforms state-of-the-art methods on multiple evaluation metrics.

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Zixin Guo, Jiayang Sun, Tzu-Jui Julius Wang, Abduljalil Radman, Selen Pehlivan, Min Cao, Jorma Laaksonen

SOLAR: Serendipity Optimized Language Model Aligned for Recommendation (2025.findings-emnlp)

Challenge:	Large Multimodal Models (LMMs) are used to capture subtle differences between images but are noisy and coarse summaries.
Approach:	They propose a noise-robust approach to image difference capture using large multimodal models . they use LMMs with structured prompts to generate fine-grained change descriptions .
Outcome:	The proposed model outperforms streamlined architectures and improves inference efficiency.

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Zichen Yuan, Lifan Sun, Yucen Zhuang, Yue Wang, Xinyuan Song, Tianqi Xu, Siyuan Li, Junchen Fu, Youhua Li, Sirui Hong, Jiaqi Chen, Joemon M. Jose, Yongxin Ni

AIRepr: An Analyst-Inspector Framework for Evaluating Reproducibility of LLMs in Data Science (2025.findings-emnlp)

Challenge:	Large Language Models have shown strong potential in recommendation tasks . however, their application to serendipity-oriented recommendations remains challenging .
Approach:	They propose a domain-adaptive instruction tuning method that aligns Large Language Models with recommendation tasks.
Outcome:	The proposed framework bridges the domain gap between LLMs and recommendation tasks.

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Qiuhai Zeng, Claire Jin, Xinyue Wang, Yuhan Zheng, Qunhua Li

MisinfoBench: A Multi-Dimensional Benchmark for Evaluating LLMs’ Resilience to Misinformation (2025.findings-emnlp)

Challenge:	Large language models are increasingly used to automate data analysis, but data science tasks often admit multiple statistically valid solutions.
Approach:	They propose a framework to evaluate LLM-generated code and assess its reproducibility . they introduce two reproducibility-enhancing prompting strategies and benchmark them against standard prompting .
Outcome:	The proposed framework improves reproducibility of large language models . it provides a foundation for transparent, reliable, and efficient human–AI collaboration in data science.

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Ye Yang, Donghe Li, Zuchen Li, Fengyuan Li, Jingyi Liu, Li Sun, Qingyu Yang

Fuzzy Reasoning Chain (FRC): An Innovative Reasoning Framework from Fuzziness to Clarity (2025.findings-emnlp)

Challenge:	Existing benchmarks assess factual accuracy in isolated queries but fail to evaluate LLMs’ resilience to misinformation in interactive settings.
Approach:	MisinfoBench is a benchmark designed to assess LLMs’ ability to discern, resist, and reject misinformation.
Outcome:	MisinfoBench assesses large language models’ ability to discern, resist, and reject misinformation in interactive settings.

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Ping Chen, Xiang Liu, Zhaoxiang Liu, Zezhou Chen, Xingpeng Zhang, Huan Hu, Zipeng Wang, Kai Wang, Shuming Shi, Shiguo Lian

HighMATH: Evaluating Math Reasoning of Large Language Models in Breadth and Depth (2025.findings-emnlp)

Challenge:	ambiguity, polysemy, or uncertainty remain significant challenges in natural language processing.
Approach:	They introduce a framework that integrates LLM semantic priors with continuous fuzzy membership degrees to create an explicit interaction between probability-based reasoning and fuzzy membership reasoning.
Outcome:	The proposed framework integrates semantic priors with continuous fuzzy membership degrees . it allows ambiguous inputs to be gradually transformed into clear and interpretable decisions .

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Yan Liu, Minghui Zhang, Bojian Xiong, Yifan Xiao, Yinong Sun, Yating Mei, Longyu Zeng, Jingchao Yang, Yang Wang, Deyi Xiong

CATCH: A Novel Data Synthesis Framework for High Therapy Fidelity and Memory-Driven Planning Chain of Thought in AI Counseling (2025.findings-emnlp)

Challenge:	a gap in math models' accuracy has been widened with the development of large language models (LLMs) . a new study aims to bridge this gap by evaluating a set of high-level math reasoning models .
Approach:	They propose to evaluate large language models on existing math benchmarks to bridge this gap . they collect 5,293 problems from Chinese senior high school mathematics exams .
Outcome:	The proposed model is based on o1-like models and a high-level model.

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Mingyu Chen, Jingkai Lin, Zhaojie Chu, Xiaofen Xing, Yirong Chen, Xiangmin Xu

MediVLM: A Vision Language Model for Radiology Report Generation from Medical Images (2025.findings-emnlp)

Challenge:	Existing studies employ a one-time generation approach to synthesize multi-turn dialogue samples, resulting in low therapy fidelity and failing to capture decision-making rationale behind each response.
Approach:	They propose a data synthesis framework that synthesizes multi-turn dialogue samples and incrementally generates stage-aligned counseling dialogues.
Outcome:	The proposed framework significantly improves therapy fidelity and logical coherence in AI counseling.

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Debanjan Goswami, Ronast Subedi, Shayok Chakraborty

AdDriftBench: A Benchmark for Detecting Data Drift and Label Drift in Short Video Advertising (2025.findings-emnlp)

Challenge:	Existing methods for radiology report generation from medical images are incomplete and inconsistent, fail to focus on informative regions within an image and impose strong annotation assumptions for model training.
Approach:	They propose a vision language model (VLM) for radiology report generation from medical images that uses a pre-trained object detector to extract the salient anatomical regions from images, an image encoder, a text encoder and a transformer based decoder to generate the final report.
Outcome:	The proposed model generates radiology reports even when no reports are available for training.

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Yinghao Song, Xiangji Zeng, Shuai Cui, Lu Sun, Zhaowei Liu, Yuan Yuan, Yulu Wang, Hai Zhou, Zhaohan Gong

NIM: Neuro-symbolic Ideographic Metalanguage for Inclusive Communication (2025.findings-emnlp)

Challenge:	Short video advertising scenarios present unique challenges due to data drift (DD) and label drift (LD).
Approach:	They propose to use data drift and label drift to evaluate models under rapidly shifting content distributions and labeling scenarios to assess their generalization capabilities.
Outcome:	The proposed model performs moderately in short video advertising contexts, particularly in handling fine-grained semantics and adapting to shifting instructions.

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Prawaal Sharma, Poonam Goyal, Navneet Goyal, Vidisha Sharma

Linguistic Theories

Challenge:	ideographic metalanguage is a communication framework that transcends academic, linguistic, and cultural boundaries.
Approach:	They propose a universal ideographic metalanguage that leverages neuro-symbolic AI to create a system that transcends academic, linguistic, and cultural boundaries.
Outcome:	The proposed system transcends academic, linguistic, and cultural boundaries and enables semantic decomposition of complex ideas into simpler, atomic concepts.

ViFT: Towards Visual Instruction-Free Fine-tuning for Large Vision-Language Models (2025.findings-emnlp)

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Zikang Liu, Kun Zhou, Xin Zhao, Dawei Gao, Yaliang Li, Ji-Rong Wen

Do Code Semantics Help? A Comprehensive Study on Execution Trace-Based Information for Code Large Language Models (2025.findings-emnlp)

Challenge:	Visual instruction tuning is the predominant technology in eliciting multimodal task-solving capabilities of large vision-language models.
Approach:	They propose a visual instruction-free fine-tuning framework for large vision-language models . they require only text-only instructions and image caption data during training .
Outcome:	The proposed framework is based on visual instruction tuning, but requires images as input . it can achieve state-of-the-art performance on several downstream benchmarks with less training data.

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Jian Jornbowrl Wang, Xiaofei Xie, Qiang Hu, Shangqing Liu, Yi Li

LongWeave: A Long-Form Generation Benchmark Bridging Real-World Relevance and Verifiability (2025.findings-emnlp)

Challenge:	Code Large Language Models have limited ability to reason about runtime behavior and understand functionality . authors present a generic framework to support integrating semantic information to code task-relevant prompts .
Approach:	a study examines the role of trace-based semantic information in boosting supervised fine-tuning and post-phase inference of Code LLMs.
Outcome:	a new framework integrates semantic information to code task-relevant prompts . the proposed framework shows that trace-based semantic information boosts reasoning ability .

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Zikai Xiao, Fei Huang, Jianhong Tu, Jianhui Wei, Wen Ma, Yuxuan Zhou, Jian Wu, Bowen Yu, Zuozhu Liu, Junyang Lin

Challenge:	Existing benchmarks for long-form generation assess real-world queries with hard-to-verify metrics or use synthetic setups that overlook real-life intricacies.
Approach:	They propose a new approach that balances verifiable and real-world assessment with Target-Anchored Evaluation.
Outcome:	The proposed model balances real-world and verifiable assessment with Target-Anchored Evaluation (TAE) it generates queries, textual materials, and anchors based on verifier targets within real-life scenarios .

XL-Suite: Cross-Lingual Synthetic Training and Evaluation Data for Open-Ended Generation (2025.findings-emnlp)

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Vivek Iyer, Pinzhen Chen, Ricardo Rei, Alexandra Birch

Accelerating LLM Reasoning via Early Rejection with Partial Reward Modeling (2025.findings-emnlp)

Challenge:	Cross-lingual open-ended generation is an important yet understudied problem.
Approach:	They propose XL-Instruct, a novel technique for generating high-quality synthetic data, and introduce Xl-AlpacaEval, evaluating cross-lingual generation capabilities of large language models.
Outcome:	The proposed technique improves model performance by fine tuning with just 8K instructions generated using XL-Instruct, and also by improving on several fine-grained quality metrics.

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Seyyed Saeid Cheshmi, Azal Ahmad Khan, Xinran Wang, Zirui Liu, Ali Anwar

CultureSynth: A Hierarchical Taxonomy-Guided and Retrieval-Augmented Framework for Cultural Question-Answer Synthesis (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) are increasingly relied upon for solving complex reasoning tasks.
Approach:	They propose to use Process Reward Models to scale inference time compute by generating in parallel . they propose to provide early signals that enable the rejection of suboptimal candidates before full generation of step is complete.
Outcome:	The proposed method achieves 1.4 – 9 reduction in inference FLOPs without degrading final performance.

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Xinyu Zhang, Pei Zhang, Shuang Luo, Jialong Tang, Yu Wan, Baosong Yang, Fei Huang

DesignCLIP: Multimodal Learning with CLIP for Design Patent Understanding (2025.findings-emnlp)

Challenge:	Cultural competence is defined as the ability to understand and adapt to multicultural contexts.
Approach:	They propose a framework that uses a hierarchical multilingual taxonomy and a Retrieval-Augmented Generation to synthesize culturally relevant question-answer pairs.
Outcome:	The proposed framework contains a hierarchical multilingual taxonomy covering 12 primary and 130 secondary topics and a Retrieval-Augmented Generation (RAG)-based methodology leveraging factual knowledge to synthesize culturally relevant question-answer pairs.

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Zhu Wang, Homaira Huda Shomee, Sathya N. Ravi, Sourav Medya

R3-RAG: Learning Step-by-Step Reasoning and Retrieval for LLMs via Reinforcement Learning (2025.findings-emnlp)

Challenge:	patent images often lack comprehensive visual context and semantic information, authors say . recent advances in vision-language models offer promising opportunities for patent analysis .
Approach:	They develop a framework for design patent analysis using large-scale patent dataset . they validate the effectiveness of DesignCLIP across various downstream tasks .
Outcome:	The proposed framework outperforms baseline and SOTA models on all tasks.

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Yuan Li, Qi Luo, Xiaonan Li, Bufan Li, Qinyuan Cheng, Bo Wang, Yining Zheng, Yuxin Wang, Zhangyue Yin, Xipeng Qiu

‘Hello, World!’: Making GNNs Talk with LLMs (2025.findings-emnlp)

Challenge:	RAG systems that integrate external knowledge with Large Language Models often become bottlenecks due to their limited parameters compared to LLMs and their inability to perform step-by-step reasoning.
Approach:	They propose a model that integrates external knowledge with Large Language Models to enhance factual correctness and mitigate hallucination.
Outcome:	The proposed model outperforms baselines and can transfer well to different retrievers.

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Sunwoo Kim, Soo Yong Lee, Jaemin Yoo, Kijung Shin

Both Text and Images Leaked! A Systematic Analysis of Data Contamination in Multimodal LLM (2025.findings-emnlp)

Challenge:	graph neural networks have shown remarkable performance across diverse graph-related tasks, but their high-dimensional hidden representations render them black boxes.
Approach:	They propose a graph-based neural network with hidden representations in the form of human-readable text.
Outcome:	The proposed GNN outperforms existing LLM-based baseline methods on node classification and link prediction.

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Dingjie Song, Sicheng Lai, Mingxuan Wang, Shunian Chen, Lichao Sun, Benyou Wang

NLKI: A Lightweight Natural Language Knowledge Integration Framework for Improving Small VLMs in Commonsense VQA Tasks (2025.findings-emnlp)

Challenge:	Existing methods for unimodal large language models are inadequate for MLLMs due to multimodal data complexity and multi-phase training.
Approach:	MM-DETECT analyzes data contamination using a framework that defines two contamination categories - unimodal and cross-modal .
Outcome:	The proposed framework quantifies contamination severity across multiple-choice and caption-based Visual Question Answering tasks.

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Aritra Dutta, Swapnanil Mukherjee, Deepanway Ghosal, Somak Aditya

Text or Pixels? Evaluating Efficiency and Understanding of LLMs with Visual Text Inputs (2025.findings-emnlp)

Challenge:	Small vision-language models lag behind their larger generative counterparts due to lack of knowledge.
Approach:	They propose a framework that integrates commonsense knowledge into small vision-language models . the framework retrieves natural language facts and prompts an LLM to craft natural language explanations .
Outcome:	The proposed framework retrieves natural language facts and prompts an LLM to craft natural language explanations.

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Yanhong Li, Zixuan Lan, Jiawei Zhou

Assessing Socio-Cultural Alignment and Technical Safety of Sovereign LLMs (2025.findings-emnlp)

Challenge:	visual text representations are a practical and surprisingly effective form of input compression for decoder LLMs.
Approach:	They exploit visual representations to render long text inputs as a single image and provide it directly to the model.
Outcome:	The proposed method reduces token usage while preserving performance.

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Kyubyung Chae, Gihoon Kim, Gyuseong Lee, Taesup Kim, Jaejin Lee, Heejin Kim

Sample Efficient Alignment Learning With Episodic Control (2025.findings-emnlp)

Challenge:	Recent trends in LLMs development show growing interest in the use and application of sovereign LLM models.
Approach:	They propose a framework for extracting and evaluating socio-cultural elements of sovereign LLMs and assess their technical robustness.
Outcome:	The proposed framework assesses the socio-cultural elements of sovereign LLMs and their technical robustness.

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Van Dai Do, Quan Hung Tran, Ahmed Kirmani, Lu Zhang, Hung Le

Evaluating Automatic Speech Recognition Systems for Korean Meteorological Experts (2025.findings-emnlp)

Challenge:	Existing parametric methods for aligning large language models with task objectives are limited.
Approach:	They propose a non-parametric framework that aligns large language models with task objectives . they use a key-value memory to store associations between generated text and its corresponding values .
Outcome:	The proposed framework outperforms state-of-the-art baselines on harmless, helpful, and summarization tasks.

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ChaeHun Park, Hojun Cho, Jaegul Choo

3D-Aware Vision-Language Models Fine-Tuning with Geometric Distillation (2025.findings-emnlp)

Challenge:	Using a dataset of Korean weather queries, we find that automatic speech recognition systems fail on specialized vocabulary.
Approach:	They propose an evaluation dataset of Korean weather queries . the dataset was recorded by diverse native speakers following pronunciation guidelines .
Outcome:	The proposed model reduces error rates on meteorological terms and improves overall recognition accuracy.

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Seonho Lee, Jiho Choi, Inha Kang, Jiwook Kim, Junsung Park, Hyunjung Shim

CAPE: Context-Aware Personality Evaluation Framework for Large Language Models (2025.findings-emnlp)

Challenge:	Vision-Language Models (VLMs) have shown remarkable performance on diverse visual and linguistic tasks, yet they remain limited in their understanding of 3D spatial structures.
Approach:	They propose a framework that injects human-inspired geometric cues into pretrained VLMs . they use sparse correspondences, relative depth relations and dense cost volumes .
Outcome:	The proposed framework outperforms existing methods on vision-language reasoning and 3D perception benchmarks.

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Jivnesh Sandhan, Fei Cheng, Tushar Sandhan, Yugo Murawaki

AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving (2025.findings-emnlp)

Challenge:	Existing studies use a context-free approach to assess humans . existing studies use the Disney World test, which ignores real-world applications .
Approach:	They propose a framework to assess personality traits in large language models . they use conversational history to quantify the consistency of LLM responses .
Outcome:	The proposed framework improves consistency of responses in large language models . it also shows that conversational history enhances consistency and personality shifts .

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Kangan Qian, Sicong Jiang, Yang Zhong, Ziang Luo, Zilin Huang, Tianze Zhu, Kun Jiang, Mengmeng Yang, Zheng Fu, Jinyu Miao, Yining Shi, He Zhe Lim, Li Liu, Tianbao Zhou, Hongyi Wang, Huang Yu, Yifei Hu, Guang Li, Guang Chen, Hao Ye, Lijun Sun, Diange Yang

Select to Know: An Internal-External Knowledge Self-Selection Framework for Domain-Specific Question Answering (2025.findings-emnlp)

Challenge:	Vision-Language Models struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate perception and robust step-by-step reasoning.
Approach:	AgentThink integrates Chain-of-Thought reasoning with dynamic, agent-style tool invocation for autonomous driving tasks.
Outcome:	Experiments on the DriveLMM-o1 benchmark show AgentThink significantly boosts overall reasoning scores by 53.91% and enhances answer accuracy by 33.54% .

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Bolei He, Xinran He, Run Shao, Shanfu Shu, Xianwei Xue, MingQuan Cheng, Haifeng Li, Zhen-Hua Ling

Knowledge Representation

Challenge:	Large Language Models (LLMs) perform well in general QA but often struggle in domain-specific scenarios.
Approach:	They propose a framework that internalizes domain knowledge through internal-external knowledge self-selection and selective supervised fine-tuning.
Outcome:	The proposed framework outperforms existing methods and matches domain-pretrained LLMs with significantly lower cost.

GenPTQ: Green Post-Training Quantization for Large-Scale ASR Models with Mixed-Precision Bit Allocation (2025.findings-emnlp)

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Beom Jin Kang, Hyun Kim

“Where Does This Strange Smell Come from?”: Enabling Conversational Interfaces for Artificial Olfaction (2025.findings-emnlp)

Challenge:	Large-scale models with high memory and computation demands face significant challenges for deployment.
Approach:	They propose a weight-only quantization method that optimizes for weights and QAT to restore accuracy.
Outcome:	The proposed method achieves 89.1% model size reduction with only 0.8% increase in WER and completes optimization in just 15 seconds.

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Xueyi Zhou, Qi Lu, Dong-Kyu Chae

LightRAG: Simple and Fast Retrieval-Augmented Generation (2025.findings-emnlp)

Challenge:	Existing Artificial Olfaction (AO) systems are not compatible with smart home scenarios due to diverse obstacles and the need for natural interaction.
Approach:	They propose to use large language models to train a CIAO system for Odor Classification and Odor Source Localization in smart home scenarios.
Outcome:	The proposed system outperforms existing systems in indoor event detection scenarios.

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Zirui Guo, Lianghao Xia, Yanhua Yu, Tu Ao, Chao Huang

Beyond Distribution: Investigating Language Models’ Understanding of Sino-Korean Morphemes (2025.findings-emnlp)

Challenge:	Existing RAG systems rely on flat data representations and inadequate contextual awareness . lightRAG framework incorporates graph structures into text indexing and retrieval processes .
Approach:	LightRAG is a framework that integrates graph structures into text indexing and retrieval processes.
Outcome:	The proposed framework incorporates graph structures into text indexing and retrieval processes.

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Taehee Jeon

Sarcasm-R1: Enhancing Sarcasm Detection through Focused Reasoning (2025.findings-emnlp)

Challenge:	Transformer-based language models can learn compositional morphology of SK morphemes . morphological models trained on Hangul text can learn SK, but performance is based on frequency of words .
Approach:	They investigate whether Transformer-based language models can learn compositional morphology of Sino-Korean morphemes.
Outcome:	The proposed models learn the compositional morphology of SK morphemes from real and fake pairs.

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Qi Yang, Jingjie Zeng, Liang Yang, Kai Ma, Hongfei Lin

ISACL: Internal State Analyzer for Copyrighted Training Data Leakage (2025.findings-emnlp)

Challenge:	Existing methods for sarcasm detection are limited by supervised learning or prompt engineering . a new approach decomposes sarcasm detection into three dimensions: language, context, and emotion .
Approach:	They propose a method that decomposes sarcasm detection into three dimensions: language, context, and emotion.
Outcome:	The proposed method outperforms state-of-the-art methods in most cases.

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Guangwei Zhang, Qisheng Su, Jiateng Liu, Cheng Qian, Yanzhou Pan, Yanjie Fu, Denghui Zhang

Steering LVLMs via Sparse Autoencoder for Hallucination Mitigation (2025.findings-emnlp)

Challenge:	Traditional methods address leaks only after content is generated, which can lead to the exposure of sensitive information.
Approach:	They propose a proactive approach: examining LLMs’ internal states before text generation to detect potential leaks.
Outcome:	The proposed framework ensures adherence to copyright and licensing requirements while enhancing data privacy and ethical standards.

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Zhenglin Hua, Jinghan He, Zijun Yao, Tianxu Han, Haiyun Guo, Yuheng Jia, Junfeng Fang

On the Perception Bottleneck of VLMs for Chart Understanding (2025.findings-emnlp)

Challenge:	Existing approaches to address hallucinations in large vision-language models require substantial computational cost and time.
Approach:	They propose to leverage sparse autoencoders to identify semantic directions closely associated with faithfulness or hallucination, extracting more precise and disentangled hallucinian-related representations.
Outcome:	The proposed method outperforms existing decoding approaches while maintaining transferability across different model architectures with negligible additional time overhead.

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Junteng Liu, Weihao Zeng, Xiwen Zhang, Yijun Wang, Zifei Shan, Junxian He

Self-Guided Function Calling in Large Language Models via Stepwise Experience Recall (2025.findings-emnlp)

Challenge:	a perception bottleneck in large vision-language models is critical for chart understanding . instruction tuning improves the extraction capability of LVLMs, but the vision encoder remains a bottleneck .
Approach:	They propose to decompose the perception bottleneck into two components . the vision encoder bottleneck is where visual representation fails to encapsulate the correct information .
Outcome:	The proposed approach significantly mitigates the vision encoder bottleneck and improves the ability of LVLMs to comprehend charts.

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Sijia Cui, Aiyao He, Shuai Xu, Hongming Zhang, Yanna Wang, Qingyang Zhang, Yajing Wang, Bo Xu

Multilingual Generative Retrieval via Cross-lingual Semantic Compression (2025.findings-emnlp)

Challenge:	Existing methods for function calling require expert effort and prompt engineering becomes inefficient.
Approach:	They propose a method that performs fine-grained, stepwise retrieval from a continually updated experience pool.
Outcome:	The proposed method achieves an average improvement of 6.1% on easy and 4.7% on hard questions.

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Yuxin Huang, Simeng Wu, Ran Song, Yan Xiang, Yantuan Xian, Shengxiang Gao, Zhengtao Yu

Towards Multi-Document Question Answering in Scientific Literature: Pipeline, Dataset, and Evaluation (2025.findings-emnlp)

Challenge:	Existing methods for multilingual retrieval still face cross-lingual identifier misalignment and identifiere inflation.
Approach:	They propose a framework that unifies semantically equivalent multilingual keywords into shared atoms to align semantics and compresses the identifier space.
Outcome:	The proposed framework improves cross-lingual alignment and reduces redundancy.

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Hui Huang, Julien Velcin, Yacine Kessaci

Multilingual Knowledge Graph Completion via Efficient Multilingual Knowledge Sharing (2025.findings-emnlp)

Challenge:	Existing QA systems do not strictly enforce cross-document synthesis or exploit the explicit inter-paper structure that links sources.
Approach:	They propose a pipeline methodology for constructing a multi-document academic QA dataset . they detect communities based on citation networks and leverage Large Language Models .
Outcome:	The proposed method generates QA pairs related to multi-document content automatically and forms coherent communities based on citation networks and large language models.

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Cunli Mao, Xiaofei Gao, Ran Song, Shizhu He, Shengxiang Gao, Kang Liu, Zhengtao Yu

Mitigating Attention Localization in Small Scale: Self-Attention Refinement via One-step Belief Propagation (2025.findings-emnlp)

Challenge:	Existing MKGC research ignores the shareability of cross-lingual knowledge.
Approach:	They propose a multilingual knowledge Graph Completion framework that leverages multilingual shared knowledge to significantly enhance performance through two components: Knowledge-level Grouped Mixture of Experts (KL-GMoE) and Iterative Entity Reranking (IER).
Outcome:	The proposed framework achieves improvements of 5.47%, 3.27%, and 1.01% in the Hits@1, Hits @3, and Hits_10 metrics, respectively, compared with existing state-of-the-art (SOTA) MKGC method.

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Nakyung Lee, Yeongoon Kim, Minhae Oh, Suhwan Kim, Jin Woo Koo, Hyewon Jo, Jungwoo Lee

Imagination and Contemplation: A Balanced Framework for Semantic-Augmented Multimodal Machine Translation (2025.findings-emnlp)

Challenge:	a new framework for self-attention models is proposed to address this problem . it injects multi-hop relationships into the attention graph, allowing for better performance .
Approach:	They propose a framework that injects multi-hop relationships through a belief propagation process.
Outcome:	The proposed framework helps prevent entropy collapse in deeper layers and maintains GTD at task-appropriate levels.

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Zhuang Yu, Shiliang Sun, Jing Zhao, Tengfei Song, Hao Yang

NeLLCom-Lex: A Neural-agent Framework to Study the Interplay between Lexical Systems and Language Use (2025.findings-emnlp)

Challenge:	Multimodal Machine Translation (MMT) is effective in resolving linguistic ambiguities, but visual information often introduces redundancy or noise, potentially impairing translation quality.
Approach:	They propose a semantic-augmented framework that integrates "Imagination" and "Contemplation" they first generate synthetic images from source text and align them with authentic images via an optimal transport loss .
Outcome:	The proposed framework outperforms baselines on translation datasets with visually ambiguous or weakly correlated content.

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Yuqing Zhang, Ecesu Ürker, Tessa Verhoef, Gemma Boleda, Arianna Bisazza

Challenge:	Lexical semantic change has been investigated with observational and experimental methods, but observational methods cannot get at causal mechanisms.
Approach:	They introduce a neural-agent framework designed to simulate semantic change by first grounding agents in a real lexical system and then manipulating their communicative needs.
Outcome:	The proposed framework simulates the evolution of a lexical system within a single generation by grounding agents in a real lexicon and manipulating their communicative needs.

RLMEval: Evaluating Research-Level Neural Theorem Proving (2025.findings-emnlp)

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Auguste Poiroux, Antoine Bosselut, Viktor Kunčak

KaeDe: Progressive Generation of Logical Forms via Knowledge-Aware Question Decomposition for Improved KBQA (2025.findings-emnlp)

Challenge:	RLMEval evaluates large language models for research-level neural theorem proving and proof autoformalization . the best model achieves only a 10.3% pass rate on existing benchmarks .
Approach:	They propose a new evaluation suite for large language models . it evaluates research-level theorems from real-world Lean formalization projects .
Outcome:	RLMEval evaluates research-level theorems from real-world Lean formalization projects.

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Ranran Bu, Jian Cao, Jianqi Gao, Shiyou Qian, Hongming Cai

Where Fact Ends and Fairness Begins: Redefining AI Bias Evaluation through Cognitive Biases (2025.findings-emnlp)

Challenge:	Existing methods for answering natural language questions are difficult to generate . lack of a logical form for complex graphs can negatively impact overall performance .
Approach:	They propose a generate-then-retrieve method that converts questions into structured LF queries . they propose to combine knowledge-aware question decomposition and progressive LF generation .
Outcome:	The proposed method achieves state-of-the-art (SOTA) performance on WebQuestionSP and ComplexWebQuestions benchmarks.

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Jen-tse Huang, Yuhang Yan, Linqi Liu, Yixin Wan, Wenxuan Wang, Kai-Wei Chang, Michael R. Lyu

Equal Truth: Rumor Detection with Invariant Group Fairness (2025.findings-emnlp)

Challenge:	Existing benchmarks conflate factual correctness and normative fairness . a model may generate responses that are factually accurate but socially unfair .
Approach:	They propose a benchmark to examine the boundary between fact and fair . they draw on representativeness bias, attribution bias and ingroup–outgroup bias to explain why models often misalign fact and faireness.
Outcome:	The proposed model is based on ten frontier models and is available on github . it is compared with a standard model that generates people of color in Nazi-era uniforms .

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Junyi Chen, Mengjia Wu, Qian Liu, Jing Sun, Ying Ding, Yi Zhang

STEAM: A Semantic-Level Knowledge Editing Framework for Large Language Models (2025.findings-emnlp)

Challenge:	Existing rumor detection methods rarely consider fairness issues inherent in the model . this can lead to biased predictions across stakeholder groups, undermining their detection effectiveness .
Approach:	They propose a framework to address fairness issues inherent in rumor detection models . they perform unsupervised partitioning to dynamically identify potential unfair data patterns . then, they apply invariant learning to these partitions to extract fair and informative feature representations .
Outcome:	The proposed method outperforms strong baselines regarding detection and fairness performance . it also shows robust performance on out-of-distribution samples .

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Geunyeong Jeong, Juoh Sun, Seonghee Lee, Harksoo Kim

SoT: Structured-of-Thought Prompting Guides Multilingual Reasoning in Large Language Models (2025.findings-emnlp)

Challenge:	Existing methods for locate-and-editing focus on token-level likelihood optimization without addressing semantic coherence.
Approach:	They propose a semantic-level knowledge editing framework that enhances integration of updated knowledge into the model's knowledge structure.
Outcome:	The proposed framework improves integration of updated knowledge into the model's knowledge structure and improves semantic coherence.

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Rui Qi, Zhibo Man, Yufeng Chen, Fengran Mo, Jinan Xu, Kaiyu Huang

How Reliable is Multilingual LLM-as-a-Judge? (2025.findings-emnlp)

Challenge:	Large Language Models struggle with multilingual reasoning tasks due to resource constraints . a training-free method improves performance on multilingual thinking tasks .
Approach:	They propose a training-free method that transforms language-specific semantic information into language-agnostic structured representations.
Outcome:	The proposed method outperforms strong baselines on multilingual reasoning tasks.

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Xiyan Fu, Wei Liu

Cognitive-Level Adaptive Generation via Capability-Aware Retrieval and Style Adaptation (2025.findings-emnlp)

Challenge:	LLMs are a popular evaluation strategy, but their reliability in multilingual evaluation remains uncertain.
Approach:	They evaluate five models from different model families across five diverse tasks involving 25 languages.
Outcome:	The models perform poorly across languages and average Fleiss’ Kappa is 0.3 .

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Qingsong Wang, Tao Wu, Wang Lin, Yueying Feng, Gongsheng Yuan, Chang Yao, Jingyuan Chen

Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs (2025.findings-emnlp)

Challenge:	Large Language Models struggle to adapt content to users with differing cognitive capacities, leading to cognitive misalignment.
Approach:	They propose a cognitive-level alignment framework that aligns both knowledge complexity and presentation style with user cognition.
Outcome:	The proposed framework aligns knowledge complexity and presentation style with user cognition.

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Essa Jan, Moiz Ali, Muhammad Saram Hassan, Muhammad Fareed Zaffar, Yasir Zaki

INDOORWORLD : Integrating Physical Task Solving and Social Simulation in A Heterogeneous Multi-Agent Environment (2025.findings-emnlp)

Challenge:	a study shows that comprehension-intensive fine-tuning tasks retain knowledge longer . however, all models exhibit significant performance drops when applying injected knowledge in broader contexts .
Approach:	study: comprehension-intensive fine-tuning tasks achieve higher knowledge retention rates . larger models show improved retention across all task types, study finds .
Outcome:	a new study shows that comprehension-intensive fine-tuning tasks retain knowledge better than mapping-oriented tasks despite exposure to identical factual content.

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Dekun Wu, Frederik Brudy, Bang Liu, Yi Wang

Challenge:	Existing virtual environments for LLM agent research focus on task solving or social simulation . existing environments for virtual environments lack physical grounding of social behaviors .
Approach:	They propose a virtual environment that tightly integrates physical and social dynamics . IndoorWorld is a heterogeneous multi-agent environment that integrates social and physical dynamics based on a simulation of physical environments .
Outcome:	The proposed environment integrates physical and social dynamics into a heterogeneous multi-agent environment.

ARXSA: A General Negative Feedback Control Theory in Vision-Language Models (2025.findings-emnlp)

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Zeyu Zhang, Tianqi Chen, Yuki Todo

Breaking the Attention Trap in Code LLMs: A Rejection Sampling Approach to Enhance Code Execution Prediction (2025.findings-emnlp)

Challenge:	a new approach to the self-attention mechanism is proposed for integrating data from multiple batches.
Approach:	They propose an autoregressive with exogenous inputs approach for the Transformer model . the proposed method transforms the Encoder block into a negative feedback predictive control system .
Outcome:	The proposed method is validated through comparative evaluations.

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Xingcheng Ruan, Haoxiang Geng, Yunhui Xia, Bingran Zhao

HiMATE: A Hierarchical Multi-Agent Framework for Machine Translation Evaluation (2025.findings-emnlp)

Challenge:	Existing research reveals significant performance bottlenecks in Code Execution tasks, which requires models to predict the execution results of given code snippets.
Approach:	They propose to use attention cracking with rejection sampling to eliminate attention traps in training data and to conduct secondary training on the outputs generated by the fine-tuned model to mitigate negative impacts from manual data intervention.
Outcome:	The proposed method eliminates the Attention Trap phenomenon in training data and conducts secondary training on the outputs generated by the fine-tuned model to mitigate negative impacts from manual data intervention.

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Shijie Zhang, Renhao Li, Songsheng Wang, Philipp Koehn, Min Yang, Derek F. Wong

ReliableEval: A Recipe for Stochastic LLM Evaluation via Method of Moments (2025.findings-emnlp)

Challenge:	Existing LLM-based evaluation methods fail to accurately identify error spans and assess their severity.
Approach:	They propose a Hierarchical Multi-Agent Framework for Machine Translation Evaluation based on the MQM error typology and a hierarchical multi-agent system enabling granular evaluation of subtype errors.
Outcome:	The proposed framework outperforms baselines in error span detection and severity assessment.

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Gili Lior, Eliya Habba, Shahar Levy, Avi Caciularu, Gabriel Stanovsky

From Characters to Tokens: Dynamic Grouping with Hierarchical BPE (2025.findings-emnlp)

Challenge:	LLMs are highly sensitive to prompt phrasing, yet standard benchmarks typically report performance using a single prompt, raising concerns about the reliability of such evaluations.
Approach:	They propose a stochastic method of moments evaluation over the space of meaning-preserving prompt perturbations and propose resamplings to estimate the number of prompt re-sampleds needed to obtain meaningful results.
Outcome:	The proposed method is model-, task-, and metric-agnostic, offering a recipe for meaningful and robust evaluation.

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Rares Dolga, Lucas Maystre, Tudor Berariu, David Barber

Auto-SLURP: A Benchmark Dataset for Evaluating Multi-Agent Frameworks in Smart Personal Assistant (2025.findings-emnlp)

Challenge:	Subword tokenization methods suffer from inefficiencies in representing rare words and require large embedding matrices.
Approach:	They propose a dynamic character grouping method that leverages existing BPE tokenization without adding additional models.
Outcome:	The proposed method matches or exceeds existing entropy- and whitespace-based patching strategies while maintaining a compact vocabulary.

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Lei Shen, Xiaoyu Shen

Challenge:	Auto-SLURP is a benchmark dataset for evaluating multi-agent frameworks powered by large language models.
Approach:	Auto-SLURP is a benchmark dataset aimed at evaluating LLM-based multi-agent frameworks . authors propose it extends original SLURP dataset by relabeling data and integrating simulated servers and external services.
Outcome:	The proposed dataset extends the original SLURP dataset for natural language understanding tasks.

NER Retriever: Zero-Shot Named Entity Retrieval with Type-Aware Embeddings (2025.findings-emnlp)

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Or Shachar, Uri Katz, Yoav Goldberg, Oren Glickman

MMATH: A Multilingual Benchmark for Mathematical Reasoning (2025.findings-emnlp)

Challenge:	NER Retriever uses a user-defined type description to retrieve documents mentioning entities of that type.
Approach:	They propose a zero-shot retrieval framework for ad-hoc Named Entity Recognition . a user-defined type description is used to retrieve documents mentioning entities of that type .
Outcome:	The proposed framework outperforms lexical and dense retrieval baselines on three benchmarks.

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Wenyang Luo, Xin Zhao, Jing Sha, Shijin Wang, Ji-Rong Wen

MultiClaimNet: A Massively Multilingual Dataset of Fact-Checked Claim Clusters (2025.findings-emnlp)

Challenge:	a benchmark for multilingual complex reasoning spans 374 high-quality math problems across 10 typologically diverse languages.
Approach:	They propose a benchmark for multilingual complex reasoning across 10 languages . they show reasoning in English and answering in target languages can enhance performance .
Outcome:	The proposed benchmark demonstrates that models with high-quality reasoning can perform in multiple languages.

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Rrubaa Panchendrarajan, Rubén Míguez Pérez, Arkaitz Zubiaga

DS-MHP: Improving Chain-of-Thought through Dynamic Subgraph-Guided Multi-Hop Path (2025.findings-emnlp)

Challenge:	a growing number of unverified claims and expanding size of fact-checked databases require alternative, more efficient solutions.
Approach:	They propose to group fact-checked claims into multilingual clusters to improve claim retrieval and validation.
Outcome:	The proposed approach reduces redundancy by grouping claims into clusters . the proposed dataset contains 85.3K fact-checked claims written in 78 languages .

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Yongqiang Liu, Qiyao Peng, Binrong Liu, Hongtao Liu, XueWei Li, Wenjun Wang

LongTail-Swap: benchmarking language models’ abilities on rare words (2025.findings-emnlp)

Challenge:	Existing knowledge graph methods lack adaptability in knowledge-intensive tasks with multiple entities and implicit multi-hop relations.
Approach:	They propose a zero-shot framework to enhance LLM reasoning in multi-entity relation tasks.
Outcome:	DS-MHP outperforms baselines and state-of-the-art methods on 12 datasets spanning commonsense, logical, symbolic, and arithmetic reasoning.

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Robin Algayres, Charles-Éric Saint-James, Mahi Luthra, Jiayi Shen, Youssef Benchekroun, Dongyan Lin, Rashel Moritz, Juan Pino, Emmanuel Dupoux

TF-Mamba: Text-enhanced Fusion Mamba with Missing Modalities for Robust Multimodal Sentiment Analysis (2025.findings-emnlp)

Challenge:	LongTail-Swap is a benchmark that focuses on the tail of the word distribution, i.e., measures the ability of LMs to learn new words with very little exposure, like infants do.
Approach:	They introduce LongTail-Swap, a benchmark that measures the ability of language models to learn new words with very little exposure, like infants do.
Outcome:	The proposed benchmark measures the ability of language models to learn new words with very little exposure, like infants do.

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Xiang Li, Xianfu Cheng, Dezhuang Miao, Xiaoming Zhang, Zhoujun Li

Are Economists Always More Introverted? Analyzing Consistency in Persona-Assigned LLMs (2025.findings-emnlp)

Challenge:	Existing Transformer-based methods with missing modalities are difficult to use and have quadratic complexity.
Approach:	They propose a text-enhanced Fusion Mamba framework for robust MSA with missing modalities . a Text-aware Modality Enhancement module aligns and enriches non-text modality while reconstructing missing text semantics.
Outcome:	The proposed method is efficient under missing modalities and can be used in long-range modeling and multimodal fusion scenarios.

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Manon Reusens, Bart Baesens, David Jurgens

Challenge:	Personalized Large Language Models are increasingly used in diverse applications . prior research examined how well LLMs adhere to predefined personas in writing style . inconsistent responses are influenced by multiple factors, including the assigned persona, stereotypes, and model design choices.
Approach:	They propose a standardized framework to analyze consistency in persona-assigned LLMs.
Outcome:	The proposed framework evaluates personas across multiple tasks and runs.

Can you SPLICE it together? A Human Curated Benchmark for Probing Visual Reasoning in VLMs (2025.findings-emnlp)

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Mohamad Ballout, Okajevo Wilfred, Seyedalireza Yaghoubi, Nohayr Muhammad Abdelmoneim, Julius Mayer, Elia Bruni

On the Effectiveness of Prompt-Moderated LLMs for Math Tutoring at the Tertiary Level (2025.findings-emnlp)

Challenge:	SPLICE is a benchmark designed to probe event-based reasoning across multiple dimensions.
Approach:	They introduce a human-curated benchmark to probe event-based reasoning across multiple dimensions.
Outcome:	The proposed benchmark includes 3,381 human-filtered videos spanning 12 categories and 180 sub-categories . results show that state-of-the-art vision-language models struggle to match human performance .

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Sebastian Steindl, Fabian Brunner, Nada Sissouno, Dominik Schwagerl, Florian Schöler-Niewiera, Ulrich Schäfer

SkewRoute: Training-Free LLM Routing for Knowledge Graph Retrieval-Augmented Generation via Score Skewness of Retrieved Context (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have been studied intensively in the context of education, yielding heterogeneous results.
Approach:	They conduct a three-phase study with 49 students receiving a review of the topics, solving exercises, and writing an exam.
Outcome:	The prompt-moderated LLMs performed better than the unmoderated model .

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Hairu Wang, Yuan Feng, Yukun Cao, Xike Xie, S Kevin Zhou

Acquiescence Bias in Large Language Models (2025.findings-emnlp)

Challenge:	Large language models incur high inference costs during deployment, causing hallucination . no dedicated routing methods exist for RAG, and existing training-based routers face challenges scaling to this domain .
Approach:	They propose a plug-and-play routing framework that optimizes performance and cost . the framework delivers over 3x higher routing effectiveness while reducing runtime to less than 0.001x .
Outcome:	The proposed framework delivers over 3x higher routing effectiveness while reducing runtime to less than 0.001x compared to existing methods.

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Daniel Braun

Time to Talk: LLM Agents for Asynchronous Group Communication in Mafia Games (2025.findings-emnlp)

Challenge:	Acquiescence bias is a tendency of humans to agree with statements in surveys, independent of their actual beliefs.
Approach:	They investigate the presence of acquiescence bias in Large Language Models across different models, tasks, and languages.
Outcome:	The proposed model shows that LLMs display a bias towards answering no regardless of whether it indicates agreement or disagreement.

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Niv Eckhaus, Uri Berger, Gabriel Stanovsky

Challenge:	LLMs are used in synchronous communication, where a human user and a model communicate in alternating turns.
Approach:	They develop an adaptive asynchronous LLM agent consisting of two modules that decide what to say and a scheduler that decides when to say it.
Outcome:	The proposed agent performs on par with human players in online Mafia games and in its ability to blend in with the other human players.

How Sampling Affects the Detectability of Machine-written texts: A Comprehensive Study (2025.findings-emnlp)

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Matthieu Dubois, François Yvon, Pablo Piantanida

Challenge:	Recent detectors report near-perfect accuracy, often boasting AUROC scores above 99%, but these claims typically assume fixed generation settings, leaving open the question of how robust such systems are to changes in decoding strategies.
Approach:	They examine how sampling-based decoding impacts detectability with a focus on how subtle variations in a model’s (sub)word-level distribution affect detection performance.
Outcome:	The proposed framework systematically examines how sampling-based decoding impacts detectability, with a focus on how subtle variations in a model’s (sub)word-level distribution affect detection performance.

An Improved, Strong Baseline for Pre-Trained Large Language Models as Task-Oriented Dialogue Systems (2025.findings-emnlp)

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Sebastian Steindl, André Kestler, Ulrich Schäfer, Bernd Ludwig

MATCH: Task-Driven Code Evaluation through Contrastive Learning (2025.findings-emnlp)

Challenge:	Recent studies have shown that Large Language Models perform insufficiently as TOD systems.
Approach:	They propose a self-checking mechanism to improve LLM performance as TOD systems.
Outcome:	The proposed model outperforms existing models and improves their performance.

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Marah Ghoummaid, Vladimir Tchuiev, Ofek Glick, Michal Moshkovitz, Dotan Di Castro

Evaluating Large Language Models for Cross-Lingual Retrieval (2025.findings-emnlp)

Challenge:	GitHub Copilot generates 46% of the code on GitHub.
Approach:	They propose a reference-free metric that uses Contrastive Learning to generate meaningful embeddings for code and natural language task descriptions.
Outcome:	This paper compares the performance of a new similarity score with existing metrics.

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Longfei Zuo, Pingjun Hong, Oliver Kraus, Barbara Plank, Robert Litschko

SGCD: Subtask-Guided Causal-Debiasing Framework for Robust Cross-Utterance Sentiment Quadruple Extraction in Dialogues (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have been evaluated as second-stage reranking models for monolingual IR, but a systematic comparison is lacking for cross-lingual reranked IR.
Approach:	They propose to use machine translation to evaluate rerankers in cross-lingual IR . they find that LLMs perform better than LLM-based reranked models .
Outcome:	The proposed model improves cross-lingual IR but relies on machine translation for the first stage.

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Xiang Li, Keyu Yao, Gang Shen

FaMTEB: Massive Text Embedding Benchmark in Persian Language (2025.findings-emnlp)

Challenge:	a new framework for sentiment analysis in dialogues addresses cross-utterance elements and focus biases . SGCD framework employs multi-granularity attention paths to enhance cross-interaction matching .
Approach:	a framework is developed to help analyze sentiments in multi-turn dialogues . it leverages subtask-specific features to guide learning of token-level features .
Outcome:	The proposed framework outperforms state-of-the-art methods in analyzing conversational data . cross-utterance elements and focus bias are challenges, authors say .

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Erfan Zinvandi, Morteza Alikhani, Mehran Sarmadi, Zahra Pourbahman, Sepehr Arvin, Reza Kazemi, Arash Amini

Leveraging High-Resource English Corpora for Cross-lingual Domain Adaptation in Low-Resource Japanese Medicine via Continued Pre-training (2025.findings-emnlp)

Challenge:	a comprehensive benchmark for Persian text embeddings is built upon the Massive Text Embedding Benchmark (MTEB) 63 datasets are included in the benchmark, including a novel task of summary retrieval.
Approach:	They propose a benchmark for Persian (Farsi) text embeddings built upon the Massive Text Embedding Benchmark.
Outcome:	The proposed framework includes 63 datasets spanning seven different tasks . the evaluation datasets were rigorously evaluated by humans and automated systems .

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Kazuma Kobayashi, Zhen Wan, Fei Cheng, Yuma Tsuta, Xin Zhao, Junfeng Jiang, Jiahao Huang, Zhiyi Huang, Yusuke Oda, Rio Yokota, Yuki Arase, Daisuke Kawahara, Akiko Aizawa, Sadao Kurohashi

Structure Trumps Size: Rethinking Data Quality for LLM Reasoning (2025.findings-emnlp)

Challenge:	low-resource language corpora in professional domains like medicine hinder cross-lingual domain adaptation of pre-trained large language models.
Approach:	They examine how linguistic features affect performance on a Japanese–English medical knowledge benchmark.
Outcome:	The proposed model can leverage English-language resources in medical domains while ensuring sufficient coverage of language-specific expressions in a target language.

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Hu Xu, Zeyan Li, Rui Wang, Jianfeng Xu

A Zero-Shot Neuro-Symbolic Approach for Complex Knowledge Graph Question Answering (2025.findings-emnlp)

Challenge:	Existing methods for fine-tuning Large Language Models rely on heuristic strategies and lack systematic, quantitative frameworks for evaluating data quality.
Approach:	They propose a multi-dimensional quantitative framework for reasoning data management . they rigorously evaluate and optimize datasets along six orthogonal dimensions .
Outcome:	The proposed framework rigorously evaluates and optimizes datasets along six orthogonal dimensions.

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Prerna Agarwal, Srikanta Bedathur

Making Every Step Effective: Jailbreaking Large Vision-Language Models Through Hierarchical KV Equalization (2025.findings-emnlp)

Challenge:	Existing low-resource Knowledge Graph Question Answering (KGQA) methods rely heavily on Large Language Models (LLMs) KGQA methods based on LLMs are limited in their ability to model KG structure without additional data.
Approach:	They propose a KGQA framework that can operate in a zero-shot setting . they propose NS-KGQA to use neural KG embeddings to model KG structure .
Outcome:	The proposed framework outperforms existing LLM-based zero-shot baselines by 26%.

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Shuyang Hao, Yiwei Wang, Bryan Hooi, Jun Liu, Muhao Chen, Zi Huang, Yujun Cai

MT-Mol: Multi Agent System with Tool-based Reasoning for Molecular Optimization (2025.findings-emnlp)

Challenge:	HKVE selectively accepts gradient optimization results based on the distribution of attention scores across different layers, ensuring that every optimization step positively contributes to the attack.
Approach:	They propose a framework that selectively accepts gradient optimization results based on the distribution of attention scores across different layers and selectively takes them into account when calculating the attack success rate.
Outcome:	The proposed framework outperforms existing methods by achieving success rates of 75.08% on MiniGPT4, 85.84% on LLaVA and 81.00% on Qwen-VL.

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Hyomin Kim, Yunhui Jang, Sungsoo Ahn

A Survey on LLM-powered Agents for Recommender Systems (2025.findings-emnlp)

Challenge:	Structured reasoning and tool-grounded molecular optimization are still underexplored.
Approach:	They propose a framework for molecular optimization that leverages tool-guided reasoning and role-specialized LLM agents.
Outcome:	a new framework outperforms existing LLM frameworks on 15 of 23 tasks.

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Qiyao Peng, Hongtao Liu, Hua Huang, Jian Yang, Qing Yang, Minglai Shao

Efficiently Selecting Response Generation Strategies for Synthetic Data Construction by Self-Aligned Perplexity (2025.findings-emnlp)

Challenge:	Large Language Models have demonstrated remarkable capabilities in natural language understanding, reasoning, and generation.
Approach:	They present a comprehensive synthesis of large language models and their applications . they dissect a four-module agent architecture and review representative designs .
Outcome:	The proposed models address fundamental challenges in traditional recommender systems . they include limited comprehension of complex user intents, insufficient interaction capabilities .

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Xuan Ren, Qi Chen, Lingqiao Liu

Benchmarking for Domain-Specific LLMs: A Case Study on Academia and Beyond (2025.findings-emnlp)

Challenge:	Using a small sample of data, we find that perplexity is suboptimal in characterizing “familiarity” .
Approach:	They propose a method that assesses a small subset of generated data to estimate suitability for a specific target LLM.
Outcome:	The proposed method assesses a small subset of generated data to estimate suitability for a specific target LLM.

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Rubing Chen, Jiaxin Wu, Jian Wang, Xulu Zhang, Wenqi Fan, Chenghua Lin, Xiaoyong Wei, Li Qing

FrameEOL: Semantic Frame Induction using Causal Language Models (2025.findings-emnlp)

Challenge:	Comp-Comp is an iterative benchmarking framework grounded in the principles of comprehensiveness and compactness.
Approach:	They propose a benchmark framework that incorporates the principle of comprehensiveness and compactness.
Outcome:	The proposed framework is domain-agnostic and adaptable to a wide range of specialized fields.

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Chihiro Yano, Kosuke Yamada, Hayato Tsukagoshi, Ryohei Sasano, Koichi Takeda

CaTER: A Framework for Context-aware Topology Entity Retrieval Contrastive Learning in End-to-End Task-Oriented Dialogue Systems (2025.findings-emnlp)

Challenge:	Semantic frame induction is the task of clustering frame-evoking words according to the semantic frames they evoke.
Approach:	They propose a prompt-based method for obtaining Frame Embeddings that outputs One frame-name as a Label .
Outcome:	The proposed method outperforms existing methods on English and Japanese datasets.

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Di Wu Hebeu, Zhizhi Yu

Attribution and Application of Multiple Neurons in Multimodal Large Language Models (2025.findings-emnlp)

Challenge:	generative models suffer from implicit association preference, while retrieval-generation approaches face knowledge transfer discrepancies.
Approach:	They propose a topology entity retrieval contrastive learning framework that uses context-aware distilling attention mechanism to suppress noise from weakly relevant attributes.
Outcome:	The proposed framework outperforms strong baselines on three TOD benchmarks with small and large knowledge bases.

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Feiyu Wang, Ziran Zhao, Dong Yu, Pengyuan Liu

When Models Lie, We Learn: Multilingual Span-Level Hallucination Detection with PsiloQA (2025.findings-emnlp)

Challenge:	Existing methods to identify multimodal neurons in MLLMs are insufficiently understood . previous studies focused on identifying neurons corresponding to single-tokens .
Approach:	They propose a method to identify multimodal neurons in Transformer-based MLLMs . they introduce fuzzy set theory to model the complex relationship between neurons and semantic concepts .
Outcome:	The proposed method improves performance on the Visual Question Answering task.

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Elisei Rykov, Kseniia Petrushina, Maksim Savkin, Valerii Olisov, Artem Vazhentsev, Kseniia Titova, Alexander Panchenko, Vasily Konovalov, Julia Belikova

Unraveling Misinformation Propagation in LLM Reasoning (2025.findings-emnlp)

Challenge:	Existing hallucination detection benchmarks operate at the sequence level and are limited to English . Existing methods lacking fine-grained, multilingual supervision are limited in English based on the sequence .
Approach:	They propose a large-scale, multilingual dataset annotated with span-level hallucinations across 14 languages.
Outcome:	The proposed dataset annotated with span-level hallucinations across 14 languages is scalable and cost-efficient.

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Yiyang Feng, Yichen Wang, Shaobo Cui, Boi Faltings, Mina Lee, Jiawei Zhou

RAISE: Reinforced Adaptive Instruction Selection For Large Language Models (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning, but how they propagate within their reasoning process remains underexplored.
Approach:	They propose a practical approach to mitigating misinformation propagation in LLMs by applying factual corrections early in the reasoning process and fine-tuning on synthesized data with early-stage corrections significantly improves reasoning factuality.
Outcome:	The proposed model can correct misinformation when explicitly instructed, but fails to correct misinformation less than half the time even with explicit instructions.

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Qingsong Lv, Yangning Li, Zihua Lan, Zishan Xu, Jiwei Tang, Tingwei Lu, Yinghui Li, Wenhao Jiang, Hong-Gee Kim, Hai-Tao Zheng, Philip S. Yu

Teaching According to Talents! Instruction Tuning LLMs with Competence-Aware Curriculum Learning (2025.findings-emnlp)

Challenge:	Existing selection methods rely on static, heuristic quality scores and are executed only once before training.
Approach:	They propose a dynamic selection framework that integrates selection into every training step.
Outcome:	The proposed framework integrates selection into every training step.

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Yangning Li, Tingwei Lu, Yinghui Li, Yankai Chen, Wei-Chieh Huang, Wenhao Jiang, Hui Wang, Hai-Tao Zheng, Philip S. Yu

Let Them Down Easy! Contextual Effects of LLM Guardrails on User Perceptions and Preferences (2025.findings-emnlp)

Challenge:	Efficient instruction tuning aims to enhance the ultimate performance of large language models (LLMs) current methods suffer from the curriculum rigidity, resulting in a fixed and potentially sub-optimal learning trajectory.
Approach:	a framework for efficient instruction tuning is proposed to address the issue of curriculum rigidity . current methods rely on static heuristic difficulty metrics and fail to adapt to evolving capabilities .
Outcome:	Efficient instruction tuning aims to enhance the ultimate performance of large language models . current methods suffer from the curriculum rigidity, resulting in a fixed learning trajectory .

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Mingqian Zheng, Wenjia Hu, Patrick Zhao, Motahhare Eslami, Jena D. Hwang, Faeze Brahman, Carolyn Rose, Maarten Sap

From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems (2025.findings-emnlp)

Challenge:	Current LLMs are trained to refuse potentially harmful input queries regardless of intent . a study of 480 participants evaluating 3,840 query-response pairs reveals that response strategy largely shapes user experience .
Approach:	They examine how different refusal strategies affect user perceptions across varying motivations . they find partial compliance reduces negative user perception by over 50% to flat-out refusals a 480 participants study .
Outcome:	The study examines the perceptions of LLMs on user intents and their response strategies . it shows that partial compliance reduces negative user perceptions by over 50% to flat refusals .

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Zekun Zhou, Xiaocheng Feng, Lei Huang, Xiachong Feng, Ziyun Song, Ruihan Chen, Liang Zhao, Weitao Ma, Yuxuan Gu, Baoxin Wang, Dayong Wu, Guoping Hu, Ting Liu, Bing Qin

Enhancing Model Privacy in Federated Learning with Random Masking and Quantization (2025.findings-emnlp)

Challenge:	rapid development of artificial intelligence (AI) technologies has inspired researchers to explore how AI can accelerate and enhance research.
Approach:	They organize the relevant studies into three main categories: hypothesis formulation, hypothesis validation, and manuscript publication.
Outcome:	The authors summarize the current state of research in three main areas: hypothesis formulation, hypothesis validation, and manuscript publication.

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Zhibo Xu, Zhu JianHao, Jingwen Xu, Changze Lv, Zhenghua Wang, Zisu Huang, Xiaohua Wang, Muling Wu, Qi Qian, Xiaoqing Zheng, Xuanjing Huang

SuPreME: A Supervised Pre-training Framework for Multimodal ECG Representation Learning (2025.findings-emnlp)

Challenge:	federated learning approaches are limited by the complexity of large language models and the need for specialized expertise to protect intellectual property.
Approach:	They propose a federated learning approach that leverages random masking to obscure a subnetwork of model parameters and applies quantization to the remaining parameters.
Outcome:	The proposed approach maintains strong model performance in federated learning settings and achieves enhanced protection of model parameters compared to baseline methods.

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Mingsheng Cai, Jiuming Jiang, Wenhao Huang, Che Liu, Rossella Arcucci

Ferret: Faster and Effective Automated Red Teaming with Reward-Based Scoring Technique (2025.findings-emnlp)

Challenge:	Recent ECG Self-Supervised Learning methods mitigate this by learning features without extensive labels but fail to capture fine-grained clinical semantics and require extensive task-specific fine-tuning.
Approach:	They propose a supervised pre-training framework for Multimodal ECG representation learning that combines structured diagnostic labels with large language models to help denoise, standardize cardiac concepts and improve clinical representation learning.
Outcome:	The proposed framework improves on six downstream datasets covering 106 cardiac conditions and achieves a zero-shot AUC performance of 77.20% over state-of-the-art eSSLs.

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Tej Deep Pala, Vernon Toh, Rishabh Bhardwaj, Soujanya Poria

Do What? Teaching Vision-Language-Action Models to Reject the Impossible (2025.findings-emnlp)

Challenge:	Existing red-teaming methods generate adversarial attacks to identify vulnerabilities, but they face slow performance, limited categorical diversity, and high resource demands.
Approach:	They propose a method that generates multiple adversarial prompt mutations per iteration and ranks them using scoring functions.
Outcome:	The proposed method achieves a 95% attack success rate and reduces time to a 90% ASR by 15.2%.

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Wen-Han Hsieh, Elvis Hsieh, Dantong Niu, Trevor Darrell, Roei Herzig, David M. Chan

AgentInit: Initializing LLM-based Multi-Agent Systems via Diversity and Expertise Orchestration for Effective and Efficient Collaboration (2025.findings-emnlp)

Challenge:	Recent studies show that VLAs can recognize, interpret, and respond to false-premise instructions.
Approach:	They propose a framework that detects when an instruction cannot be executed due to a false premise and engages in language-based clarification or correction.
Outcome:	The proposed framework detects when an instruction cannot be executed due to a false premise and engages in language-based clarification or correction.

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Chunhao Tian, Yutong Wang, Xuebo Liu, Zhexuan Wang, Liang Ding, Miao Zhang, Min Zhang

Time to Revisit Exact Match (2025.findings-emnlp)

Challenge:	Existing MAS initialization methods do not fully account for the collaborative needs of the generated agents in subsequent stages.
Approach:	They propose to use a Natural Language to Format mechanism to optimize the structure of agent teams and incorporate a natural language to format mechanism to ensure consistency and standardization.
Outcome:	The proposed method outperforms state-of-the-art initialization methods and pre-defined strategies across various frameworks and tasks while reducing token consumption.

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Auss Abbood, Zaiqiao Meng, Nigel Collier

LongTableBench: Benchmarking Long-Context Table Reasoning across Real-World Formats and Domains (2025.findings-emnlp)

Challenge:	Temporal question answering is an established method for evaluating temporal reasoning in large language models.
Approach:	They propose a numerical estimation task where all questions require a numeric, temporal answer, allowing us to evaluate models beyond EM.
Outcome:	The proposed model responses are based on a numerical estimation task and are distilled from Test of Time and TempTabQA.

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Liyao Li, Jiaming Tian, Hao Chen, Wentao Ye, Chao Ye, Haobo Wang, Ningtao Wang, Xing Fu, Gang Chen, Junbo Zhao

Exploring and Evaluating Multimodal Knowledge Reasoning Consistency of Multimodal Large Language Models (2025.findings-emnlp)

Challenge:	Evaluating 52 LLMs reveals that only the strongest models maintain robust performance under increasing context lengths and format diversity.
Approach:	They propose a benchmark for evaluating long-context reasoning over semi-structured tables across diverse formats, tasks, and domains.
Outcome:	The proposed model outperforms compression-based approaches on tasks requiring semantic integration.

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Boyu Jia, Junzhe Zhang, Huixuan Zhang, Xiaojun Wan

MPTA: MultiTask Personalization Assessment (2025.findings-emnlp)

Challenge:	MLLMs have achieved significant breakthroughs in understanding across text and vision, but current models still face inconsistencies in reasoning outcomes.
Approach:	They propose to evaluate multimodal large language models using a multimodal knowledge reasoning dataset to examine the extent of consistency degradation.
Outcome:	The proposed evaluation tasks show that MLLMs are inefficient at integrating knowledge across modalities .

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Matthieu Tehenan, Eric Chamoun, Andreas Vlachos

Challenge:	MTPA tests large language models on real personas spanning demographics, beliefs, and values . aggregate metrics suggest models are truthful and safe, subgroup-specific evaluations reveal hidden pockets of degraded factuality, fairness disparities, and inconsistent value alignment.
Approach:	a benchmark is a tool that leverages large-scale survey data to construct real personas . they show persona conditioning exposes pluralistic misalignment .
Outcome:	MTPA conditions models on real personas and tests their behavior across alignment tasks.

Semantic Geometry of Sentence Embeddings (2025.findings-emnlp)

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Matthieu Tehenan

ReAlign: Structured Revision for Small Language Model Alignment (2025.findings-emnlp)

Challenge:	Sentence embeddings are central to natural language processing, but their internal features are not interpretable and users lack fine-grained control for downstream tasks.
Approach:	They propose a formal framework to characterize the organization of features in sentence embeddings . they show how they can be composed to capture richer semantic structures .
Outcome:	The proposed method can be used to capture richer semantic structures.

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Ruijun Chen, Jiajian Guo, Hongzhan Chen, Fanqi Wan, Qifan Wang, Xiaojun Quan

Curr-ReFT: Overcoming Training Bottlenecks in Small-scale Vision-Language Models via Curriculum Reinforcement Finetuning (2025.findings-emnlp)

Challenge:	weak policies struggle to generate informative on-policy samples and suffer from unstable gradients when trained on off-police signals from stronger models.
Approach:	They propose a training framework that combines stability of on-policy learning with reviser-assisted supervision.
Outcome:	The proposed training framework outperforms strong preference optimization baselines on AlpacaEval-2 and Arena-Hard.

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Huilin Deng, Ding Zou, Xinghao Zhao, Rui Ma, Yanming Guo, Yang Cao, Yu Kang

Layer-Aware Task Arithmetic: Disentangling Task-Specific and Instruction-Following Knowledge (2025.findings-emnlp)

Challenge:	State-of-the-art vision-language models require massive scaling that limits practical deployment.
Approach:	They propose to use supervised fine-tuning to train small-scale vision-language models but face out-of-domain collapse when trained with traditional supervised learning (SFT).
Outcome:	Experiments show that curr-reFT achieves state-of-the-art performance across visual tasks in both in- and out-of domain settings and benchmarks.

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Yan-Lun Chen, Yi-Ru Wei, Chia-Yi Hsu, Chia-Mu Yu, Chun-Ying Huang, Ying-Dar Lin, Yu-Sung Wu, Wei-Bin Lee

Revisiting Pruning vs Quantization for Small Language Models (2025.findings-emnlp)

Challenge:	Large language models (LLMs) demonstrate strong task-specific capabilities through fine-tuning, but merging multiple fine- tuned models often leads to degraded performance due to overlapping instruction-following components.
Approach:	They propose a layer-wise approach that assigns layer-specific weights to task vectors based on their alignment with instruction-following or task-specific components.
Outcome:	The proposed approach outperforms existing methods in learning and forgetting tasks while preserving overall model utility.

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Zihan Zhou, Simon Kurz, Zhixue Zhao

CLaw: Benchmarking Chinese Legal Knowledge in Large Language Models - A Fine-grained Corpus and Reasoning Analysis (2025.findings-emnlp)

Challenge:	Compressing Small Language Models (SLMs) is particularly suited for resource-constrained devices, but their compression dynamics remain underexplored compared to Large Language Model (LLMs).
Approach:	They evaluated post-training pruning and quantization methods across six SLMs from 0.5 to 3.8B, seven languages, and seven downstream tasks.
Outcome:	The proposed methods outperform pruning and quantization on six SLMs from 0.5 to 3.8B, seven languages, and seven downstream tasks.

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Xinzhe Xu, Liang Zhao, Hongshen Xu, null Chenchenc

polyBART: A Chemical Linguist for Polymer Property Prediction and Generative Design (2025.findings-emnlp)

Challenge:	a new benchmark is designed to evaluate LLMs on Chinese legal knowledge and its application in reasoning . general pre-training that ingests legal texts without specialized focus compromises reliability of LLM responses . achieving trustworthy legal reasoning in LLM requires a robust synergy of accurate knowledge retrieval and strong general reasoning capabilities.
Approach:	They propose a benchmark specifically engineered to evaluate LLMs on Chinese legal knowledge and its application in reasoning.
Outcome:	The proposed benchmark evaluates LLMs on Chinese legal knowledge and its application in reasoning.

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Anagha Savit, Harikrishna Sahu, Shivank S. Shukla, Wei Xiong, Rampi Ramprasad

A Survey of RAG-Reasoning Systems in Large Language Models (2025.findings-emnlp)

Challenge:	a new language model for polymer design is needed to accurately predict polymers' properties.
Approach:	They propose a language model-driven polymer discovery capability that enables rapid and accurate exploration of the polymer design space.
Outcome:	The proposed polyBART is the first language model capable of bidirectional translation between polymer structures and properties.

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Yangning Li, Weizhi Zhang, Yuyao Yang, Wei-Chieh Huang, Yaozu Wu, Junyu Luo, Yuanchen Bei, Henry Peng Zou, Xiao Luo, Yusheng Zhao, Chunkit Chan, Yankai Chen, Zhongfen Deng, Yinghui Li, Hai-Tao Zheng, Dongyuan Li, Renhe Jiang, Ming Zhang, Yangqiu Song, Philip S. Yu

REGen: A Reliable Evaluation Framework for Generative Event Argument Extraction (2025.findings-emnlp)

Challenge:	a survey of RAG-based reasoning-based approaches shows that it is not effective for multi-step inferences.
Approach:	They map how advanced reasoning optimizes each stage of RAG . they show how retrieved knowledge supply missing premises and expand context for complex inference .
Outcome:	The proposed frameworks achieve state-of-the-art across knowledge-intensive benchmarks.

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Omar Sharif, Joseph Gatto, Madhusudan Basak, Sarah Masud Preum

Mitigating Interviewer Bias in Multimodal Depression Detection: An Approach with Adversarial Learning and Contextual Positional Encoding (2025.findings-emnlp)

Challenge:	Existing work evaluates event argument extraction with exact match (EM), where predicted arguments must align exactly with annotated spans.
Approach:	They propose a Reliable Evaluation framework for Generative event argument extraction that combines exact, relaxed, and LLM-based matching to better align with human judgment.
Outcome:	Experiments on six datasets show that REGen achieves an average performance gain of +23.93 F1 over EM, reflecting capabilities overlooked by prior evaluation.

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Enshi Zhang, Christian Poellabauer

AMIA: Automatic Masking and Joint Intention Analysis Makes LVLMs Robust Jailbreak Defenders (2025.findings-emnlp)

Challenge:	Clinical interviews are a standard method for assessing depression . however, these methods neglect the broader conversational context .
Approach:	They develop a multimodal dialogue-level transformer that captures the dynamics of dialogue within each interview . they also build an adversarial classifier with a gradient reversal layer to learn shared representations .
Outcome:	The proposed model captures the dynamics of dialogue within each interview using positional embedding and question context vectors.

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Yuqi Zhang, Yuchun Miao, Zuchao Li, Liang Ding

Disentangling Language Understanding and Reasoning Structures in Cross-lingual Chain-of-Thought Prompting (2025.findings-emnlp)

Challenge:	AMIA is a lightweight, inference-only defense for Large Vision–Language Models . it automatically masks text-irrelevant image patches and conducts joint Intention Analysis .
Approach:	AMIA is a lightweight, inference-only defense for large vision–language models . it automatically masks a small set of text-irrelevant image patches to disrupt adversarial perturbations .
Outcome:	AMIA improves defense success rates across diverse LVLMs and jailbreak benchmarks . it preserves general utility with only 2% accuracy drop, incurs only modest inference overhead .

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Khanh-Tung Tran, Nguyet-Hang Vu, Barry O’Sullivan, Hoang D. Nguyen

MoRoVoc: A Large Dataset for Geographical Variation Identification of the Spoken Romanian Language (2025.findings-emnlp)

Challenge:	a recent study has shown that cross-lingual chain-of-thought prompting improves learning in low-resource languages.
Approach:	They examine whether benefits of cross-lingual prompting arise from language-specific reasoning structures . authors employ neuron intervention and perturbation techniques to analyze and deactivate language-related reasoning neurons .
Outcome:	The proposed study shows that language-specific reasoning structures are essential for reasoning in each language, but have minimal effect on reasoning in other languages.

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Andrei-Marius Avram, Bănescu Ema-Ioana, Anda-Teodora Robea, Dumitru-Clementin Cercel, Mihaela-Claudia Cercel

Language-Informed Synthesis of Rational Agent Models for Grounded Theory-of-Mind Reasoning On-the-fly (2025.findings-emnlp)

Challenge:	MoRoVoc is the largest dataset for analyzing the regional variation of spoken Romanian . it has more than 93 hours of audio and 88,192 audio samples .
Approach:	They propose a multi-target adversarial training framework that incorporates demographic attributes as adversarials for speech models.
Outcome:	The proposed model achieves 78.21% accuracy for variation identification of spoken Romanian using gender as an adversarial target.

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Lance Ying, Ryan Truong, Katherine M. Collins, Cedegao E. Zhang, Megan Wei, Tyler BrookeWilson, Tan Zhi-Xuan, Lionel Wong, Joshua B. Tenenbaum

MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMs (2025.findings-emnlp)

Challenge:	Language is a powerful source of information in social settings, especially in novel situations where language can provide both abstract information about the environment dynamics and concrete specifics about an agent that cannot be easily visually observed.
Approach:	They propose a language-informed rational agent synthesis framework that integrates linguistic and visual inputs to draw context-specific social inferences.
Outcome:	The proposed framework outperforms ablations and state-of-the-art models on a range of social reasoning tasks derived from cognitive science experiments.

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Zaid Alyafeai, Maged S. Al-shaibani, Bernard Ghanem

MASSV: Multimodal Adaptation and Self-Data Distillation for Speculative Decoding of Vision-Language Models (2025.findings-emnlp)

Challenge:	Metadata extraction relies heavily on manual annotation of documents.
Approach:	They propose a framework that leverages Large Language Models to automatically extract metadata attributes from scientific papers covering datasets of languages other than Arabic.
Outcome:	The proposed framework automates the extraction of metadata attributes from Arabic scientific papers using large language models.

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Mugilan Ganesan, Shane Segal, Ankur Aggarwal, Nish Sinnadurai, Sean Lie, Vithursan Thangarasa

FESTA: Functionally Equivalent Sampling for Trust Assessment of Multimodal LLMs (2025.findings-emnlp)

Challenge:	Speculative decoding of vision-language models provides a novel way to accelerate language model inference by enabling a lightweight draft model to propose multiple tokens that a larger target model verifies simultaneously.
Approach:	They propose a technique that allows a lightweight draft model to propose multiple tokens that a larger target model verifies simultaneously.
Outcome:	The proposed technique increases accepted length by 30% and delivers speedups of up to 1.46x compared to conventional text-only drafting baselines on visually-grounded tasks.

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Debarpan Bhattacharya, Apoorva Kulkarni, Sriram Ganapathy

ClaimGen-CN: A Large-scale Chinese Dataset for Legal Claim Generation (2025.findings-emnlp)

Challenge:	Existing methods for trust assessment of multimodal large language models generate mispredictions due to multimodal input paradigms.
Approach:	They propose a multimodal input sampling technique that generates an uncertainty measure based on equivalent and complementary input samplings.
Outcome:	The proposed technique improves selective prediction performance with visual and audio reasoning tasks.

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Siying Zhou, Yiquan Wu, Hui Chen, Xueyu Hu, Kun Kuang, Adam Jatowt, Chunyan Zheng, Fei Wu

Challenge:	Currently, legal claims are not being used by non-professionals.
Approach:	They construct a dataset for Chinese legal claim generation task and then use it to evaluate the generated claims.
Outcome:	The proposed dataset is the first for the Chinese legal claim generation task and will be made publicly available.

Summarize-Exemplify-Reflect: Data-driven Insight Distillation Empowers LLMs for Few-shot Tabular Classification (2025.findings-emnlp)

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Yifei Yuan, Jiatong Li, Weijia Zhang, Mohammad Aliannejadi, Evangelos Kanoulas, Renjun Hu

Rethinking LLM Uncertainty: A Multi-Agent Approach to Estimating Black-Box Model Uncertainty (2025.findings-emnlp)

Challenge:	Recent studies show the promise of large language models for few-shot tabular classification but highlight challenges due to the variability in structured data.
Approach:	They propose a framework that distills data into actionable insights to enable robust and effective classification by large language models.
Outcome:	The proposed framework integrates rule summarization, strategic exemplification, and insight reflection through deep collaboration between LLMs and data modeling techniques.

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Yu Feng, Phu Mon Htut, Zheng Qi, Wei Xiao, Manuel Mager, Nikolaos Pappas, Kishaloy Halder, Yang Li, Yassine Benajiba, Dan Roth

Challenge:	Existing methods to gauge model’s uncertainty through self-consistency in responses to the target query are misleading: an LLM may confidently provide an incorrect answer to a target query, yet give a confident and accurate answer to that same query when answering a knowledge-preserving perturbation of the query.
Approach:	They propose a method that uses multi-agent interaction to estimate black-box LLMs' uncertainty.
Outcome:	The proposed method outperforms existing self-consistency based methods and improves hallucination detection.

Stress-Testing the Reasoning Competence of Language Models With Formal Proofs (2025.findings-emnlp)

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Konstantine Arkoudas, Serafim Batzoglou

Topic-Guided Reinforcement Learning with LLMs for Enhancing Multi-Document Summarization (2025.findings-emnlp)

Challenge:	a new battery of challenging but tractable logical inference tasks is developed on ProofGrid . the tasks include proof writing and proof checking across propositional and equational logic .
Approach:	They propose a new battery of challenging but tractable logical inference tasks on ProofGrid . they introduce two new tasks: proof inpainting and proof gap-filling .
Outcome:	The proposed model performs well on top-tier models but also shows systematic failure modes.

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Chuyuan Li, Austin Xu, Shafiq Joty, Giuseppe Carenini

FACTCHECKMATE: Preemptively Detecting and Mitigating Hallucinations in LMs (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have shown impressive results in single-document summarization, but their performance on MDS still leaves room for improvement.
Approach:	They propose a topic-guided reinforcement learning approach to improve content selection in MDS . explicit prompting models with topic labels enhances the informativeness, they show .
Outcome:	The proposed method outperforms baselines on multi-News and multi-XScience datasets.

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Deema Alnuhait, Neeraja Kirtane, Muhammad Khalifa, Hao Peng

Dialectal Toxicity Detection: Evaluating LLM-as-a-Judge Consistency Across Language Varieties (2025.findings-emnlp)

Challenge:	Language models (LMs) hallucinate.
Approach:	They introduce a classifier that predicts whether LMs hallucinate based on model’s hidden states before decoding begins.
Outcome:	The proposed model preemptively detects hallucinations by learning a classifier that predicts whether the LM will hallucinate . if a hallucinomy is detected, FactCheckmate intervenes by adjusting the model’s hidden states to produce more factual outputs.

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Fahim Faisal, Md Mushfiqur Rahman, Antonios Anastasopoulos

Mitigate One, Skew Another? Tackling Intersectional Biases in Text-to-Image Models (2025.findings-emnlp)

Challenge:	toxicity detection of modern LLMs is underexplored due to dialectal differences.
Approach:	They evaluate toxicity detection by using LLMs as evaluators across diverse dialects . they create a multi-dialect dataset using synthetic transformations and human-assisted translations based on human-aided translations.
Outcome:	The proposed model shows that LLMs are sensitive to dialectal shifts and low-resource multilingual variation.

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Pushkar Shukla, Aditya Chinchure, Emily Diana, Alexander Tolbert, Kartik Hosanagar, Vineeth N. Balasubramanian, Leonid Sigal, Matthew A. Turk

Language-Specific Layer Matters: Efficient Multilingual Enhancement for Large Vision-Language Models (2025.findings-emnlp)

Challenge:	a new tool for analyzing and quantifying bias interactions in text-to-image models is being developed . a bias in text models can be deeply interrelated, but measuring such effects quantitatively remains a challenge.
Approach:	They propose a tool to quantify bias interactions in text-to-image models by analyzing and quantifying bias interactions along bias axes.
Outcome:	a new tool analyzes and quantifies bias interactions in text-to-image models . estimates show strong correlations with observed post-mitigation outcomes .

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Yuchun Fan, Yilin Wang, Yongyu Mu, Lei Huang, Bei Li, Xiaocheng Feng, Tong Xiao, JingBo Zhu

InfAL: Inference Time Adversarial Learning for Improving Research Ideation (2025.findings-emnlp)

Challenge:	Large vision-language models exhibit an imbalance in multilingual capabilities .
Approach:	They propose a training recipe that achieves efficient multilingual enhancement for LVLMs by Precise Language Specific layers fine-tuning.
Outcome:	The proposed training recipe achieves efficient multilingual enhancement for LVLMs by fine-tuning language specific layers.

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Sikun Guo, Amir Hassan Shariatmadari, Peng Wang, Albert Huang, Aidong Zhang

Speculative Decoding for Multi-Sample Inference (2025.findings-emnlp)

Challenge:	Advancements in Large Language Models (LLMs) have opened new opportunities for scientific discovery by assisting researchers in generating novel hypotheses and ideas.
Approach:	They propose an inference time adversarial learning approach that optimizes the utilization of LLMs’ parametric knowledge without additional model training.
Outcome:	The proposed approach optimizes the utilization of LLMs’ parametric knowledge without requiring additional model training, making adversarial learning efficient and context-driven.

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Yiwei Li, Jiayi Shi, Shaoxiong Feng, Peiwen Yuan, Xinglin Wang, Yueqi Zhang, Ji Zhang, Chuyi Tan, Boyuan Pan, Yao Hu, Kan Li

LSRL: Process-Supervised GRPO on Latent Recurrent States Improves Mathematical Reasoning (2025.findings-emnlp)

Challenge:	Speculative decoding method exploits consensus of parallel reasoning paths to synthesize high-quality draft tokens without auxiliary models or external databases.
Approach:	They propose a speculative decoding method that exploits the consensus of parallel reasoning paths to synthesize high-quality draft tokens without auxiliary models or external databases.
Outcome:	The proposed method exploits the intrinsic consensus of parallel reasoning paths to synthesize high-quality draft tokens without auxiliary models or databases.

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Hangliang Ren

Multi-token Mask-filling and Implicit Discourse Relations (2025.findings-emnlp)

Challenge:	Latent-recurrent language models solve tasks by iteratively refining hidden states rather than emitting chain-of-thought tokens.
Approach:	They propose a process-supervised variant of Guided Reward Policy Optimization that rewards latent steps at every latent step.
Outcome:	The proposed model improves absolute accuracy by +4.27 points on GSM-8K and +2.06 points on MathQA.

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Meinan Liu, Yunfang Dong, Xixian Liao, Bonnie Webber

Schema Generation for Large Knowledge Graphs Using Large Language Models (2025.findings-emnlp)

Challenge:	Existing mask-filling methods were ambiguous and ambiguic . a new study extends existing mask-filled methods to a discourse-level task .
Approach:	They propose a multi-token mask-filling approach that supports the prediction of multi-tongue connectives in masked positions.
Outcome:	The proposed multi-token mask-filling outperforms the single-token approach in recognizing implicit discourse relations.

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Bohui Zhang, Yuan He, Lydia Pintscher, Albert Meroño-Peñuela, Elena Simperl

MCTS-RAG: Enhancing Retrieval-Augmented Generation with Monte Carlo Tree Search (2025.findings-emnlp)

Challenge:	Schemas are a vital part of ontology engineering and require substantial knowledge engineers and domain experts to create them.
Approach:	They propose to use large language models to generate schemas in Shape Expressions (ShEx) to bridge the resource gap between knowledge engineers and domain experts.
Outcome:	The proposed pipelines use local and global information from knowledge graphs (KGs) to generate high-quality schemas in Shape Expressions (ShEx).

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Yunhai Hu, Yilun Zhao, Chen Zhao, Arman Cohan

What if Othello-Playing Language Models Could See? (2025.findings-emnlp)

Challenge:	MCTS-RAG combines structured reasoning with adaptive retrieval . compared to conventional MCTLs, MCTR-RAg relies on internal model knowledge without external facts .
Approach:	a new approach integrates retrieval-augmented generation and Monte Carlo Tree Search to enhance reasoning capabilities of small language models.
Outcome:	MCTS-RAG integrates retrieval-augmented generation and Monte Carlo Tree Search to improve reasoning paths.

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Xinyi Chen, Yifei Yuan, Jiaang Li, Serge Belongie, Maarten de Rijke, Anders Søgaard

LLM-Based Web Data Collection for Research Dataset Creation (2025.findings-emnlp)

Challenge:	a multi-modal model trained on move sequences and board images is a popular testbed for language models .
Approach:	They propose a multi-modal model trained jointly on move sequences and board images.
Outcome:	The proposed multi-modal model trains on move sequences and board images.

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Thomas Berkane, Marie-Laure Charpignon, Maimuna S. Majumder

Challenge:	researchers across many fields rely on web data to gain new insights and validate methods.
Approach:	They propose a human-in-the-loop framework that automates web-scale data collection end-to-end using large language models (LLMs)
Outcome:	The proposed framework outperforms existing methods in three different tasks and a user evaluation demonstrates its practical utility.

PsyScam: A Benchmark for Psychological Techniques in Real-World Scams (2025.findings-emnlp)

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Shang Ma, Tianyi Ma, Jiahao Liu, Wei Song, Zhenkai Liang, Xusheng Xiao, Yanfang Ye

Challenge:	PTs are employed by scammers to manipulate victims and cause lasting psychological trauma.
Approach:	They propose a benchmark to capture the PTs employed in real-worldscam reports and investigate how LLMs can be utilized to generate variants of scams based on the pts and the contexts provided by thesescams.
Outcome:	The proposed model can generate variants of scams based on the PTs employed in real-world scam reports and the contexts provided by these scams.

LoRaDA: Low-Rank Direct Attention Adaptation for Efficient LLM Fine-tuning (2025.findings-emnlp)

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Zhangming Li, Qinghao Hu, Yiqun Chen, Peisong Wang, Yifan Zhang, Jian Cheng

Inductive Reasoning on Few-Shot Knowledge Graphs with Task-Aware Language Models (2025.findings-emnlp)

Challenge:	Recent advances in parameter-efficient fine-tuning techniques allow for adjustments to only a minor fraction of the parameters of language models.
Approach:	They propose a low-rank direct attention adapted method for efficient LLM fine-tuning . they propose LMAM, which can bring negative attention to self-attention modules .
Outcome:	The proposed method outperforms the full fine-tuning method by 2.1% on GLUE benchmark.

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Cheng Yan, Feng Zhao, Ruilin Zhao, Hong Zhang

ForestCast: Open-Ended Event Forecasting with Semantic News Forest (2025.findings-emnlp)

Challenge:	Current knowledge graph reasoning methods rely on extensive structural information to perform in these few-shot scenarios.
Approach:	They propose a task-aware language model that activates the language model’s in-context learning ability for structured KG tasks.
Outcome:	The proposed method achieves state-of-the-art in few-shot scenarios while reducing the inference time required by previous methods.

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Zi Yu, Shaoxiang Wang, Guozheng Li, Yu Zhang, Chi Harold Liu

Challenge:	Existing approaches and datasets overlook the complex relationships among events . current research lacks comprehensive evaluation methods to evaluate OEEF .
Approach:	They propose a prediction pipeline that extracts forecast-relevant events from news data . forestcast organizes news events into a story tree and predicts subsequent events along each path .
Outcome:	The proposed pipeline extracts forecast-relevant events from news data and predicts subsequent events along each path.

Agentic Medical Knowledge Graphs Enhance Medical Question Answering: Bridging the Gap Between LLMs and Evolving Medical Knowledge (2025.findings-emnlp)

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Mohammad Reza Rezaei, Reza Saadati Fard, Jayson Lee Parker, Rahul G Krishnan, Milad Lankarany

Text Anomaly Detection with Simplified Isolation Kernel (2025.findings-emnlp)

Challenge:	Large Language Models have greatly advanced medical Question Answering (QA) however, the rapid evolution of medical knowledge and manual updating of domain-specific resources can undermine reliability of these systems.
Approach:	AMG-RAG automates the construction and continuous updating of Medical Knowledge Graph (MKG) . afriq: rapid evolution of medical knowledge and manual updating can undermine reliability of LLMs .
Outcome:	AMG-RAG achieves an F1 score of 74.1% on MEDQA and an accuracy of 66.34% on medMCQA.

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Yang Cao, Sikun Yang, Yujiu Yang, Lianyong Qi, Ming Liu

Idola Tribus of AI: Large Language Models tend to perceive order where none exists (2025.findings-emnlp)

Challenge:	High-dimensional dense embeddings extracted by large language models pose memory requirements and high computation time.
Approach:	They propose a method that maps high-dimensional dense embeddings to lower-dimensional sparse representations while preserving crucial anomaly characteristics.
Outcome:	The proposed method achieves better detection performance than 11 SOTA anomaly detection algorithms while maintaining computational efficiency and low memory cost.

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Shin-nosuke Ishikawa, Masato Todo, Taiki Ogihara, Hirotsugu Ohba

Thunder-DeID: Accurate and Efficient De-identification Framework for Korean Court Judgments (2025.findings-emnlp)

Challenge:	a tendency of large language models to generate absurd patterns is observed . authors say this is a limitation of the models' ability to perform complex tasks .
Approach:	We present a tendency of large language models to generate absurd patterns . authors conducted an experiment to evaluate logical consistency and self-coherence of LLMs .
Outcome:	a recent study shows that large language models generate absurd patterns despite their inadequacy . the model over-recognized patterns that were inconsistent with the given numbers, the study finds .

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Sungeun Hahm, Heejin Kim, Gyuseong Lee, Hyunji M. Park, Jaejin Lee

Multi-Agent Autonomous Driving Systems with Large Language Models: A Survey of Recent Advances, Resources, and Future Directions (2025.findings-emnlp)

Challenge:	a recent study shows that the current de-identification process is inadequate for court judgments at scale .
Approach:	They propose a framework for de-identification that aligns with relevant laws and practices . they construct and release the first Korean legal dataset containing annotated judgments .
Outcome:	The proposed framework achieves state-of-the-art in the de-identification of court judgments.

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Yaozu Wu, Dongyuan Li, Yankai Chen, Renhe Jiang, Henry Peng Zou, Wei-Chieh Huang, Yangning Li, Liancheng Fang, Zhen Wang, Philip S. Yu

Comprehensive Evaluation on Lexical Normalization: Boundary-Aware Approaches for Unsegmented Languages (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) are used to assist with driving decisions, but they face limitations in perception and computational demands.
Approach:	They propose a survey of LLM-based multi-agent ADSs and their applications . they analyze agent-human interactions in scenarios where LLM agents engage with humans .
Outcome:	The proposed approach reduces human intervention and improves safety and efficiency.

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Shohei Higashiyama, Masao Utiyama

Challenge:	Lexical normalization research has sought to tackle the challenge of processing informal expressions in user-generated text.
Approach:	They focus on Japanese normalization and developing methods based on state-of-the-art pre-trained models .
Outcome:	The proposed methods achieve high accuracy and efficiency across multiple evaluation perspectives.

Explainable Text Classification with LLMs: Enhancing Performance through Dialectical Prompting and Explanation-Guided Training (2025.findings-emnlp)

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Huaming Du, Lei Yuan, Cancan Feng, Guisong Liu, Gang Kou, Carl Yang

MultiPL-MoE: Multi-Programming-Lingual Extension of Large Language Models through Hybrid Mixture-of-Experts (2025.findings-emnlp)

Challenge:	Existing explanation methods that generate keywords may be less effective due to missing critical contextual information.
Approach:	They propose a new method to generate explanations for possible labels using LLMs and a dialectical prompt.
Outcome:	The proposed method significantly improves accuracy and explanation quality over state-of-the-art methods on multiple datasets from diverse domains.

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Qing Wang, Xue Han, Jiahui Wang, Lehao Xing, Qian Hu, Lianlian Zhang, Chao Deng, Junlan Feng

AutoSpec: An Agentic Framework for Automatically Drafting Patent Specification (2025.findings-emnlp)

Challenge:	MultiPL is a special case of multiple natural languages and requires limited computational resources to generate multilingual code.
Approach:	They propose to extend LLMs by combining two paired experts to optimize expert selection at token and segment levels.
Outcome:	The proposed extension improves the performance of the base LLMs while retaining the most popular ones using limited computational resources.

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Ryan Shea, Zhou Yu

LimaCost: Data Valuation for Instruction Tuning of Large Language Models (2025.findings-emnlp)

Challenge:	drafting a patent application is expensive and time-consuming, making it a prime candidate for automation.
Approach:	a new framework automates the process of drafting a patent application . the framework decomposes drafting into manageable subtasks .
Outcome:	a new framework outperforms existing baselines on drafting patent specification tasks.

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Hyeonseok Moon, Jaehyung Seo, Seonmin Koo, Jinsung Kim, Young-kyoung Ham, Jiwon Moon, Heuiseok Lim

Two Challenges, One Solution: Robust Multimodal Learning through Dynamic Modality Recognition and Enhancement (2025.findings-emnlp)

Challenge:	Instruction tuning is an effective approach for aligning large language models with human intentions.
Approach:	They propose a data quality measure that exhibits a strong correlation with model performance.
Outcome:	The proposed measure exhibits a strong correlation with model performance.

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Lanxin Bi, Yunqi Zhang, Luyi Wang, Yake Niu, Hui Zhao

SwiftPrune: Hessian-Free Weight Pruning for Large Language Models (2025.findings-emnlp)

Challenge:	Existing methods require full-modality data during training phase or require explicit annotations to detect missing modalities.
Approach:	They propose a Dynamic modality Recognition and Enhancement for Adaptive Multimodal fusion framework that directs selective reconstruction of missing or underperforming modalities.
Outcome:	The proposed framework outperforms several baseline and state-of-the-art models on three benchmark datasets.

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Yuhan Kang, Yang Shi, Mei Wen, Jun He, Jianchao Yang, Zeyu Xue, Jing Feng, Xinwang Liu

Training LLMs for Optimization Modeling via Iterative Data Synthesis and Structured Validation (2025.findings-emnlp)

Challenge:	a novel post-training pruning method relies on the Hessian matrix to perform pruning . current pruning methods are computationally intensive and lack performance due to second-order derivative calculations.
Approach:	They propose a Hessian-free weight pruning method that reduces computational burden . they use an Exponentially Weighted Moving Average technique to bypass weight sorting .
Outcome:	The proposed method achieves hardware-efficient model compression by eliminating computational intensive calculations.

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Yang Wu, Yifan Zhang, Yurong Wu, Yuran Wang, Junkai Zhang, Jian Cheng

Exploiting Prompt-induced Confidence for Black-Box Attacks on LLMs (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) are a promising tool for OR, but they face challenges when dealing with complex problems.
Approach:	They propose a framework that augments existing datasets and generates high-quality fine-tuning data tailored to OR.
Outcome:	The proposed framework augments existing datasets and generates high-quality fine-tuning data . it prevents error propagation and ensures the quality of the generated dataset .

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Meina Chen, Yihong Tang, Kehai Chen

DPF-CM: A Data Processing Framework with Privacy-Preserving Vector Databases for Chinese Medical LLMs Training and Deployment (2025.findings-emnlp)

Challenge:	Large language models (LLMs) are vulnerable to adversarial attacks even in strict black-box settings with only hard-label feedback.
Approach:	They propose a black-box framework that leverages prompt-induced confidence as an auxiliary signal to guide attacks.
Outcome:	The proposed framework improves the attack success rate and query efficiency while maintaining semantic fidelity.

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Wei Huang, Anda Cheng, Zhao Zhang, Yinggui Wang

Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise Reward (2025.findings-emnlp)

Challenge:	a new framework for training data processing for Chinese medical language models is proposed . experimental results show that the framework significantly improves model accuracy .
Approach:	They propose a data processing framework for Chinese medical language models training and deployment . the framework is based on a question-oriented model training strategy and privacy preservation .
Outcome:	The proposed framework significantly improves model accuracy and reduces privacy leakage by 27%.

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Han Weng, Puzhen Wu, Cui Longjie, Yi Zhan, Boyi Liu, Yuanfeng Song, Dun Zeng, Yingxiang Yang, Qianru Zhang, Dong Huang, Xiaoming Yin, Yang Sun, Xing Chen

StatsChartMWP: A Dataset for Evaluating Multimodal Mathematical Reasoning Abilities on Math Word Problems with Statistical Charts (2025.findings-emnlp)

Challenge:	Existing methods to enhance performance of large language models (LLMs) on Text-to-SQL tasks rely on execution-based or LLM-based reward models.
Approach:	They propose a reward model framework for RL-based Text-to-SQL that employs the GMNScore outcome reward model.
Outcome:	The proposed reward model outperforms existing reward models on standard benchmarks including Spider and BIRD.

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Dan Zhu, Tianqiao Liu, Zitao Liu

Logic-Thinker: Teaching Large Language Models to Think more Logically. (2025.findings-emnlp)

Challenge:	StatsChartMWP is a dataset for evaluating visual mathematical reasoning abilities on math word problems with statistical charts.
Approach:	They propose a dataset for evaluating visual mathematical reasoning abilities on math word problems with statistical charts.
Outcome:	The proposed model is more effective than open-source approaches.

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Chengyao Wen, Qiang Cheng, Shaofei Wang, Zhizhen Liu, Deng Zhao, Lei Liang

ACEBench: A Comprehensive Evaluation of LLM Tool Usage (2025.findings-emnlp)

Challenge:	Recent Large Reasoning Models (LRMs) have demonstrated the ability to generate long chains of thought (LongCoT) LongCoT still faces challenges such as redundancy and logical incoherence.
Approach:	They propose a neural-symbolic reasoning framework that generates chains of thought . they propose Logic-Thinker, which transforms symbolic solvers into chains of thoughts .
Outcome:	The proposed framework outperforms models fine-tuned with ThinkerCoT on logic reasoning tasks.

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Chen Chen, Xinlong Hao, Weiwen Liu, Xu Huang, Xingshan Zeng, Shuai Yu, Dexun Li, Yuefeng Huang, Xiangcheng Liu, Wang Xinzhi, Wu Liu

RevPRAG: Revealing Poisoning Attacks in Retrieval-Augmented Generation through LLM Activation Analysis (2025.findings-emnlp)

Challenge:	Existing benchmarks for evaluating LLMs’ tool usage face several limitations: limited evaluation scenarios, lacking assessments in real multi-turn dialogue contexts; narrow evaluation dimensions, with insufficient detailed assessments of how LLM use tools; and reliance on LLM or real API executions for evaluation, which introduces significant overhead.
Approach:	ACEBench is a benchmark for evaluating tool usage in Large Language Models . it categorizes data into three primary types based on evaluation methodology: Normal, Special, and Agent.
Outcome:	ACEBench categorizes data into three primary types based on evaluation methodology: Normal, Special, and Agent.

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Xue Tan, Hao Luan, Mingyu Luo, Xiaoyan Sun, Ping Chen, Jun Dai

DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression (2025.findings-emnlp)

Challenge:	Retrieval-Augmented Generation (RAG) enriches the input to LLMs by retrieving information from the relevant knowledge database.
Approach:	They propose to use a knowledge database to enrich the input of LLMs by retrieving information from the relevant knowledge database.
Outcome:	The proposed approach can achieve 98% true positive rate while maintaining a false positive rate close to 1%.

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Wei Huang, Huang Wei, Yinggui Wang

CoAT: Chain-of-Associated-Thoughts Framework for Enhancing Large Language Models Reasoning (2025.findings-emnlp)

Challenge:	Large language models excel in general tasks but struggle with domain-specific ones, requiring fine-tuning with specific data.
Approach:	They propose a Data and Model Compression Framework that categorizes data filtering methodologies into three distinct paradigms: (1) distribution-aware methods, (2) quality-a aware methods, and (3) hybrid approaches considering both dimensions.
Outcome:	The proposed framework can select the optimal LLM while saving approximately 20-fold in training time.

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Jianfeng Pan, Senyou Deng, Shaomang Huang

ChartM3: A Multi-Stage Code-Driven Pipeline for Constructing Multi-Dimensional and Multi-Step Visual Reasoning Data in Chart Comprehension (2025.findings-emnlp)

Challenge:	OpenAI-o1 enables ‘slow thinking’ because it is closer to the human thought process .
Approach:	They propose a new framework that integrates the Monte Carlo Tree Search algorithm and a dynamic mechanism for integrating new key information, termed ‘associative memory’.
Outcome:	The proposed framework improves performance on open-source multi-hop reasoning datasets and more than 15% gain on proprietary CRB dataset.

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Duo Xu, Hao Cheng, Xin Lin, Zhen Xie, Hao Henry Wang

Can LLMs Truly Plan? A Comprehensive Evaluation of Planning Capabilities (2025.findings-emnlp)

Challenge:	Currently, research on complex chart understanding tasks is limited . a pipeline for visual reasoning datasets addresses these limitations .
Approach:	They propose a code-driven pipeline for generating visual reasoning datasets . pipeline integrates retrieval-augmented generation to retrieve professional chart templates .
Outcome:	The proposed pipeline enhances chart diversity and data quality through model-based evaluation.

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Gayeon Jung, HyeonSeok Lim, Minjun Kim, Joon-ho Lim, KyungTae Lim, Hansaem Kim

Challenge:	Existing assessments of planning capabilities of large language models are limited to single-language or specific representation formats.
Approach:	a new benchmark is developed to assess the planning capabilities of large language models.
Outcome:	The Multi-Plan benchmark highlights performance disparities among models . language differences showed minimal impact, while mathematically structured representations improved accuracy .

MARIO-0.5B: A Multi-Agent Lightweight Model for Real-Time Open Information Extraction in Low-Resource Settings (2025.findings-emnlp)

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Donghai Zhang, SHuangtao Yang, Dong Xiaozheng, Wei Song, Bo Fu

BiMax: Bidirectional MaxSim Score for Document-Level Alignment (2025.findings-emnlp)

Challenge:	Large language models have shown remarkable capabilities in open information extraction, but their resource requirements often restrict their deployment in resource-constrained industrial settings.
Approach:	They introduce an ultra-lightweight large language model trained on instruction-based samples in Chinese, English, Korean, and Russian.
Outcome:	The proposed model outperforms large-scale models with up to 70B parameters, reducing computational resources by 140x and delivering 11x faster response times.

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Xiaotian Wang, Takehito Utsuro, Masaaki Nagata

DocMMIR: A Framework for Document Multi-modal Information Retrieval (2025.findings-emnlp)

Challenge:	Document alignment is necessary for the hierarchical mining of documents across source and target languages.
Approach:	They propose a cross-lingual Bidirectional Maxsim score for computing doc-to-doc similarity.
Outcome:	The proposed method achieves accuracy comparable to OT with an approximate 100-fold speed increase.

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Zirui Li, Siwei Wu, Yizhi Li, Xingyu Wang, Yi Zhou, Chenghua Lin

MoVoC: Morphology-Aware Subword Construction for Ge’ez Script Languages (2025.findings-emnlp)

Challenge:	Existing multi-modal information retrieval models lack a comprehensive exploration of document-level retrieval . existing models suffer from the absence of cross-domain datasets at this granularity.
Approach:	They propose a multi-modal document retrieval framework to unify diverse document formats and domains with a comprehensive retrieval scenario.
Outcome:	The proposed framework improves document retrieval performance on a large multimodal dataset.

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Hailay Kidu Teklehaymanot, Dren Fazlija, Wolfgang Nejdl

MMA: Cross-Domain Knowledge Integration via Mixture of Multi-Domain Agents (2025.findings-emnlp)

Challenge:	Subword-based tokenization methods fail to preserve morphological boundaries, a limitation especially pronounced in low-resource, morphology complex languages such as those written in the Ge‘ez script.
Approach:	They propose a tokenizer that integrates supervised morphological analysis into the subword vocabulary and propose morpheme-based tokenization with Byte Pair Encoding (BPE) tokens.
Outcome:	The proposed tokenizer preserves morphological integrity while maintaining lexical meaning.

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Kehang Jia, Juntao Li, Xiaobo Liang, Yisheng Xiao, Yixuan Yang, Min Zhang

Challenge:	achieving synergistic improvements between generalization and domain specialization remains a challenge in pre-training and post-training.
Approach:	They propose a test-time cross-domain knowledge integration method that integrates general-purpose and domain-specific models to enhance their performance on complex, domainspecific tasks.
Outcome:	The proposed method combines the outputs of general-purpose and domain-specific models to improve their performance on complex, domainspecific tasks.

HAWK: Highlighting Entity-aware Knowledge for Alleviating Information Sparsity in Long Contexts (2025.findings-emnlp)

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Seonmin Koo, Jinsung Kim, Chanjun Park, Heuiseok Lim

Sensitivity-LoRA : Low-Load Sensitivity-Based Fine-Tuning for Large Language Models (2025.findings-emnlp)

Challenge:	a problem of information sparsity in QA tasks is causing fragmentation of textual data . highlighting entity-AWare Knowledge (HAWK) framework can be used to address this problem .
Approach:	a framework is proposed to highlight key information in a context and structuralize it in an entity-aware manner.
Outcome:	a proposed framework improves QA tasks with long contexts by highlighting key information in a context . the framework achieves a 27.6-point F1 score increase and an average win rate of 76.75% .

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Hao Zhang, Bo Huang, Zhenjia Li, Xi Xiao, Hui Yi Leong, Zumeng Zhang, Xinwei Long, Tianyang Wang, Hao Xu

ROSE: A Reward-Oriented Data Selection Framework for LLM Task-Specific Instruction Tuning (2025.findings-emnlp)

Challenge:	Low-Rank Adaptation (LoRA) is a promising approach to adapting LLMs to specialized tasks . existing rank allocation techniques remain computationally inefficient and unstable .
Approach:	They propose a low-rank adapted model that approximates model weight updates using low-ranked decomposition.
Outcome:	The proposed method is limited by its uniform rank allocation to each incremental matrix . it leverages the second-order derivatives of the loss function to capture weight sensitivity .

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Yang Wu, Huayi Zhang, Yizheng Jiao, Lin Ma, Xiaozhong Liu, Jinhong Yu, Dongyu Zhang, Dezhi Yu, Wei Xu

SimBA: Simplifying Benchmark Analysis Using Performance Matrices Alone (2025.findings-emnlp)

Challenge:	Prevailing methods for task-specific instruction tuning use similarity metrics to select training data . but instruction tuning loss often fails to exhibit a monotonic relationship with actual task performance .
Approach:	They propose a task-specific instruction tuning method that leverages pairwise preference loss as a reward signal.
Outcome:	The proposed method surpasses state-of-the-art methods for task-specific instruction tuning.

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Nishant Subramani, Alfredo Gomez, Mona T. Diab

Paraphrasing

Challenge:	Modern language models are evaluated on large benchmarks, which are difficult to make sense of.
Approach:	They propose a framework to Simplify Benchmark Analysis using model-centric evaluation numbers.
Outcome:	The proposed framework can be applied to HELM, MMLU, and BigBenchLite benchmarks.

MarathiEmoExplain: A Dataset for Sentiment, Emotion, and Explanation in Low-Resource Marathi (2025.findings-emnlp)

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Anuj Kumar, Mohammed Faisal Sayed, Satyadev Ahlawat, Yamuna Prasad

Active Domain Knowledge Acquisition with 100-Dollar Budget: Enhancing LLMs via Cost-Efficient, Expert-Involved Interaction in Sensitive Domains (2025.findings-emnlp)

Challenge:	Marathi is the third most widely spoken language in India with over 83 million native speakers . available Marath datasets are limited to coarse sentiment labels and lack fine-grained emotional categorization or interpretability through explanations.
Approach:	They propose to annotate Marathi sentences labeled with sentiment, emotion and a corresponding natural language justification.
Outcome:	The proposed dataset provides a benchmark for future research in multilingual and explainable NLP.

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Yang Wu, Raha Moraffah, Rujing Yao, Jinhong Yu, Zhimin Tao, Xiaozhong Liu

Structure-aware Propagation Generation with Large Language Models for Fake News Detection (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have demonstrated an impressive level of general knowledge, but often struggle in highly specialized domains due to the lack of expert knowledge.
Approach:	They propose a framework to actively engage domain experts within a fixed budget to enhance domain-specific LLMs.
Outcome:	The proposed framework improves LLMs in highly specialized domains while adhering to budget constraints.

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Mengyang Chen, Lingwei Wei, Wei Zhou, Songlin Hu

Ethical NLP

Challenge:	propagation-based methods for fake news detection often lack structural data . authors propose a structure-aware synthetic propagation enhanced detection framework .
Approach:	They propose a structure-aware synthetic propagation enhanced detection framework to capture real-world propagation.
Outcome:	The proposed framework captures structural dynamics from real propagation, while ignoring structural patterns.

UniCoM: A Universal Code-Switching Speech Generator (2025.findings-emnlp)

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Sangmin Lee, Woojin Chung, Seyun Um, Hong-Goo Kang

Mitigating Sequential Dependencies: A Survey of Algorithms and Systems for Generation-Refinement Frameworks in Autoregressive Models (2025.findings-emnlp)

Challenge:	Code-switching (CS) is a common phenomenon in real-world conversations and poses significant challenges for multilingual speech technology.
Approach:	They propose a pipeline for generating high-quality, natural CS samples without altering sentence semantics.
Outcome:	The proposed pipeline generates high-quality, natural CS samples without altering sentence semantics without alteration of sentence semantic.

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Yunhai Hu, Zining Liu, Zhenyuan Dong, Tianfan Peng, Bradley McDanel, Sai Qian Zhang

Challenge:	Sequential dependencies present a fundamental bottleneck in deploying large-scale autoregressive models .
Approach:	They analyze methods based on generation strategies and refinement mechanisms . they examine deployment strategies across computing environments and explore applications spanning text, images, and speech generation.
Outcome:	The proposed frameworks can be used to improve the quality of autoregressive models.

Do We Really Need All Those Dimensions? An Intrinsic Evaluation Framework for Compressed Embeddings (2025.findings-emnlp)

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Nathan Inkiriwang, Necva Bölücü, Garth Tarr, Maciej Rybinski

Mixture of LoRA Experts for Continual Information Extraction with LLMs (2025.findings-emnlp)

Challenge:	Existing evaluation methods for compressed text embeddings are either expensive or too simplistic.
Approach:	They propose a task-agnostic intrinsic evaluation framework that provides a reliable proxy for downstream performance.
Outcome:	The proposed framework provides a reliable proxy for downstream performance.

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Zitao Wang, Xinyi Wang, Wei Hu

Spelling-out is not Straightforward: LLMs’ Capability of Tokenization from Token to Characters (2025.findings-emnlp)

Challenge:	Existing methods to continual information extraction are either task-specialized for a single task or suffer from catastrophic forgetting and insufficient knowledge transfer in continual IE.
Approach:	They propose a continual IE model that uses token-level mixture of LoRA experts with LLMs to extract emerging information across diverse IE tasks incessantly while avoiding forgetting.
Outcome:	The proposed model achieves state-of-the-art performance, effectively mitigating catastrophic forgetting and enhancing knowledge transfer in continual IE.

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Tatsuya Hiraoka, Kentaro Inui

OAgents: An Empirical Study of Building Effective Agents (2025.findings-emnlp)

Challenge:	Large language models (LLMs) can spell out tokens character by character with high accuracy, yet struggle with more complex character-level tasks.
Approach:	They examine how large language models internally represent character-level information during the spelling-out process.
Outcome:	The embedding layer does not fully encode character-level information, especially beyond the first character.

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He Zhu, Tianrui Qin, King Zhu, Heyuan Huang, Yeyi Guan, Jinxiang Xia, Hanhao Li, Yi Yao, Ningning Wang, Pai Liu, Tianhao Peng, Xin Gui, Li Xiaowan, Yuhui Liu, Xiangru Tang, Jian Yang, Ge Zhang, Xitong Gao, Yuchen Eleanor Jiang, Changwang Zhang, Jun Wang, Jiaheng Liu, Wangchunshu Zhou

2Columns1Row: A Russian Benchmark for Textual and Multimodal Table Understanding and Reasoning (2025.findings-emnlp)

Challenge:	a recent study shows that agent research practices are far from standard, rigorous . lack of a standard evaluation protocol makes previous works not reproducible, authors say .
Approach:	They conduct an empirical study on the GAIA benchmark to investigate agent design choices . they find that lack of a standard evaluation protocol makes previous works not reproducible .
Outcome:	The proposed framework achieves state-of-the-art performance among open-source projects.

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Vildan Saburov, Daniil Vodolazsky, Danil Sazanakov, Alena Fenogenova

Permitted Knowledge Boundary: Evaluating the Knowledge-Constrained Responsiveness of Large Language Models (2025.findings-emnlp)

Challenge:	2Columns1Row is the first open-source benchmark for the table understanding task in Russian.
Approach:	They propose a benchmark for table understanding in Russian using textual and multimodal inputs.
Outcome:	The proposed benchmark evaluates models' ability to reason about relationships between rows and columns in tables using text-only and multimodal approaches.

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Wenrui Bao, Kai Wang, Siqiang Luo, Xiang Li

A Closer Look at Bias and Chain-of-Thought Faithfulness of Large (Vision) Language Models (2025.findings-emnlp)

Challenge:	Recent research has raised concerns about the controllability of large language models.
Approach:	They propose to define a "boundary bias" to depict KCR in large language models . they propose to quantify the boundary bias of LLMs and assess the KCR .
Outcome:	The proposed model is based on two new datasets to assess its performance.

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Sriram Balasubramanian, Samyadeep Basu, Soheil Feizi

From Remembering to Metacognition: Do Existing Benchmarks Accurately Evaluate LLMs? (2025.findings-emnlp)

Challenge:	Chain-of-thought reasoning improves performance of large language models, but is it faithfully reflecting internal processes?
Approach:	They propose a new evaluation pipeline for categorizing bias articulation patterns and a novel evaluation pipeline to examine CoT faithfulness in large vision-language models.
Outcome:	The proposed evaluation pipeline enables significantly more precise analysis of CoT reasoning than previous methods.

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Geng Zhang, Yizhou Ying, Sihang Jiang, Jiaqing Liang, Guanglei Yue, Yifei Fu, Hailin Hu, Yanghua Xiao

How a Bilingual LM Becomes Bilingual: Tracing Internal Representations with Sparse Autoencoders (2025.findings-emnlp)

Challenge:	Existing benchmark datasets focus on low-level cognitive tasks while providing limited coverage of higher-level reasoning skills.
Approach:	They analyze the cognitive depth of popular LLM benchmarks using Bloom’s Taxonomy to evaluate both the cognitive and knowledge dimensions.
Outcome:	The results show that incorporating higher-level cognitive instructions into the current instruction fine-tuning process improves model performance.

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Tatsuro Inaba, Go Kamoda, Kentaro Inui, Masaru Isonuma, Yusuke Miyao, Yohei Oseki, Yu Takagi, Benjamin Heinzerling

MultiConIR: Towards Multi-Condition Information Retrieval (2025.findings-emnlp)

Challenge:	Using sparse autoencoders, we explore how bilingual language models develop complex internal representations.
Approach:	They employ sparse autoencoders to analyze bilingual language models' internal representations.
Outcome:	The proposed method integrates decomposed representations from a fully trained model into a mid-training model.

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Xuan Lu, Sifan Liu, Bochao Yin, Yongqi Li, Xinghao Chen, Hui Su, Yaohui Jin, Wenjun Zeng, Xiaoyu Shen

HMCL: Task-Optimal Text Representation Adaptation through Hierarchical Contrastive Learning (2025.findings-emnlp)

Challenge:	MultiConIR is a benchmark designed to evaluate retrieval and reranking models under nuanced multi-condition query scenarios.
Approach:	They propose a benchmark to evaluate retrieval and reranking models under nuanced multi-condition query scenarios.
Outcome:	The proposed benchmark evaluates retrieval and reranking models under nuanced multi-condition query scenarios across five domains.

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Zhenyi Wang, Yapeng Jia, Haiyan Ning, Peng Wang, Dan Wang, Yitao Cao

KBAlign: Efficient Self Adaptation on Specific Textual Knowledge Bases (2025.findings-emnlp)

Challenge:	Hierarchical Multilevel Contrastive Learning (HMCL) improves text representation for general large language models.
Approach:	a new contrastive learning framework is developed to improve general large language models . HMCL integrates 3-level semantic differentiation and unifies contrastive and pair classification into a strategy .
Outcome:	HMCL outperforms unsupervised methods and supervised fine-tuning approaches in multi-domain and multilingual benchmarks.

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Zheni Zeng, Yuxuan Chen, Shi Yu, Ruobing Wang, Yukun Yan, Zhenghao Liu, Shuo Wang, Xu Han, Zhiyuan Liu, Maosong Sun

Revisiting Chain-of-Thought Prompting: Zero-shot Can Be Stronger than Few-shot (2025.findings-emnlp)

Challenge:	Existing methods for retrieval-augmented generation (RAG) are limited and fine-tuning incurs prohibitive costs of external signals.
Approach:	They propose a self-supervised framework that enhances RAG systems through efficient model adaptation.
Outcome:	The proposed framework achieves 90% of the performance gain obtained through GPT-4-supervised adaptation while relying entirely on self-annotation of much smaller models.

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Xiang Cheng, Chengyan Pan, Minjun Zhao, Deyang Li, Fangchao Liu, Xinyu Zhang, Xiao Zhang, Yong Liu

RMTBench: Benchmarking LLMs Through Multi-Turn User-Centric Role-Playing (2025.findings-emnlp)

Challenge:	In-Context Learning (ICL) is an essential emergent ability of Large Language Models (LLMs).
Approach:	They introduce CoT to exemplars of ICL to enhance the reasoning capability . however, it remains unclear whether CoT exemplar is still beneficial for recent, stronger models in such tasks.
Outcome:	The enhanced exemplars fail to improve the model’s reasoning performance, despite being constructed using answers from advanced models such as Qwen2.5-Max and DeepSeek-R1.

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Hao Xiang, Tianyi Tang, Yang Su, Bowen Yu, An Yang, Fei Huang, Yichang Zhang, Yaojie Lu, Hongyu Lin, Xianpei Han, Jingren Zhou, Junyang Lin, Le Sun

Smart-Searcher: Incentivizing the Dynamic Knowledge Acquisition of LLMs via Reinforcement Learning (2025.findings-emnlp)

Challenge:	Existing benchmarks focus on character-centric approach and fail to reflect real-world applications.
Approach:	RMTBench is a user-centric bilingual role-playing benchmark featuring 80 diverse characters and over 8,000 dialogue rounds.
Outcome:	RMTBench features 80 diverse characters and over 8,000 dialogue rounds.

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Huatong Song, Jinhao Jiang, Wenqing Tian, Zhipeng Chen, Yuhuan Wu, Jiahao Zhao, Yingqian Min, Xin Zhao, Lei Fang, Ji-Rong Wen

InteGround: On the Evaluation of Verification and Retrieval Planning in Integrative Grounding (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) are powerful but prone to hallucinations due to static knowledge. Retrieval-augmented generation (RAG) helps by injecting external information, but current methods are costly, generalize poorly, or ignore the model’s internal knowledge.
Approach:	They propose a framework to train large language models to leverage both internal and external knowledge sources.
Outcome:	The proposed framework outperforms existing methods and achieves efficient retrieval-augmented reasoning.

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Cheng Jiayang, Qianqian Zhuang, Haoran Li, Chunkit Chan, Xin Liu, Lin Qiu, Yangqiu Song

MM-CRITIC: A Holistic Evaluation of Large Multimodal Models as Multimodal Critique (2025.findings-emnlp)

Challenge:	Existing grounding approaches work well for simple queries, but many real-world information needs require synthesizing multiple pieces of evidence.
Approach:	They introduce "integrative grounding" to evaluate the ability to ground large language models in external knowledge sources.
Outcome:	The proposed approach is robust to redundant evidence, but rationalizes using internal knowledge when information is incomplete.

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Gailun Zeng, Ziyang Luo, Hongzhan Lin, Yuchen Tian, Kaixin Li, Ziyang Gong, Jianxiong Guo, Jing Ma

On the Correspondence between the Squared Norm and Information Content in Text Embeddings (2025.findings-emnlp)

Challenge:	e MM-CRITIC is a holistic benchmark for evaluating the critique ability of Large Multimodal Models (LMMs) covering 8 main task types and over 500 tasks, covering 4471 samples.
Approach:	They introduce a holistic benchmark for evaluating the critique ability of Large Multimodal Models across multiple dimensions: basic, correction, and comparison.
Outcome:	The proposed benchmark covers 8 main task types and over 500 tasks and is composed of 4471 samples.

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Enrique Amigo, Adrian Ghajari, Alejandro Benito-Santos, Diego De La Fuente Rodríguez

Adversary-Aware DPO: Enhancing Safety Alignment in Vision Language Models via Adversarial Training (2025.findings-emnlp)

Challenge:	Existing evidence of the correspondence between the squared norm of an embedding and the information content of the text it represents is lacking.
Approach:	They propose to derive two sufficient theoretical conditions for this correspondence to hold in embedding models.
Outcome:	The proposed embeddings exhibit a strong correspondence with the word embeddables and the subword token composition functions.

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Fenghua Weng, Jian Lou, Jun Feng, Minlie Huang, Wenjie Wang

SLiNT: Structure-aware Language Model with Injection and Contrastive Training for Knowledge Graph Completion (2025.findings-emnlp)

Challenge:	Adversary-aware DPO (ADPO) is a training framework that explicitly considers adversary.
Approach:	a new framework integrates adversarial training into a pre-trained large language model to enhance safety alignment . adversary-aware DPO provides a framework that explicitly considers adversary .
Outcome:	a new training framework outperforms baselines in safety alignment and general utility of large language models.

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Mengxue Yang, Chun Yang, Jiaqi Zhu, Jiafan Li, Jingqi Zhang, Yuyang Li, Ying Li

LAVa: Layer-wise KV Cache Eviction with Dynamic Budget Allocation (2025.findings-emnlp)

Challenge:	Large language models (LLMs) lack structural information and semantic context to infer missing entities . large language models often lack structural signals to infuse missing entities into knowledge graphs .
Approach:	a modular framework integrates structural information and semantic context into a frozen LLM backbone for link prediction.
Outcome:	a new framework integrates KG-derived structural information and semantic context to infer missing entities.

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Yiqun Shen, Song Yuan, Zhengze Zhang, Xiaoliang Wang, Daxin Jiang, Nguyen Cam-Tu

Challenge:	Existing methods for cache compression are heuristic and lack dynamic budget allocation . cnn's john mccartney and johnny mccain present a new approach for cache eviction and dynamic budgets .
Approach:	They propose a unified framework for cache compression that minimizes information loss in transformer residual streams.
Outcome:	The proposed method consistently maintains top performance across task types.

LoRA-PAR: A Flexible Dual-System LoRA Partitioning Approach to Efficient LLM Fine-Tuning (2025.findings-emnlp)

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Yining Huang, Bin Li, Keke Tang, Meilian Chen

SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis (2025.findings-emnlp)

Challenge:	Large-scale generative models like DeepSeek-R1 and OpenAI-O1 benefit substantially from chain-of-thought reasoning, yet pushing their performance typically requires vast data, large model sizes, and full-parameter fine-tuning.
Approach:	They propose a dual-system LoRA framework that partitions data and parameters by System 1 or System 2 demands and adopts a two-stage fine-tuning strategy to enhance knowledge and intuition.
Outcome:	The proposed framework partitions data and parameters by System 1 or System 2 demands, using fewer yet more focused parameters for each task.

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Shuang Sun, Huatong Song, Yuhao Wang, Ruiyang Ren, Jinhao Jiang, Junjie Zhang, Fei Bai, Jia Deng, Xin Zhao, Zheng Liu, Lei Fang, Zhongyuan Wang, Ji-Rong Wen

LLaVE: Large Language and Vision Embedding Models with Hardness-Weighted Contrastive Learning (2025.findings-emnlp)

Challenge:	Existing approaches to deep search training lack high-quality training trajectories, prohibitive computational costs and lack of high-fidelity training data.
Approach:	They propose a framework that synthesizes high-quality training data by simulating real user interactions in live web search environments.
Outcome:	The proposed framework synthesizes high-quality training data by simulating user interactions in live web search environments.

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Zhibin Lan, Liqiang Niu, Fandong Meng, Jie Zhou, Jinsong Su

SampleMix: A Sample-wise Pre-training Data Mixing Strategy by Coordinating Data Quality and Diversity (2025.findings-emnlp)

Challenge:	Existing LMM-based embedding models exhibit a high degree of overlap in similarity distribution between positive and negative pairs, making it challenging to distinguish hard negative pairs effectively.
Approach:	They propose a framework that improves the embedding model's representation learning for negative pairs based on their discriminative difficulty.
Outcome:	The proposed framework improves the embedding model's representation learning for negative pairs based on their discriminative difficulty.

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Xiangyu Xi, Deyang Kong, Jian Yang, Jiawei Yang, Zhengyu Chen, Wei Wang, Jingang Wang, Xunliang Cai, Shikun Zhang, Wei Ye

Evaluating Test-Time Scaling LLMs for Legal Reasoning: OpenAI o1, DeepSeek-R1, and Beyond (2025.findings-emnlp)

Challenge:	Existing methods for pretraining data mixing for large language models neglect significant inter-domain overlaps and commonalities, failing to control the global diversity of the constructed training dataset.
Approach:	They propose a sample-wise data mixture approach that performs global cross-domain sampling by systematically evaluating the quality and diversity of each sample.
Outcome:	The proposed method exceeds existing domain-based methods in multiple downstream tasks and perplexity assessments.

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Yinghao Hu, Yaoyao Yu, Leilei Gan, Bin Wei, Kun Kuang, Fei Wu

LLM Agents for Education: Advances and Applications (2025.findings-emnlp)

Challenge:	Experimental results show that Legal-R1 delivers competitive performance across diverse tasks.
Approach:	They propose to evaluate 12 large language models across 17 legal tasks across statutory and case-law traditions to determine their general reasoning performance.
Outcome:	The proposed model performs well across 17 legal tasks across statutory and case-law traditions.

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Zhendong Chu, Shen Wang, Jian Xie, Tinghui Zhu, Yibo Yan, Jingheng Ye, Aoxiao Zhong, Xuming Hu, Jing Liang, Philip S. Yu, Qingsong Wen

Modeling Subjectivity in Cognitive Appraisal with Language Models (2025.findings-emnlp)

Challenge:	Large Language Model (LLM) agents are transforming education by automating complex tasks and enhancing both teaching and learning processes.
Approach:	This survey analyzes recent advances in applying Large Language Model agents to educational settings . it highlights ethical issues, hallucination and overreliance, and integration with existing ecosystems .
Outcome:	The authors analyze the technologies enabling LLM agents and highlight key challenges in deploying them in educational settings.

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Yuxiang Zhou, Hainiu Xu, Desmond Ong, Maria Liakata, Petr Slovak, Yulan He

Dementia Through Different Eyes: Explainable Modeling of Human and LLM Perceptions for Early Awareness (2025.findings-emnlp)

Challenge:	a new study explores how language models can quantify subjectivity in cognitive appraisal . existing post-hoc calibration methods fail to achieve satisfactory performance .
Approach:	They investigate how language models can quantify subjectivity in cognitive appraisal . existing post-hoc calibration methods often fail to achieve satisfactory performance .
Outcome:	The proposed model can quantify subjectivity in cognitive appraisal using fine-tuned models and prompt-based large language models.

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Lotem Peled-Cohen, Maya Zadok, Nitay Calderon, Hila Gonen, Roi Reichart

Mitigating Hallucinations in Large Vision-Language Models by Self-Injecting Hallucinations (2025.findings-emnlp)

Challenge:	a new study examines how dementia is perceived by non-experts . human perception of dementia is inconsistent and relies on a narrow set of cues compared to LLMs based on broader clinical patterns .
Approach:	They propose a method that uses LLMs to extract high-level, expert-guided features . human perception of dementia is inconsistent and relies on a narrow set of cues, they say .
Outcome:	The proposed method analyzes picture descriptions to assess whether they were produced by non-experts or by nonexperts.

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Yifan Lu, Ziqi Zhang, Chunfeng Yuan, Jun Gao, Congxuan Zhang, Xiaojuan Qi, Bing Li, Weiming Hu

How Much Do Large Language Models Know about Human Motion? A Case Study in 3D Avatar Control (2025.findings-emnlp)

Challenge:	Existing methods for hallucination mitigation are based on external dependency and require external annotations or auxiliary models for preference data collection.
Approach:	a new method is proposed to help model-generated hallucinations without external dependencies.
Outcome:	a new method that self-injects hallucinations into a generated response improves halluuutations mitigation.

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Kunhang Li, Jason Naradowsky, Yansong Feng, Yusuke Miyao

The Search for Conflicts of Interest: Open Information Extraction in Scientific Publications (2025.findings-emnlp)

Challenge:	a new study explores the human motion knowledge of Large Language Models (LLMs) using 3D avatar control.
Approach:	They use 20 representative motion instructions to interpolate LLMs into avatar animations . they find they are strong at interpreting high-level body movements but struggle with precise body part positioning .
Outcome:	The proposed model is strong at interpreting high-level body movements but struggles with precise body part positioning.

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Garima Gaur, Oana Balalau, Ioana Manolescu, Prajna Upadhyay

On Collaborating Small and Large Models For Few-shot Intent Detection (2025.findings-emnlp)

Challenge:	public repositories of relations of interest are increasingly recommended or mandated in various domains to avoid COIs.
Approach:	They propose an LLM-based open information extraction framework for extracting financial or other types of interesting relations from scientific text.
Outcome:	The proposed framework extracts financial or other types of interesting relations from scientific text.

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Peng Chen, Bang Wang

A Survey on LLMs for Story Generation (2025.findings-emnlp)

Challenge:	Existing methods for few-shot intent detection face high inference cost and label interference.
Approach:	They propose a framework that integrates a small prediction model with a large language model for FSID.
Outcome:	The proposed framework outperforms existing methods on three benchmark datasets.

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Maria Teleki, Vedangi Bengali, Xiangjue Dong, Sai Tejas Janjur, Haoran Liu, Tian Liu, Cong Wang, Ting Liu, Yin Zhang, Frank Shipman, James Caverlee

From Knowledge to Treatment: Large Language Model Assisted Biomedical Concept Representation for Drug Repurposing (2025.findings-emnlp)

Challenge:	Methods for story generation with Large Language Models (LLMs) have come into the spotlight recently.
Approach:	They propose a novel taxonomy of LLMs for story generation consisting of two major paradigms: independent story generation by an LLM, and author-assistance for story creation .
Outcome:	The proposed taxonomy compares existing work on the topic with those of novel author-assistance models.

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Chengrui Xiang, Tengfei Ma, Xiangzheng Fu, Yiping Liu, Bosheng Song, Xiangxiang Zeng

SKRAG: A Retrieval-Augmented Generation Framework Guided by Reasoning Skeletons over Knowledge Graphs (2025.findings-emnlp)

Challenge:	Existing methods for drug repurposing ignore common-sense biomedical concept knowledge in real-world labs, such as mechanistic priors indicating that certain drugs are fundamentally incompatible with specific treatments.
Approach:	They propose a Large Language Model-assisted framework for Drug Repurposing which improves the representation of biomedical concepts within KGs.
Outcome:	The proposed framework improves the representation of biomedical concepts within KGs by extracting treatment-related textual representations of biomedic entities from large language models and fine-tuning knowledge graph embedding models.

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Xiaotong Xu, Yizhao Wang, Yunfei Liu, Shengyang Li

A Generative Framework for Personalized Sticker Retrieval (2025.findings-emnlp)

Challenge:	Existing KG-based question answering frameworks face inefficient subgraph retrieval, limited reasoning capabilities, and high computational costs.
Approach:	They propose a Skeleton-guided RAG framework for knowledge graph question answering . SKRAG leverages a lightweight language model enhanced with the Finite State Machine constraint .
Outcome:	The proposed framework outperforms baselines and general-domain benchmarks on a KGQA dataset in the space science and utilization domain.

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Changjiang Zhou, Ruqing Zhang, Jiafeng Guo, Yu-An Liu, Fan Zhang, Ganyuan Luo, Xueqi Cheng

Bridging Semantic and Modality Gaps in Zero-Shot Captioning via Retrieval from Synthetic Data (2025.findings-emnlp)

Challenge:	Existing relevance-based generative retrieval methods lack personalization, leading to a mismatch between diverse user expectations and the retrieved results.
Approach:	They propose a representation learning model that learns discriminative user representations to encode user-specific sticker preferences.
Outcome:	The proposed framework outperforms state-of-the-art methods in generating relevant stickers for queries.

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Zhiyue Liu, Wenkai Zhou

Humor in Pixels: Benchmarking Large Multimodal Models Understanding of Online Comics (2025.findings-emnlp)

Challenge:	Existing methods for zero-shot image captioning fail to bridge the semantic gap between images and textual inputs.
Approach:	They propose a retrieval-based framework that leverages only existing synthetic image-text pairs as its search corpus to bridge the gap when using synthetic data for captioning.
Outcome:	The proposed method bridges the semantic gap between a synthetic image and its input text . it extracts image-related textual descriptions to mitigate the modality gap during decoding .

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Yuriel Ryan, Rui Yang Tan, Kenny Tsu Wei Choo, Roy Ka-Wei Lee

BiMediX2 : Bio-Medical EXpert LMM for Diverse Medical Modalities (2025.findings-emnlp)

Challenge:	PixelHumor is a benchmark dataset of 2,800 annotated multi-panel comics designed to evaluate LMMs’ ability to interpret multimodal humor and recognize narrative sequences.
Approach:	PixelHumor is a benchmark dataset of 2,800 annotated multi-panel comics designed to evaluate LMMs’ ability to interpret multimodal humor and recognize narrative sequences.
Outcome:	Experiments with state-of-the-art LMMs reveal that top models achieve only 61% accuracy in panel sequencing, far below human performance.

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Sahal Shaji Mullappilly, Mohammed Irfan Kurpath, Sara Pieri, Saeed Yahya Alseiari, Shanavas Cholakkal, Khaled M Aldahmani, Fahad Shahbaz Khan, Rao Muhammad Anwer, Salman Khan, Timothy Baldwin, Hisham Cholakkal

DeMAC: Enhancing Multi-Agent Coordination with Dynamic DAG and Manager-Player Feedback (2025.findings-emnlp)

Challenge:	BiMediX2 is a bilingual (Arabic-English) large multimodal model that supports text-based and image-based medical interactions.
Approach:	They introduce BiMediX2, a bilingual (Arabic-English) Bio-Medical EXpert Large Multimodal Model that supports text-based and image-based medical interactions.
Outcome:	The model outperforms existing models by over 9% in English and more than 20% in Arabic evaluations.

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Yuhan Liu, Cong Xu, Lu Liu, Yihua Wang, Feiyu Chen, Qi Jia, Yaqian Zhao, Zhichun Wang, Xiang Li

Coherence of Argumentative Dialogue Snippets: A New Method for Large Scale Evaluation with an Application to Inference Anchoring Theory (2025.findings-emnlp)

Challenge:	Multi-agent systems (MAS) powered by large language models struggle to adapt to evolving task dependencies and to handle uncertainties.
Approach:	They propose a Dynamic Environment-Aware Manager-Player Agents Coordination framework that enhances multi-agent coordination through long-term strategic planning.
Outcome:	The proposed framework outperforms traditional reinforcement learning and human-agent collaboration in the Overcooked simulation.

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Paul Piwek, Jacopo Amidei, Svetlana Stoyanchev

Angular Dispersion Accelerates k-Nearest Neighbors Machine Translation (2025.findings-emnlp)

Challenge:	illocutionary acts and propositional relations impact dialogue coherence, whereas propositional acts do not.
Approach:	They propose a method for testing the components of theories of dialogue coherence through utterance substitution and apply it to Inference Anchoring Theory (IAT)
Outcome:	The proposed method is applied to 933 dialogue snippets and 87 annotators.

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Evgeniia Tokarchuk, Sergey Troshin, Vlad Niculae

Benchmarking Contextual and Paralinguistic Reasoning in Speech-LLMs: A Case Study with In-the-Wild Data (2025.findings-emnlp)

Challenge:	k-nearest neighbors machine translation is a well-established strategy for increasing translation performance . high computational cost and memory requirements make it difficult to perform exhaustive searches in large data stores .
Approach:	They propose to augment neural machine translation with external memory at decoding time . they propose to encourage angular dispersion of the neural hidden representations of contexts .
Outcome:	The proposed method is expensive and high memory-intensive . it is based on the performance properties of k-NN lookup data structures.

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Qiongqiong Wang, Hardik Bhupendra Sailor, Tianchi Liu, Wenyu Zhang, Muhammad Huzaifah, Nattadaporn Lertcheva, Shuo Sun, Nancy F. Chen, Jinyang Wu, AiTi Aw

This is not a Disimprovement: Improving Negation Reasoning in Large Language Models via Prompt Engineering (2025.findings-emnlp)

Challenge:	Recent speech-LLMs have shown impressive performance in tasks like transcription and translation, yet they remain limited in understanding the paralinguistic aspects of speech crucial for social and emotional intelligence.
Approach:	They propose a benchmark for evaluating speech-LLMs on contextual paralinguistic reasoning . the benchmark includes curated question answering datasets requiring both linguistic and empathetic understanding .
Outcome:	The proposed benchmark reveals a key gap in existing evaluations and offers insights into building more context-aware and emotionally intelligent LLMs.

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Joshua Jose Dias Barreto, Abhik Jana

Make Every Letter Count: Building Dialect Variation Dictionaries from Monolingual Corpora (2025.findings-emnlp)

Challenge:	Negation reasoning remains a challenge for large language models (LLMs) a negative token attention score (NTAS) is introduced to quantify attention to negation words.
Approach:	They propose two genres of prompts that improve negation accuracy by up to 3.17% . they also propose a negative token attention score to quantify attention to negation words .
Outcome:	The proposed prompts improve negation accuracy and absolute accuracy by 3.17% over baselines.

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Robert Litschko, Verena Blaschke, Diana Burkhardt, Barbara Plank, Diego Frassinelli

SelfAug: Mitigating Catastrophic Forgetting in Retrieval-Augmented Generation via Distribution Self-Alignment (2025.findings-emnlp)

Challenge:	Dialects exhibit a substantial degree of variation due to the lack of a standard orthography . however, the ability of Large Language Models (LLMs) to process dialects remains understudied .
Approach:	They propose a framework for creating dialect variation dictionaries from monolingual data . they use a dataset to examine how well LLMs can judge Bavarian terms as dialect translations .
Outcome:	The proposed framework can judge dialects as dialect translations, inflected variants or unrelated forms of a given German lemma.

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Yuqing Huang, Rongyang Zhang, Qimeng Wang, Chengqiang Lu, Yan Gao, null Yiwu, Yao Hu, Xuyang Zhi, Guiquan Liu, Xin Li, Hao Wang, Enhong Chen

SEKE: Specialised Experts for Keyword Extraction (2025.findings-emnlp)

Challenge:	Existing solutions for supervised fine-tuning often lead to catastrophic forgetting, where models lose their previously acquired knowledge and general capabilities.
Approach:	They propose a self-distribution alignment method that aligns input sequence logits to preserve the model’s semantic distribution, thereby mitigating catastrophic forgetting and improving downstream performance.
Outcome:	The proposed method achieves a superior balance between downstream learning and general capability retention.

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Matej Martinc, Thi Hong Hanh Tran, Senja Pollak, Boshko Koloski

1+1>2: A Synergistic Sparse and Low-Rank Compression Method for Large Language Models (2025.findings-emnlp)

Challenge:	Keyword extraction involves identifying the most descriptive words in a document . supervised keyword extraction is based on the mixture of experts (MoE) technique .
Approach:	They propose a supervised keyword extraction approach based on the mixture of experts technique . they use a learnable routing sub-network to direct information to specialised experts .
Outcome:	The proposed approach is based on the mixture of experts (MoE) technique . experts attend to each token and integrate it with a bidirectional long-term memory network .

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Zeliang Zong, Kai Zhang, Zheyang Li, Wenming Tan, Ye Ren, Yiyan Zhai, Jilin Hu

InfiMM-WebMath-40B: Advancing Multimodal Pre-Training for Enhanced Mathematical Reasoning (2025.findings-emnlp)

Challenge:	Low-rank approximation compresses the model by retaining its essential structure with minimal information loss.
Approach:	They propose a method that leverages the strengths of pruning and low-rank approximation for LLMs.
Outcome:	The proposed methods surpass the existing methods on LLaMA and Qwen2.5 models.

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Xiaotian Han, Yiren Jian, Xuefeng Hu, Haogeng Liu, Yiqi Wang, Qihang Fan, Yuang Ai, Huaibo Huang, Ran He, Zhenheng Yang, Quanzeng You

Zero-Shot Defense Against Toxic Images via Inherent Multimodal Alignment in LVLMs (2025.findings-emnlp)

Challenge:	InfiMM-WebMath-40B is a dataset of interleaved image-text documents . it consists of 24 million web pages, 85 million image URLs, and 40 billion text tokens .
Approach:	InfiMM-WebMath-40B is a high-quality dataset of interleaved image-text documents . it contains 24 million web pages, 85 million image URLs, and 40 billion text tokens .
Outcome:	InfiMM-WebMath-40B is a high-quality dataset of interleaved image-text documents . it consists of 24 million web pages, 85 million image URLs, and 40 billion text tokens .

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Wei Zhao, Zhe Li, Yige Li, Jun Sun

Retrieval Augmented Generation based context discovery for ASR (2025.findings-emnlp)

Challenge:	Existing safeguards relying on pre-filtering or fine-tuning are costly and diminish overall utility.
Approach:	They propose a lightweight method that leverages LVLMs’ inherent multimodal alignment for zero-shot toxic image detection.
Outcome:	The proposed method achieves a 66.9% defense success rate with only 3.2% false positive rate and 7.2% overhead.

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Siskos Dimitrios, Stavros Papadopoulos, Pablo Peso Parada, Jisi Zhang, Karthikeyan Saravanan, Anastasios Drosou

pFedRAG: A Personalized Federated Retrieval-Augmented Generation System with Depth-Adaptive Tiered Embedding Tuning (2025.findings-emnlp)

Challenge:	Existing strategies for automatic context discovery remain a challenge . embedding-based retrieval reduces WER by up to 17% relative to using no-context .
Approach:	They propose an embedding-based retrieval approach for automatic context discovery . they propose two alternatives based on large language models to contextualize their effectiveness .
Outcome:	The proposed method reduces WER by up to 17% relative to using no-context .

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Hangyu He, Xin Yuan, Kai Wu, Ren Ping Liu, Wei Ni

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization (2025.findings-emnlp)

Challenge:	Personalized Federated RAG framework enables efficient collaborative fine-tuning of embedding models . depth-adaptive tieered Embedding (DATE) architecture is tailored for local data and training results of each client.
Approach:	a new Personalized Federated RAG framework is proposed for large language models . the framework enables efficient collaborative fine-tuning of embedding models based on common knowledge .
Outcome:	a novel Personalized Federated RAG framework is proposed for large language models . the framework enables efficient collaborative fine-tuning of embedding models based on common knowledge .

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Zhensheng Jin, Xinze Li, Yifan Ji, Chunyi Peng, Zhenghao Liu, Qi Shi, Yukun Yan, Shuo Wang, Furong Peng, Ge Yu

CURE: Controlled Unlearning for Robust Embeddings — Mitigating Conceptual Shortcuts in Pre-Trained Language Models (2025.findings-emnlp)

Challenge:	Existing methods to train LLMs suffer from overthinking, leading to lengthy reasoning traces . Existing approaches to train large language models suffer from this problem .
Approach:	They propose a method to combine multiple reasoning chains for training LLMs . they use stepwise exploration and long-short switched sampling to evaluate reasoning paths .
Outcome:	The proposed method reduces reasoning lengths by approximately 30-50% . it also maintains or improves reasoning accuracy compared to baselines .

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Aysenur Kocak, Shuo Yang, Bardh Prenkaj, Gjergji Kasneci

MLAlgo-Bench: Can Machines Implement Machine Learning Algorithms? (2025.findings-emnlp)

Challenge:	Pre-trained language models are susceptible to spurious, concept-driven correlations that impair robustness and fairness.
Approach:	They propose a framework that disentangles and suppresses conceptual shortcuts while preserving essential content information.
Outcome:	The proposed framework improves on IMDB and Yelp datasets with minimal computational overhead.

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Yunfei Wang, Yeqin Zhang, Yuyang Wu, Liang Lu, Phi Le Nguyen, Xiaoliang Wang, Nguyen Cam-Tu

Challenge:	Currently, the top-performing models achieve a 48.8% task completion rate on realizing machine learning algorithms .
Approach:	They propose a benchmark to test machine learning's ability to generate ML code for humans . they propose an automatic evaluation framework with metrics such as task pass rate and time overhead .
Outcome:	The proposed benchmark is unique in its focus on interpreting complex human instructions and producing multi-step, high-complexity code.

Fair Text-Attributed Graph Representation Learning (2025.findings-emnlp)

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Ruilin Luo, Tianle Gu, Lin Wang, Yunfeng Zhou, Songtao Jiang, Lei Wang, Yujiu Yang

Human-Inspired Obfuscation for Model Unlearning: Local and Global Strategies with Hyperbolic Representations (2025.findings-emnlp)

Challenge:	Text-Attributed Graphs (TAGs) inherit issues from Graph Neural Networks such as fairness.
Approach:	They propose to evolve LM-as-encoder to LM as-fair-encoding process to explore fairness in TAGRL.
Outcome:	The proposed process can be integrated with fairness-enhancing strategies on the GNNs decoder side.

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Zekun Wang, Jingjie Zeng, Yingxu Li, Liang Yang, Hongfei Lin

Do Influence Functions Work on Large Language Models? (2025.findings-emnlp)

Challenge:	Existing methods for unlearning large language models struggle to balance effective forgetting with maintaining model utility.
Approach:	They propose a human-inspired unlearning framework that simulates forgetting on fuzzy data and represents them in hyperbolic and Euclidean spaces.
Outcome:	The proposed framework is able to forget sensitive content while maintaining the model’s language understanding, fluency, and benchmark performance.

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Zhe Li, Wei Zhao, Yige Li, Jun Sun

TRUEBench: Can LLM Response Meet Real-world Constraints as Productivity Assistant? (2025.findings-emnlp)

Challenge:	Influence functions are important for quantifying the impact of individual training data points on a model’s predictions.
Approach:	They conduct a systematic study to address a key question: do influence functions work on large language models?
Outcome:	The influence functions perform poorly across multiple tasks and are therefore unsuitable for large language models.

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Jiho Park, Jongyoon Song, Minjin Choi, Kyuho Heo, Taehun Huh, Ji Won Kim

CausalMACE: Causality Empowered Multi-Agents in Minecraft Cooperative Tasks (2025.findings-emnlp)

Challenge:	Existing benchmarks fail to evaluate large language models' instruction-following capabilities . current benchmarks lack multilinguality, implicit constraints and multi-turn dialogue .
Approach:	a new benchmark is designed to evaluate large language models' instruction-following capabilities . the benchmark features input prompts across 12 languages and includes inter-instance multilingual instructions .
Outcome:	a new benchmark for large language models (LLMs) is designed to assess their performance in real-world settings.

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Qi Chai, Zhang Zheng, Junlong Ren, Deheng Ye, Zichuan Lin, Hao Wang

Challenge:	Existing work on multi-agent collaborative tasks in Minecraft is limited due to inefficiency and limited fault tolerance.
Approach:	They propose a framework that incorporates causality to manage dependencies among subtasks.
Outcome:	The proposed framework achieves state-of-the-art performance in multi-agent cooperative tasks of Minecraft.

Harry Potter is Still Here! Probing Knowledge Leakage in Targeted Unlearned Large Language Models (2025.findings-emnlp)

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Bang Trinh Tran To, Thai Le

Learning Trajectories of Figurative Language for Pre-Trained Language Models (2025.findings-emnlp)

Challenge:	Existing frameworks for unlearning models can leak idiosyncratic information under adversarial conditions.
Approach:	They propose a framework that probes for undesired knowledge retention in unlearned LLMs . LURK automatically generates adversarial suffixes designed to elicit residual knowledge .
Outcome:	The proposed framework detects undesired knowledge retention in unlearned models . it generates adversarial suffix prompts to elicit residual knowledge about the Harry Potter domain .

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Nicola Arici, Luca Putelli, Ejdis Gjinika, Ivan Serina, Alfonso Gerevini

BcQLM: Efficient Vision-Language Understanding with Distilled Q-Gated Cross-Modal Fusion (2025.findings-emnlp)

Challenge:	Figures of speech and figures of language are used in everyday communication . however, this imaginative use of words requires a solid understanding of semantics and real-world knowledge.
Approach:	They exploit probing tasks to analyse how NLMs recognise figurative language . they find out which layers have a better comprehension of figurativ language based on pre-training data.
Outcome:	The proposed model can recognise hyperboles, metaphors, oxymorons and pleonasms . data show which layers have a better comprehension of figurative language .

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Sike Xiang, Shuang Chen, Amir Atapour-Abarghouei

HapticCap: A Multimodal Dataset and Task for Understanding User Experience of Vibration Haptic Signals (2025.findings-emnlp)

Challenge:	proposed lightweight MLLM framework for end-to-end visual question answering . proposed framework uses BreezeCLIP, a vision-language encoder optimised for efficient multimodal understanding .
Approach:	proposed lightweight MLLM framework is based on BreezeCLIP, a vision-language encoder . it offers a promising path toward deployable ML models under practical hardware constraints.
Outcome:	The proposed model significantly reduces computational cost while achieving performance comparable to standard-size MLLMs.

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Guimin Hu, Daniel Hershcovich, Hasti Seifi

SubDocTrans: Enhancing Document-level Machine Translation with Plug-and-play Multi-granularity Knowledge Augmentation (2025.findings-emnlp)

Challenge:	a dataset of vibration haptic signals is developed to match descriptions to vibrations . a lack of large datasets annotated with textual descriptions is a challenge .
Approach:	They propose a multimodal dataset and task to match user descriptions to vibration haptic signals.
Outcome:	The proposed dataset matches user descriptions to vibration haptic signals . the results show that language models and audio models perform better than existing models .

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Hanghai Hong, Yibo Xie, Jiawei Zheng, Xiaoli Wang

Social Bias Evaluation for Large Language Models Requires Prompt Variations (2025.findings-emnlp)

Challenge:	Document translations generated by large language models suffer from poor consistency, weak coherence, and omission errors.
Approach:	They propose a document-level machine translation framework that extracts knowledge from documents to produce high-quality translations.
Outcome:	The proposed framework improves consistency and coherence, reduces omission errors, and mitigates hallucinations.

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Rem Hida, Masahiro Kaneko, Naoaki Okazaki

Training with Fewer Bits: Unlocking Edge LLMs Training with Stochastic Rounding (2025.findings-emnlp)

Challenge:	Recent studies have tried to evaluate and mitigate social biases accurately using limited prompts.
Approach:	They investigate the sensitivity of Large Language Models when changing prompt variations . they found that LLM rankings fluctuate across prompts for both task performance and social bias .
Outcome:	The results show that LLM rankings fluctuate when changing prompt variations .

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Taowen Liu, Marta Andronic, Deniz Gunduz, George Anthony Constantinides

FactReasoner: A Probabilistic Approach to Long-Form Factuality Assessment for Large Language Models (2025.findings-emnlp)

Challenge:	Quantized training improves computational and memory efficiency but introduces quantization noise.
Approach:	They propose to use stochastic rounding to improve LLM training but introduce quantization noise.
Outcome:	The proposed method can compensate for reduced accuracy during backpropagation.

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Radu Marinescu, Debarun Bhattacharjya, Junkyu Lee, Tigran T. Tchrakian, Javier Carnerero-Cano, Yufang Hou, Elizabeth M. Daly, Alessandra Pascale

Robust Knowledge Editing via Explicit Reasoning Chains for Distractor-Resilient Multi-Hop QA (2025.findings-emnlp)

Challenge:	Large language models often fail to ensure factual accuracy of outputs thus limiting reliability in real-world applications.
Approach:	They propose a neuro-symbolic based factuality assessment framework that employs probabilistic reasoning to evaluate the truthfulness of long-form generated responses.
Outcome:	The proposed framework outperforms state-of-the-art prompt-based methods in factual accuracy and recall.

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Yuchen Wu, Liang Ding, Li Shen, Dacheng Tao

RadialRouter: Structured Representation for Efficient and Robust Large Language Models Routing (2025.findings-emnlp)

Challenge:	Large language models encode vast amounts of knowledge but remain static once trained, making timely integration of emerging facts prohibitively expensive via full retraining.
Approach:	They introduce a reasoning-chain-based editing framework that steers a pretrained LLM through four structured stages to filter distractors in a single pass.
Outcome:	The proposed framework steers a pretrained LLM through four structured stages to filter distractors in a single pass.

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Ruihan Jin, Pengpeng Shao, Zhengqi Wen, Jinyang Wu, Mingkuan Feng, Shuai Zhang, Jianhua Tao

Decoding Uncertainty: The Impact of Decoding Strategies for Uncertainty Estimation in Large Language Models (2025.findings-emnlp)

Challenge:	Current routing methods are limited in exploring the connection between query and LLM characteristics.
Approach:	They propose a framework for LLM routing that uses a transformer-based backbone and a radial structure to articulate the query-LLMs relationship.
Outcome:	The proposed framework outperforms existing routing methods by 9.2% and 5.8% on RouterBench.

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Wataru Hashimoto, Hidetaka Kamigaito, Taro Watanabe

Elucidating Mechanisms of Demographic Bias in LLMs for Healthcare (2025.findings-emnlp)

Challenge:	Decoding strategies affect the probability distribution underlying the output of a language model and can therefore affect both generation quality and uncertainty.
Approach:	They investigate the impact of decoding strategies on uncertainty estimation in large language models .
Outcome:	The proposed methods improve the uncertainty estimation of large language models by reducing repetition.

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Hiba Ahsan, Arnab Sen Sharma, Silvio Amir, David Bau, Byron C Wallace

Can You Trick the Grader? Adversarial Persuasion of LLM Judges (2025.findings-emnlp)

Challenge:	a recent study has shown that LLMs encode social biases and manifest in clinical tasks.
Approach:	They use mechanistic interpretability to uncover biases within LLMs . they find gender information is highly localized in MLP layers .
Outcome:	The proposed method can reveal biases and representations within LLMs in healthcare.

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Yerin Hwang, Dongryeol Lee, Taegwan Kang, Yongil Kim, Kyomin Jung

Navigating the Unknown: Intent Classification and Out-of-Distribution Detection Using Large Language Models (2025.findings-emnlp)

Challenge:	Large language models (LLMs) are increasingly used as automated evaluators in practical settings .
Approach:	a study by the university of california reveals that persuasive language can bias large language models when scoring mathematical reasoning tasks.
Outcome:	The proposed model can bias judges when scoring mathematical reasoning tasks . Consistency causes the most severe distortion, with Consistencies leading to 8% distortion .

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Yusuf Sali, Sıtkı Can Toraman

Trust Me, I’m Wrong: LLMs Hallucinate with Certainty Despite Knowing the Answer (2025.findings-emnlp)

Challenge:	Out-of-Distribution (OOD) detection requires great generalization capability .
Approach:	They propose a method that is cost-efficient, high-performing, highly robust and versatile enough to be used with smaller LLMs without sacrificing performance.
Outcome:	The proposed method is cost-efficient, high-performing, robust, and versatile enough to be used with smaller LLMs without sacrificing performance.

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Adi Simhi, Itay Itzhak, Fazl Barez, Gabriel Stanovsky, Yonatan Belinkov

QUARTZ: QA-based Unsupervised Abstractive Refinement for Task-oriented Dialogue Summarization (2025.findings-emnlp)

Challenge:	Prior work on large language model (LLM) hallucinations associated with model uncertainty or inaccurate knowledge.
Approach:	They define and investigate a type of hallucination where a model can answer a question correctly but a perturbation causes it to produce a hallucinous response with high certainty.
Outcome:	The proposed mitigations outperform existing methods on CHOKE hallucinations . the findings highlight the need to understand their origins and improve mitigation strategies .

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Mohamed Imed Eddine Ghebriout, Gaël Guibon, Ivan Lerner, Emmanuel Vincent

MDSEval: A Meta-Evaluation Benchmark for Multimodal Dialogue Summarization (2025.findings-emnlp)

Challenge:	a framework for task-oriented utility-based dialogue summarization is proposed . QUARTZ is a tool for task summarizing dialogues, but its outputs lack task-specific focus.
Approach:	They propose a framework for task-oriented utility-based dialogue summarization . QUARTZ generates summaries and question-answer pairs from a dialogue in a zero-shot manner .
Outcome:	The proposed framework achieves competitive results in zero-shot settings, rivaling fully-supervised State-of-the-Art methods.

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Yinhong Liu, Jianfeng He, Hang Su, Ruixue Lian, Yi Nian, Jake W. Vincent, Srikanth Vishnubhotla, Robinson Piramuthu, Saab Mansour

PMPO: Probabilistic Metric Prompt Optimization for Small and Large Language Models (2025.findings-emnlp)

Challenge:	Multimodal Dialogue Summarization (MDS) is a critical task with wide-ranging applications.
Approach:	They propose a meta-evaluation benchmark for multimodal dialogue summarization based on image-sharing dialogues, corresponding summaries and human judgments .
Outcome:	The proposed framework is the first to identify and formalize key evaluation dimensions specific to MDS.

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ChenZhuo Zhao, Ziqian Liu, Xinda Wang, Junting Lu, Chaoyi Ruan

Evaluating the Creativity of LLMs in Persian Literary Text Generation (2025.findings-emnlp)

Challenge:	Existing methods evaluate candidate prompts by sampling full outputs, often coupled with self critique or human annotated preferences, which limits scalability, especially for smaller models or models that are not instruction tuned.
Approach:	They propose a framework that uses token level cross entropy as a direct, lightweight evaluation signal to evaluate candidate prompts.
Outcome:	The proposed framework outperforms prior prompt optimizers across model sizes and datasets.

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Armin Tourajmehr, Mohammad Reza Modarres, Yadollah Yaghoobzadeh

SCDTour: Embedding Axis Ordering and Merging for Interpretable Semantic Change Detection (2025.findings-emnlp)

Challenge:	Prior research has focused primarily on English, with limited exploration of non-English literary traditions and without standardized methods for assessing creativity.
Approach:	They build a dataset of user-generated Persian literary spanning 20 diverse topics and assess model outputs along four creativity dimensions .
Outcome:	The proposed models generate Persian literary text enriched with culturally relevant expressions.

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Taichi Aida, Danushka Bollegala

Resolving UnderEdit & OverEdit with Iterative & Neighbor-Assisted Model Editing (2025.findings-emnlp)

Challenge:	Existing methods to improve interpretability of SCD often lead to performance degradation . agglomerating axes produces a more refined set of word senses, which improves performance .
Approach:	They propose a method that orders and merges interpretable axes to improve SCD performance.
Outcome:	The proposed method preserves performance while maintaining high interpretability . it produces a more refined set of word senses, which improves performance .

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Bhiman Kumar Baghel, Emma Jordan, Zheyuan Ryan Shi, Xiang Lorraine Li

LLM-empowered Dynamic Prompt Routing for Vision-Language Models Tuning under Long-Tailed Distributions (2025.findings-emnlp)

Challenge:	Existing methods to update model parameters are limited due to their low efficiency and cost.
Approach:	They propose two methods to improve model editing performance by incorporating neighboring knowledge during editing.
Outcome:	The proposed methods reduce UnderEdit by 38 percentage points and OverEdit by up to 6 .

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Yongju Jia, Jiarui Ma, Xiangxian Li, Baiqiao Zhang, Xianhui Cao, Juan Liu, Yulong Bian

HGAdapter: Hypergraph-based Adapters in Language Models for Code Summarization and Clone Detection (2025.findings-emnlp)

Challenge:	Pre-trained vision-language models (VLMs) often suffer from bias in class-imbalanced scenes.
Approach:	They propose a multi-dimensional dynamic prompt routing framework that integrates a knowledge base for classes spanning multiple visual-semantic dimensions.
Outcome:	The proposed framework achieves comparable results with current SOTA methods on long-tailed benchmarks, including CIFAR-LT, ImageNet-LT and Places-LT.

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Guang Yang, Yujie Zhu

Evaluating distillation methods for data-efficient syntax learning (2025.findings-emnlp)

Challenge:	Pre-trained language models (PLMs) are increasingly being applied to code-related tasks.
Approach:	They propose a hypergraph-based adapter to capture high-order data correlations in code tokens . they improve hypergraph neural networks and combine it with adapter tuning to propose adapter .
Outcome:	The proposed adapter can encode high-order data correlations and be inserted into PLMs to enhance performance.

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Takateru Yamakoshi, Thomas L. Griffiths, R. Thomas McCoy, Robert D. Hawkins

“Going to a trap house” conveys more fear than “Going to a mall”: Benchmarking Emotion Context Sensitivity for LLMs (2025.findings-emnlp)

Challenge:	knowledge distillation (KD) targeting attention should selectively accelerate syntax acquisition, a study finds . logit-based KD dramatically improves data-efficiency, attention-based one provides minimal benefit even for syntactic tasks.
Approach:	a study predicts that knowledge distillation targeting attention should selectively accelerate syntax acquisition . a systolic analysis of student models compared to logit-based knowledge distillations .
Outcome:	a new study shows that knowledge distillation (KD) targeting attention accelerates syntax acquisition . the hypothesis is tested on syntactic benchmarks and perplexity.

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Eojin Jeon, Mingyu Lee, Sangyun Kim, Junho Kim, Wanzee Cho, Tae-Eui Kam, SangKeun Lee

[MASK]ED - Language Modeling for Explainable Classification and Disentangling of Socially Unacceptable Discourse. (2025.findings-emnlp)

Challenge:	a new benchmark evaluates whether large language models can understand emotion context sensitivity of humans.
Approach:	a new benchmark evaluates whether large language models can understand emotion context sensitivity of humans.
Outcome:	a new benchmark evaluates whether large language models can understand emotion context sensitivity of humans.

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Dimitra Niaouri, Mohamed Rayane Ghilene, Michele Linardi, Julien Longhi

A Survey of Cognitive Distortion Detection and Classification in NLP (2025.findings-emnlp)

Challenge:	Pre-trained PMLMs often degrade in multi-label SUD classification due to overlapping linguistic cues.
Approach:	They propose an artifact-guided pre-training strategy that injects statistically salient linguistic features into masked language models to enhance generalization across discourse types.
Outcome:	The proposed approach improves performance in 10 datasets extensively used in SUD classification benchmarks.

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Archie Sage, Jeroen Keppens, Helen Yannakoudakis

Curse of Knowledge: Your Guidance and Provided Knowledge are biasing LLM Judges in Complex Evaluation (2025.findings-emnlp)

Challenge:	despite momentum in natural language processing, the field remains fragmented . inconsistencies in CD taxonomies, task formulations and evaluation practices limit comparability .
Approach:	This review provides a comprehensive review of 38 studies spanning two decades . they map how CDs have been implemented in computational research and evaluate the methods applied.
Outcome:	The paper presents the first comprehensive review of 38 studies spanning two decades . it summarises common task setups and highlights persistent challenges to support more coherent research.

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Weiyuan Li, Xintao Wang, Siyu Yuan, Rui Xu, Jiangjie Chen, Qingqing Dong, Yanghua Xiao, Deqing Yang

Challenge:	a recent study has focused on simple settings, but their reliability in complex tasks remains understudied.
Approach:	They propose to use large language models as judges to evaluate reliability in complex tasks . they use a challenge benchmark to expose and quantify Auxiliary Information Induced Biases .
Outcome:	The proposed benchmark exposes and quantifies Auxiliary Information Induced Biases across 12 basic and 3 advanced scenarios.

Self-Training Large Language Models with Confident Reasoning (2025.findings-emnlp)

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Hyosoon Jang, Yunhui Jang, Sungjae Lee, Jungseul Ok, Sungsoo Ahn

Error Typing for Smarter Rewards: Improving Process Reward Models with Error-Aware Hierarchical Supervision (2025.findings-emnlp)

Challenge:	Large language models generate reasoning paths before final answers, but learning such a path requires costly human supervision.
Approach:	They propose a method that fine-tunes LLMs to prefer reasoning paths with high confidence . they propose 'cORE-PO' that fine tunes Lms to choose high-quality reasoning paths .
Outcome:	The proposed method improves the accuracy of outputs on four in-distribution and two out-of-difference benchmarks.

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Tej Deep Pala, Panshul Sharma, Amir Zadeh, Chuan Li, Soujanya Poria

Enhancing LLM-Based Persuasion Simulations with Cultural and Speaker-Specific Information (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) are prone to hallucination, especially during multihop tasks.
Approach:	They propose a hierarchical, erroraware discriminative PRM that classifies math errors at each step and combines finegrained signals to estimate step correctness.
Outcome:	The proposed model outperforms the prior best in a new stateof-theart PRMScore of 67.7 on a 400Ksample dataset .

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Weicheng Ma, Hefan Zhang, Shiyu Ji, Farnoosh Hashemi, Qichao Wang, Ivory Yang, Joice Chen, Juanwen Pan, Michael Macy, Saeed Hassanpour, Soroush Vosoughi

An LLM-based Temporal-spatial Data Generation and Fusion Approach for Early Detection of Late Onset Alzheimer’s Disease (LOAD) Stagings Especially in Chinese and English-speaking Populations (2025.findings-emnlp)

Challenge:	Existing approaches to persuasive dialogue generation suffer from stance oscillation and low informativeness.
Approach:	They propose reinforced instructional prompting, a method that ensures speaker characteristics consistently guide all stages of dialogue generation.
Outcome:	The proposed method ensures speaker characteristics guide all stages of dialogue generation and aligns language use with speakers’ native languages to better capture cultural nuances.

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Yang Han, Jacqueline C.k. Lam, Victor O.k. Li, Lawrence Y. L. Cheung

Side Effects of Erasing Concepts from Diffusion Models (2025.findings-emnlp)

Challenge:	Existing approaches struggle with temporal-spatial challenges in capturing subtle linguistic shifts across different disease stages.
Approach:	They propose a large language model-driven T-S fusion framework that integrates multilingual LLMs, contrastive learning and interpretable marker discovery to revolutionize late onset AD detection.
Outcome:	The proposed framework achieves state-of-the-art performance in late onset AD detection while enabling cross-linguistic diagnostics.

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Shaswati Saha, Sourajit Saha, Manas Gaur, Tejas Gokhale

SaCa: A Highly Compatible Reinforcing Framework for Knowledge Graph Embedding via Structural Pattern Contrast (2025.findings-emnlp)

Challenge:	Concept erasure techniques (CETs) are used to erade text-to-image (T2I) generative models infringing on privacy, copyright and safety.
Approach:	They propose a side effect evaluation benchmark to measure the robustness of CETs.
Outcome:	The proposed techniques can be easily circumvented by hierarchy, prompts, and compositional variants of the target.

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Jiashi Lin, Changhong Jiang, Yixiao Wang, Xinyi Zhu, Zhongtian Hu, Wei Zhang

Real, Fake, or Manipulated? Detecting Machine-Influenced Text (2025.findings-emnlp)

Challenge:	Existing knowledge Graph Embedding approaches lack structural semantics of knowledge graphs . structure-aware calibration (SaCa) is a framework designed to calibrate KGEs based on global structural patterns.
Approach:	a new framework is designed to calibrate knowledge graphs using global structural patterns.
Outcome:	a new framework can calibrate KGE models using global structural patterns . the framework consistently boosts performance across ten models on link prediction and entity classification tasks .

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Yitong Wang, Zhongping Zhang, Margherita Piana, Zheng Zhou, Peter Gerstoft, Bryan A. Plummer

Character is Destiny: Can Persona-assigned Language Models Make Personal Choices? (2025.findings-emnlp)

Challenge:	Prior work on machine generated text detection focused on identifying whether document was human or machine written, ignoring these fine-grained uses.
Approach:	They propose a machine-influenced text detector that learns to separate text samples from four primary types . the detector uses a subcategory guidance module to help separate the fine-grained categories .
Outcome:	The proposed detector outperforms the state-of-the-art in five LLMs and six domains.

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Rui Xu, Xintao Wang, Jiangjie Chen, Siyu Yuan, Xinfeng Yuan, Jiaqing Liang, Zulong Chen, null Xiaoqingdong, Yanghua Xiao

Neutral Is Not Unbiased: Evaluating Implicit and Intersectional Identity Bias in LLMs Through Structured Narrative Scenarios (2025.findings-emnlp)

Challenge:	Recent research has demonstrated the potential of using LLMs to develop role-playing language agents (RPLAs) however, imitative decision-making necessitates a more nuanced understanding of personas.
Approach:	They propose a method that uses persona-based memory retrieval to improve RPLAs.
Outcome:	The proposed method significantly advances RPLAs on this task.

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Saba Ghanbari Haez, Mauro Dragoni

BTW: A Non-Parametric Variance Stabilization Framework for Multimodal Model Integration (2025.findings-emnlp)

Challenge:	Large Language Models often reproduce societal biases, yet most evaluations overlook how such biase evolve across nuanced contexts or intersecting identities.
Approach:	They propose a scenario-based evaluation framework built on 100 narrative tasks . they use critical discourse analysis and quantitative linguistic metrics to analyze LLMs .
Outcome:	The proposed evaluation framework provides ethically coherent and socially plausible settings for probing model behavior.

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Jun Hou, Le Wang, Xuan Wang

Can LLMs Be Efficient Predictors of Conversational Derailment? (2025.findings-emnlp)

Challenge:	Existing methods for multimodal learning are difficult to scale beyond two modalities and lack resolution for instance-level control.
Approach:	They propose a bi-level weighting framework that combines instance-level Kullback-Leibler divergence and modality-level mutual information to dynamically adjust modality importance during training.
Outcome:	The proposed method significantly improves regression performance and multiclass classification accuracy.

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Kaustubh Olpadkar, Vikram Sunil Bajaj, Leslie Barrett

Q-PRM: Adaptive Query Rewriting for Retrieval-Augmented Generation via Step-level Process Supervision (2025.findings-emnlp)

Challenge:	Conversational derailment is a common issue on online platforms due to toxic or inappropriate remarks.
Approach:	They prompt pre-trained large language models to predict conversational derailment without fine-tuning . they compare chain-of-thought reasoning and few-shot exemplars to predict derailments .
Outcome:	The proposed model predicts conversational derailment without task-specific fine-tuning without fine-cuning.

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Xiaopeng Ye, Chen Xu, Chaoliang Zhang, Zhaocheng Du, Jun Xu, Gang Wang, Zhenhua Dong

Paraphrasing

Challenge:	Existing approaches to rewriting queries often lack supervision signals for intermediate steps . existing approaches rely on outcome-supervised training or heuristic rules to guide the rewrite process .
Approach:	They propose a query rewriting framework that generates process-level supervision signals for intermediate steps.
Outcome:	a new query rewriting framework outperforms existing approaches on open-domain QA benchmarks.

Factuality Beyond Coherence: Evaluating LLM Watermarking Methods for Medical Texts (2025.findings-emnlp)

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Rochana Prih Hastuti, Rian Adam Rajagede, Mansour Al Ghanim, Mengxin Zheng, Qian Lou

Challenge:	Existing benchmarks focus on detection-quality tradeoffs and overlook factual risks.
Approach:	They propose a method that assesses factual accuracy and coherence . they use a factor-weighted score to prioritize factual accurate beyond coherency .
Outcome:	The proposed method assesses factual accuracy and coherence in medical text . it shows current watermarking methods substantially compromise medical factuality .

Guess What I am Thinking: A Benchmark for Inner Thought Reasoning of Role-Playing Language Agents (2025.findings-emnlp)

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Rui Xu, Mingyu Wang, Xintao Wang, Dakuan Lu, Xiaoyu Tan, Wei Chu, Xu Yinghui

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs (2025.findings-emnlp)

Challenge:	Recent advances in Large Language Models (LLMs)-based Role-Playing Language Agents (RPLAs) have attracted broad attention in various applications.
Approach:	They propose a benchmark for evaluating character thought generation using literature . they propose 'MIRROR' which generates character thoughts by retrieving memories, predicting character reactions, and synthesizing motivations.
Outcome:	The proposed benchmark outperforms existing methods in evaluating character thought generation.

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Yixiao Zhou, Ziyu Zhao, Dongzhou Cheng, Zhiliang Wu, Jie Gui, Yi Yang, Fei Wu, Yu Cheng, Hehe Fan

BiasFilter: An Inference-Time Debiasing Framework for Large Language Models (2025.findings-emnlp)

Challenge:	Sparse Mixture-of-Experts (SMoE) architectures require loading all expert parameters . previous work focused on expert pruning and merging but focused on neuron-level structure .
Approach:	They propose a task-agnostic framework for expert pruning and reconstruction . it prunes redundant experts using router statistics, then decomposes them into neuron-level expert segments .
Outcome:	The proposed framework reduces the number of experts and memory usage, making it easier to deploy.

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Xiaoqing Cheng, Ruizhe Chen, Hongying Zan, Yuxiang Jia, Min Peng

X-LeBench: A Benchmark for Extremely Long Egocentric Video Understanding (2025.findings-emnlp)

Challenge:	Existing methods for debiasing large language models incur high human and computational costs and are limited in their effectiveness.
Approach:	They propose a model-agnostic, inference-time debiasing framework that enforces fairness by filtering generation outputs in real time.
Outcome:	The proposed framework mitigates social bias across a range of LLMs while preserving overall generation quality.

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Wenqi Zhou, Kai Cao, Hao Zheng, Yunze Liu, Xinyi Zheng, Miao Liu, Per Ola Kristensson, Walterio W. Mayol-Cuevas, Fan Zhang, Weizhe Lin, Junxiao Shen

A Survey on Multi-modal Intent Recognition: Recent Advances and New Frontiers (2025.findings-emnlp)

Challenge:	Existing benchmark datasets focus on short to moderately long videos, leaving a substantial gap in evaluating extensive, ultra-long egocentric video recordings.
Approach:	X-LeBench is a benchmark dataset designed to evaluate long egocentric video recordings . it uses a life-logging pipeline to produce realistic, coherent daily plans .
Outcome:	X-LeBench is a new benchmark dataset designed to evaluate long-form egocentric video understanding . the approach produces realistic, coherent daily plans aligned with real-world video data .

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Zhihong Zhu, Fan Zhang, Yunyan Zhang, Jinghan Sun, Zhiqi Huang, Qingqing Long, Bowen Xing, Xian Wu

Will Annotators Disagree? Identifying Subjectivity in Value-Laden Arguments (2025.findings-emnlp)

Challenge:	Multi-modal intent recognition (MIR) requires integrating non-verbal cues from real-world contexts to enhance human intention understanding.
Approach:	They present a comprehensive review of multi-modal intent recognition . they provide a survey of the field covering textual, visual, and acoustic signals .
Outcome:	The present survey summarises the current state of multi-modal intent recognition . it includes a comprehensive taxonomy and advanced methods .

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Amir Homayounirad, Enrico Liscio, Tong Wang, Catholijn M Jonker, Luciano Cavalcante Siebert

Challenge:	aggregating multiple annotations into a single ground truth label may hide valuable insights into disagreement .
Approach:	They propose methods for identifying subjectivity in recognizing human values that motivate arguments.
Outcome:	The proposed methods can help identify arguments that individuals may interpret differently.

LLMs Can Compensate for Deficiencies in Visual Representations (2025.findings-emnlp)

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Sho Takishita, Jay Gala, Abdelrahman Mohamed, Kentaro Inui, Yova Kementchedjhieva

Adapting Large Language Models for Character-based Augmentative and Alternative Communication (2025.findings-emnlp)

Challenge:	a strong language backbone in vision-language models compensates for weak visual features by contextualizing or enriching them.
Approach:	They investigate whether strong language backbone compensates for weak visual features . they use CLIP-based vision encoders to perform controlled self-attention ablations .
Outcome:	The proposed model compensates for weak visual features by contextualizing or enriching them.

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Dylan Gaines, Keith Vertanen

Token-Level Metrics for Detecting Incorrect Gold Annotations in Named Entity Recognition (2025.findings-emnlp)

Challenge:	Most character language models predict subword tokens of variable length .
Approach:	They propose to use large pretrained character language models to make accurate character predictions.
Outcome:	The proposed method produces more accurate character predictions than classification models and n-gram models.

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Elena Merdjanovska, Alan Akbik

Exploring Paraphrasing Strategies for CEFR A1-Level Constraints in LLMs (2025.findings-emnlp)

Challenge:	Annotated datasets for supervised learning often contain incorrect labels, i.e. label noise.
Approach:	They compare popular sample metrics for detecting incorrect annotations in named entity recognition (NER) they find that training dynamics metrics work the best overall, and they detect errors that the model has not yet memorized .
Outcome:	The proposed measures reduce label noise across noise types by detecting errors in trained models.

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Eugenio Marzona, Maria Goikhman, Alessio Palmero Aprosio, Massimo Zancanaro

Efficient Layer-wise LLM Fine-tuning for Revision Intention Prediction (2025.findings-emnlp)

Challenge:	a new study compares prompt engineering approaches to rephrase general-domain texts . it compares 4 approaches to meet CEFR A1-level constraints in english and italian .
Approach:	They compare prompt engineering approaches to rephrase general-domain texts to meet CEFR A1-level constraints in English and Italian.
Outcome:	The proposed approaches meet CEFR A1-level constraints in English and Italian.

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Zhexiong Liu, Diane Litman

ConText-LE: Cross-Distribution Generalization for Longitudinal Experiential Data via Narrative-Based LLM Representations (2025.findings-emnlp)

Challenge:	Large Language Models have shown extraordinary success across text generation tasks . however, their potential for simple yet essential text classification remains underexplored .
Approach:	a plug-and-play layer-wise parameter-efficient fine-tuning framework is proposed . it fine- tunes a subset of important LLM layers while freezing redundant ones .
Outcome:	a plug-and-play framework fine-tunes a subset of important LLM layers while freezing redundant layers.

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Ahatsham Hayat, Bilal Khan, Mohammad Rashedul Hasan

Chain of Strategy Optimization Makes Large Language Models Better Emotional Supporter (2025.findings-emnlp)

Challenge:	Longitudinal experiential data offers rich insights into dynamic human states, yet building models that generalize across diverse contexts remains challenging.
Approach:	They propose a framework that investigates text representation strategies and output formulations to maximize large language model cross-distribution generalization for behavioral forecasting.
Outcome:	The proposed framework achieves 12.28% improvement in out-of-distribution accuracy and 11.99% improvement in F1 scores over binary classification methods.

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Weixiang Zhao, Xingyu Sui, Xinyang Han, Yang Deng, Yulin Hu, Jiahe Guo, Libo Qin, Qianyun Du, Shijin Wang, Yanyan Zhao, Bing Qin, Ting Liu

Unlocking Legal Knowledge: A Multilingual Dataset for Judicial Summarization in Switzerland (2025.findings-emnlp)

Challenge:	Existing supervised fine-tuning (SFT) fails to address these issues, as it trains models on single gold-standard responses without modeling nuanced strategy trade-offs.
Approach:	They propose a two-stage framework that optimizes strategy selection preferences at each dialogue turn.
Outcome:	The proposed framework improves strategy selection preferences at each dialogue turn.

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Luca Rolshoven, Vishvaksenan Rasiah, Srinanda Brügger Bose, Sarah Hostettler, Lara Burkhalter, Matthias Stürmer, Joel Niklaus

Context Minimization for Resource-Constrained Text Classification: Optimizing Performance-Efficiency Trade-offs through Linguistic Features (2025.findings-emnlp)

Challenge:	a dataset of 20K rulings from the Swiss Federal Supreme Court is lacking in legal headnotes due to the high cost of manual annotation.
Approach:	They propose a dataset that contains 20K rulings from the Swiss Federal Supreme Court . they fine-tune open models and compare them to larger general-purpose and reasoning-tunned LLMs .
Outcome:	The proposed dataset contains 20K rulings from the Swiss Federal Supreme Court with headnotes in German, French, and Italian.

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Nahid Hossain, Md Faisal Kabir

FLAIRR-TS - Forecasting LLM-Agents with Iterative Refinement and Retrieval for Time Series (2025.findings-emnlp)

Challenge:	Pretrained language models have transformed text classification, but their computational demands often render them impractical for resource-constrained settings.
Approach:	They propose a linguistically-grounded framework for context minimization that leverages theme-rheme structure to preserve critical classification signals while reducing input complexity.
Outcome:	The proposed framework preserves critical classification signals while reducing input complexity.

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Gunjan Jalori, Preetika Verma, Sercan O Arik

ULTRABENCH: Benchmarking LLMs under Extreme Fine-grained Text Generation (2025.findings-emnlp)

Challenge:	Effective time series forecasting with large language models often relies on extensive pre-processing and fine-tuning.
Approach:	a new time series prompt optimization framework is developed to optimize time series forecasts.
Outcome:	The proposed framework improves forecasting over static prompting and retrieval-augmented baselines.

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Longfei Yun, Letian Peng, Jingbo Shang

The Price of Format: Diversity Collapse in LLMs (2025.findings-emnlp)

Challenge:	Existing benchmarks evaluate models on only a few attributes, typically fewer than five . a new benchmark evaluates large language models under dense, multi-attribute constraints .
Approach:	They propose a benchmark that evaluates large language models under dense, multi-attribute constraints.
Outcome:	The proposed benchmark evaluates large language models under dense, multi-attribute constraints.

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Longfei Yun, Chenyang An, Zilong Wang, Letian Peng, Jingbo Shang

Zipf’s and Heaps’ Laws for Tokens and LLM-generated Texts (2025.findings-emnlp)

Challenge:	Instruction-tuned large language models employ structured templates to enforce format consistency during inference.
Approach:	They fine-tune instruction-tuning large language models with structured templates and evaluate their results across three axes: downstream task performance, alignment behavior, and output diversity.
Outcome:	The proposed model generates semantically similar outputs even under high temperature sampling and structural tokens in templates significantly constrain the model’s output space.

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Nikolay Mikhaylovskiy

LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet? (2025.findings-emnlp)

Challenge:	We empirically show that Heaps’ and Zipf’s laws only hold for LLM-generated texts in a narrow model-dependent temperature range.
Approach:	They propose to apply Zipf's law to large language models to study the frequency distribution of words in human-written texts .
Outcome:	The proposed models only hold for LLM-generated texts in a narrow model-dependent temperature range.

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Rushil Gupta, Jason Hartford, Bang Liu

Challenge:	Large language models have been proposed as general-purpose agents for experimental design . eval: LLMs show no sensitivity to experimental feedback.
Approach:	They propose a method that combines LLM prior knowledge with nearest-neighbor sampling to guide the design of experiments.
Outcome:	The proposed method outperforms classical methods in the design of experiments.

A Comprehensive Taxonomy of Negation for NLP and Neural Retrievers (2025.findings-emnlp)

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Roxana Petcu, Samarth Bhargav, Maarten de Rijke, Evangelos Kanoulas

Identifying Noise in Human-Created Datasets using Training Dynamics from Generative Models (2025.findings-emnlp)

Challenge:	a new taxonomy of negation is proposed to improve neural information retrieval models . negation types are covered in existing datasets, allowing for faster convergence .
Approach:	They propose a taxonomy of negation that derives from philosophical, linguistic, and logical definitions . they also propose analyzing the performance of retrieval models on existing datasets using a logic-based classification mechanism.
Outcome:	The proposed taxonomy produces a balanced data distribution over negation types . it also provides a better training setup that leads to faster convergence on the NevIR dataset .

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Maeda Hanafi, Ishan Jindal, Yannis Katsis, Lucian Popa, Huaiyu Zhu

Can Multiple Responses from an LLM Reveal the Sources of Its Uncertainty? (2025.findings-emnlp)

Challenge:	Existing noise detection techniques for autoencoder models do not generalize to ArLMs due to differences in learning dynamics.
Approach:	They propose a method that leverages training dynamics to rank datapoints from easy-to-learn to hard-tolear . TDRanker achieves at least 2x faster denoising than previous techniques .
Outcome:	The proposed method demonstrates robustness across multiple model architectures and noise levels.

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Yang Nan, Pengfei He, Ravi Tandon, Han Xu

Challenge:	Large language models can produce unreliable or misleading outputs, posing challenges for real-world applications.
Approach:	They employ an auxiliary LLM to analyze the patterns of disagreement among LLMs . they validate their framework on AmbigQA, OpenBookQA, and MMLU-Pro .
Outcome:	The proposed model can be used to diagnose uncertainty sources in a model with an auxiliary model.

AfroXLMR-Social: Adapting Pre-trained Language Models for African Languages Social Media Text (2025.findings-emnlp)

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Tadesse Destaw Belay, Israel Abebe Azime, Ibrahim Said Ahmad, David Ifeoluwa Adelani, Idris Abdulmumin, Abinew Ali Ayele, Shamsuddeen Hassan Muhammad, Seid Muhie Yimam

Teaching Language Models To Gather Information Proactively (2025.findings-emnlp)

Challenge:	Domain adaptive pre-training and task-adaptive pre- training (TAPT) are popular methods to reduce this bias for low-resource languages, but they have not been explored for African multilingual encoders.
Approach:	They propose a large-scale social media and news domain corpus for continual pre-training on African languages.
Outcome:	The proposed methods improve performance on three subjective tasks, including sentiment analysis, multi-label emotion, and hate speech classification, while TAPT improves performance on other related tasks.

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Tenghao Huang, Sihao Chen, Muhao Chen, Jonathan May, Longqi Yang, Mengting Wan, Pei Zhou

Linguistic Alignment Predicts Learning in Small Group Tutoring Sessions (2025.findings-emnlp)

Challenge:	Large language models are often defaulted to passive responses or narrow clarifications when faced with incomplete or under-specified prompts.
Approach:	They propose a new task paradigm where LLMs must identify gaps in context and strategically elicit implicit user knowledge through targeted questions.
Outcome:	The proposed framework outperforms o3-mini on evaluation metrics and human annotators favor clarification questions and final outlines.

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Dorothea French, Robert Moulder, Kelechi Ezema, Katharina von der Wense, Sidney K. DMello

Linguistics & Cognitive NLP

Challenge:	Cognitive science offers rich theories of learning and communication, yet these are often difficult to operationalize at scale.
Approach:	They investigate linguistic alignment in a longitudinal dataset of real-world tutoring interactions and associated student test scores.
Outcome:	The proposed method can be applied to real-world tutoring interactions and student test scores.

EfficientXLang: Towards Improving Token Efficiency Through Cross-Lingual Reasoning (2025.findings-emnlp)

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Sanchit Ahuja, Praneetha Vaddamanu, Barun Patra

Not Lost After All: How Cross-Encoder Attribution Challenges Position Bias Assumptions in LLM Summarization (2025.findings-emnlp)

Challenge:	Despite recent advances in Reasoning Language Models, most research focuses solely on English, even though many models are pretrained on multilingual data.
Approach:	They evaluate three open-source RLMs: DeepSeek R1, Qwen 2.5, and Qwend 3 across four math datasets and seven typologically diverse languages.
Outcome:	The proposed model reduces token usage and preserves accuracy even after translation into English.

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Elahe Rahimi, Hassan Sajjad, Domenic Rosati, Abeer Badawi, Elham Dolatabadi, Frank Rudzicz

Summarization

Challenge:	Position bias is a key limitation in automatic summarization.
Approach:	They propose a cross-encoder-based alignment method that processes summary-source sentence pairs .
Outcome:	The proposed method allows better identification of semantic correspondences even when summaries substantially rewrite the source.

FuzzAug: Data Augmentation by Coverage-guided Fuzzing for Neural Test Generation (2025.findings-emnlp)

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Yifeng He, Jicheng Wang, Yuyang Rong, Hao Chen

DrAgent: Empowering Large Language Models as Medical Agents for Multi-hop Medical Reasoning (2025.findings-emnlp)

Challenge:	Using large language models to generate meaningful tests is expensive and time-consuming .
Approach:	They propose a data augmentation technique that incorporates valid testing semantics and diverse coverage-guided inputs into large language models.
Outcome:	The proposed technique improves performance over the baselines by incorporating valid testing semantics and providing diverse coverage-guided inputs.

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Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xin Liu, Zhengyang Wang, Xianfeng Tang, Shiyang Li, Xiang He, Ruijie Wang, Bing Yin, Xiao Gu, Lei Clifton, David A. Clifton

XRAG: Cross-lingual Retrieval-Augmented Generation (2025.findings-emnlp)

Challenge:	commercial LLMs can be difficult to use in real-world clinical decision-making . a lightweight LLM can be used to collaborate with diverse clinical tools .
Approach:	They propose a lightweight LLM that can be used to build medical LLMs as agents . they use recursive curriculum learning to optimize the LLM in an easy-to-hard progression .
Outcome:	The proposed approach outperforms human experts in medical examinations on diverse datasets.

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Wei Liu, Sony Trenous, Leonardo F. R. Ribeiro, Bill Byrne, Felix Hieber

Can VLMs Recall Factual Associations From Visual References? (2025.findings-emnlp)

Challenge:	XRAG evaluates the generation abilities of LLMs in cross-lingual RAG settings where the user language does not match retrieval results.
Approach:	They propose a benchmark to evaluate the generation abilities of LLMs in cross-lingual RAG settings where the user language does not match retrieval results.
Outcome:	XRAG is a benchmark designed to evaluate the generation abilities of LLMs in cross-lingual RAG settings where the user language does not match retrieval results.

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Dhananjay Ashok, Ashutosh Chaubey, Hirona Jacqueline Arai, Jonathan May, Jesse Thomason

MFTCXplain: A Multilingual Benchmark Dataset for Evaluating the Moral Reasoning of LLMs through Multi-hop Hate Speech Explanation (2025.findings-emnlp)

Challenge:	a systematic deficiency in the multimodal grounding of Vision Language Models is identified . VLMs can recall factual associations when provided a textual reference to an entity .
Approach:	They identify a systematic deficiency in the multimodal grounding of Vision Language Models . they show that VLMs struggle to link their internal knowledge of an entity with its image representation .
Outcome:	The study shows that VLMs struggle to link internal knowledge with image representations . the findings provide recommendations for future research .

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Jackson Trager, Francielle Vargas, Diego Alves, Matteo Guida, Mikel K. Ngueajio, Ameeta Agrawal, Yalda Daryani, Farzan Karimi Malekabadi, Flor Miriam Plaza-del-Arco

Large Language Models for Multilingual Previously Fact-Checked Claim Detection (2025.findings-emnlp)

Challenge:	Existing evaluation benchmarks for large language models lack annotations that justify moral classifications and focus on English constrain moral reasoning across diverse cultural settings.
Approach:	They propose a multilingual benchmark dataset for evaluating moral reasoning of large language models . it includes 3,000 tweets annotated with binary hate speech labels, moral categories and rationales .
Outcome:	The proposed dataset shows a misalignment between LLM outputs and human annotations in moral reasoning tasks.

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Ivan Vykopal, Matúš Pikuliak, Simon Ostermann, Tatiana Anikina, Michal Gregor, Marian Simko

Debating for Better Reasoning in Vision-Language Models (2025.findings-emnlp)

Challenge:	a new study evaluates large language models for multilingual previously fact-checked claim detection . authors assess seven LLMs across 20 languages in monolingual and cross-lingual settings .
Approach:	They evaluate large language models for multilingual previously fact-checked claim detection . they find they perform well for high-resource languages, struggle with low-resourced languages .
Outcome:	The proposed model performs well for high-resource languages, but struggle with low-resourced languages.

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Ashutosh Adhikari, Mirella Lapata

Fine-tuning LLMs with Cross-Attention-based Weight Decay for Bias Mitigation (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) gain expertise across diverse domains and modalities, a new study shows . scalable oversight becomes challenging when their capabilities surpass human evaluators.
Approach:	a new study extends the debate paradigm to a multimodal setting . it explores the potential for blind models to supervise and enhance the performance of sighted ones.
Outcome:	The proposed framework outperforms individual LLMs on multimodal tasks . it allows blind models to supervise and enhance the performance of sighted models .

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Farsheed Haque, Zhe Fu, Depeng Xu, Shuhan Yuan, Xi Niu

Profiling LLM’s Copyright Infringement Risks under Adversarial Persuasive Prompting (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) excel in natural language processing tasks but often propagate societal biases from their training data, leading to discriminatory outputs.
Approach:	They propose a method that modifies the LLM architecture to mitigate bias by adjusting the attention weights of sensitive tokens.
Outcome:	The proposed method can handle multiple sensitive attributes and does not require full knowledge of sensitive tokens presented in the dataset.

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Jikai Long, Ming Liu, Xiusi Chen, Jialiang Xu, Shenglan Li, Zhaozhuo Xu, Denghui Zhang

Peter Zeng, Pegah Alipoormolabashi, Jihu Mun, Gourab Dey, Nikita Soni, Niranjan Balasubramanian, Owen Rambow, H. Schwartz

Challenge:	Large Language Models have demonstrated impressive capabilities in text generation but raise concerns regarding potential copyright infringement.
Approach:	They propose a structured persuasion workflow to analyze the influence of persuasive prompts on LLM outputs.
Outcome:	The proposed method analyzes the influence of persuasive prompts on LLM outputs.

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Post-hoc Study of Climate Microtargeting on Social Media Ads with LLMs: Thematic Insights and Fairness Evaluation (2025.findings-emnlp)

Challenge:	Neural methods achieve high accuracy, but their representations lack direct interpretability.
Approach:	They propose a method that supplements systems using interpretable features with a neural network to improve their performance while maintaining interpretability.
Outcome:	The proposed method improves the performance of state-of-the-art models while maintaining interpretability.

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Tunazzina Islam, Dan Goldwasser

MRFD: Multi-Region Fusion Decoding with Self-Consistency for Mitigating Hallucinations in LVLMs (2025.findings-emnlp)

Challenge:	Climate change communication on social media increasingly employs microtargeting strategies to effectively reach and influence specific demographic groups.
Approach:	They analyze social media ads using large language models to examine their performance . they find that LLMs perform well overall, but certain biases exist .
Outcome:	The results show that LLMs perform well overall, but certain biases exist in certain demographic groups.

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Haonan Ge, Yiwei Wang, Ming-Hsuan Yang, Yujun Cai

Debarun Bhattacharjya, Balaji Ganesan, Junkyu Lee, Radu Marinescu, Katya Mirylenka, Michael Glass, Xiao Shou

Challenge:	Large Vision-Language Models often produce hallucinations due to the limited ability to verify information in different regions of the image.
Approach:	a new decoding method improves factual grounding by modeling inter-region consistency . the method identifies salient regions using cross-attention and generates initial responses for each .
Outcome:	a training-free decoding method reduces hallucinations and improves response consistency . the proposed method generates initial responses for each region and weights reliability weights among responses .

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Mind the Dialect: NLP Advancements Uncover Fairness Disparities for Arabic Users in Recommendation Systems (2025.findings-emnlp)

Challenge:	Uncertainty quantification (UQ) provides measures of uncertainty, such as an estimate of the confidence in an LLM’s generated output.
Approach:	They propose a black-box approach where consistency is used as a proxy for confidence in a model's output.
Outcome:	The proposed methods are primarily but not necessarily entirely black- box, with consistency between output and other sampled generations used as a proxy for confidence in its correctness.

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Abdulla Alshabanah, Murali Annavaram

Hopscotch: Discovering and Skipping Redundancies in Language Models (2025.findings-emnlp)

Challenge:	a recent study shows that recommendation systems can exhibit unfair behavior when performance varies across users . the authors highlight the intersection of NLP and recommendation system research .
Approach:	They investigate fairness disparities in recommendation quality among Arabic-speaking users . arab-speaking people's dialectal diversity is underrepresented in recommendation system research .
Outcome:	The authors highlight the intersection of NLP and recommendation systems . their findings highlight the broader social impact of N.

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Mustafa Eyceoz, Nikhil Shivakumar Nayak, Hao Wang, Ligong Han, Akash Srivastava

CLEAR: A Clinically Grounded Tabular Framework for Radiology Report Evaluation (2025.findings-emnlp)

Challenge:	Hopscotch is a method that skips attention blocks with least contributions to a task . it does not modify model weights or require access to pretraining or instruction-tuning data.
Approach:	Hopscotch proposes a method that skips attention blocks with least contributions to a task . it introduces lightweight scaling parameters to attention and MLP blocks .
Outcome:	The proposed method reduces the drop in performance by 2% even after skipping four attention blocks.

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Yuyang Jiang, Chacha Chen, Shengyuan Wang, Feng Li, Zecong Tang, Benjamin M. Mervak, Lydia Chelala, Christopher M Straus, Reve Chahine, Samuel G. Armato Iii, Chenhao Tan

Parsing the Switch: LLM-Based UD Annotation for Complex Code-Switched and Low-Resource Languages (2025.findings-emnlp)

Challenge:	Existing metrics lack the granularity and interpretability to capture nuanced clinical differences between candidate and ground-truth radiology reports.
Approach:	They propose a tabular framework with Expert-curated labels and an attribute-level comparison for radiology report evaluation (CLEAR)
Outcome:	The proposed framework can extract clinical attributes and provide automated metrics that are strongly aligned with clinical judgment.

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Olga Kellert, Nemika Tyagi, Muhammad Imran, Nelvin Licona-Guevara, Carlos Gómez-Rodríguez

HetGCoT: Heterogeneous Graph-Enhanced Chain-of-Thought LLM Reasoning for Academic Question Answering (2025.findings-emnlp)

Challenge:	Existing approaches to analyzing code-switched data are limited in their ability to generalize to multilingual and mixed-language inputs.
Approach:	They propose a large-language model-based annotation pipeline to produce UD annotations for code-switched text.
Outcome:	The proposed pipeline outperforms existing parsers and baselines in syntactic analysis.

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Runsong Jia, Mengjia Wu, Ying Ding, Jie Lu, Yi Zhang

S*: Test Time Scaling for Code Generation (2025.findings-emnlp)

Challenge:	graph neural networks capture structured graph information, but lack integration at the reasoning level.
Approach:	They propose a framework that leverages graph structural information to reason interpretable academic QA results.
Outcome:	The proposed framework outperforms sota baselines on OpenAlex and DBLP datasets.

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Dacheng Li, Shiyi Cao, Chengkun Cao, Xiuyu Li, Shangyin Tan, Kurt Keutzer, Jiarong Xing, Joseph E. Gonzalez, Ion Stoica

Language Models Can Easily Learn to Reason from Demonstrations (2025.findings-emnlp)

Challenge:	S* is the first hybrid test-time scaling framework that significantly improves the coverage and selection accuracy of generated code.
Approach:	They propose a hybrid test-time scaling framework that augments parallel scaling with sequential scaling to further increase the performance.
Outcome:	The proposed framework outperforms existing scaling approaches in large-scale modeling and reasoning models.

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Dacheng Li, Shiyi Cao, Tyler Griggs, Shu Liu, Xiangxi Mo, Eric Tang, Sumanth Hegde, Kourosh Hakhamaneshi, Shishir G Patil, Matei Zaharia, Joseph E. Gonzalez, Ion Stoica

FSTs vs ICL: Generalisation in LLMs for an under-resourced language (2025.findings-emnlp)

Challenge:	Large reasoning models (LRMs) tackle complex problems by following long chain-of-thoughts (Long CoT) however, the training techniques and data requirements to elicit Long CoT remain poorly understood.
Approach:	They propose to use data-efficient supervised fine-tuning and parameter-efficient low-rank adaptation to elicit Long CoT reasoning.
Outcome:	The proposed model can learn Long CoT reasoning through data-efficient supervised fine-tuning and parameter-efficient low-rank adaptation.

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Ximena Gutierrez, Mikel Segura Elizalde, Victor Mijangos

SRM-LLM: Semantic Relationship Mining with LLMs for Temporal Knowledge Graph Extrapolation (2025.findings-emnlp)

Challenge:	LLMs have been widely adopted to tackle many traditional NLP tasks, but their effectiveness remains uncertain in scenarios where pre-trained models have limited prior knowledge of a language.
Approach:	They propose a rule-based method using a finite-state transducer and an in-context learning method that provides the model with string transduction examples.
Outcome:	The proposed method outperforms FSTs in zero-shot settings while ICL surpasses FLMs.

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Fu Zhang, Panfeng Zhang, Jingwei Cheng

Captioning for Text-Video Retrieval via Dual-Group Direct Preference Optimization (2025.findings-emnlp)

Challenge:	Existing methods for temporal knowledge graph extrapolation neglect the complex semantic relationships between relations when modeling their dynamic evolution.
Approach:	They propose a method for extracting semantic relationships to achieve TKG extrapolation . they use large language models to analyze the types of relations in TKGs .
Outcome:	The proposed method improves on five TKG datasets and shows performance gains.

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Ji Soo Lee, Byungoh Ko, Jaewon Cho, Howoong Lee, Jaewoon Byun, Hyunwoo J. Kim

Benchmarking and Improving LLM Robustness for Personalized Generation (2025.findings-emnlp)

Challenge:	auxiliary captions are generic and indistinguishable across visually similar videos . conventional captioning approaches are evaluated using language relevance scores .
Approach:	They propose a retrieval framework that directly optimizes caption generation using retrieval relevance scores.
Outcome:	The proposed retrieval framework optimizes caption generation using retrieval relevance scores . dual-group direct preference optimization is a learning strategy that supervises captioning .

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Chimaobi Okite, Naihao Deng, Kiran Bodipati, Huaidian Hou, Joyce Chai, Rada Mihalcea

MemeInterpret: Towards an All-in-One Dataset for Meme Understanding (2025.findings-emnlp)

Challenge:	Existing evaluations focus on whether a model’s responses align with a user’s preferences, but factuality is an important yet overlooked dimension.
Approach:	They propose a scalable framework for evaluating robustness of large language models in personalization and a new dataset, PERGData.
Outcome:	The proposed framework improves robustness by 25% across models.

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Jeongsik Park, Khoi P. N. Nguyen, Jihyung Park, Minseok Kim, Jaeheon Lee, Jae Won Choi, Kalyani Ganta, Phalgun Ashrit Kasu, Rohan Sarakinti, Sanjana Vipperla, Sai Sathanapalli, Nishan Vaghani, Vincent Ng

CoRAG: Enhancing Hybrid Retrieval-Augmented Generation through a Cooperative Retriever Architecture (2025.findings-emnlp)

Challenge:	Existing research has not explored meme captioning's decomposition into subtasks or its connections to other CMU tasks.
Approach:	a new meme corpus is built upon the Facebook Hateful Memes dataset . it contains meme captions, corresponding surface messages and relevant background knowledge .
Outcome:	a new corpus of meme captions and surface messages unifies three major categories of CMU tasks for the first time.

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Zaiyi Zheng, Song Wang, Zihan Chen, Yaochen Zhu, Yinhan He, Liangjie Hong, Qi Guo, Jundong Li

Hallucination Detection in Structured Query Generation via LLM Self-Debating (2025.findings-emnlp)

Challenge:	Existing methods only retrieve related documents from local neighbors or subgraphs in the knowledge base, which often miss relevant information located further away from a global view.
Approach:	Hybrid-RAG combines textual documents and graph-structured relational information for RAG . existing methods only retrieve related documents from local neighbors or subgraphs in the knowledge base .
Outcome:	Hybrid-RAG combines textual documents and graph-structured relational information . existing methods only retrieve related documents from local neighbors or subgraphs in the knowledge base .

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Miaoran Li, Jiangning Chen, Minghua Xu, Xiaolong Wang

Not All Options Are Created Equal: Textual Option Weighting for Token-Efficient LLM-Based Knowledge Tracing (2025.findings-emnlp)

Challenge:	Hallucination remains a key challenge in applying large language models to structured query generation . we propose the Self-Debating framework to enhance detection performance .
Approach:	They propose a framework that prompts an LLM to generate contrastive explanations from opposing perspectives . they also propose 'self-debating' framework to enhance detection performance .
Outcome:	The proposed framework outperforms LLM-as-a-Judge baselines in hallucination detection . the framework generates contrastive explanations from opposing perspectives .

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Jongwoo Kim, SeongYeub Chu, Bryan Wong, Mun Yong Yi

Public Data Assisted Differentially Private In-Context Learning (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have strong reasoning and generalization abilities, but they struggle to reflect the histories of example learners within a single prompt during in-context learning.
Approach:	They propose a LLM-based option weighted knowledge tracing framework that encodes the interaction histories of example learners in context as textual categorical option weights.
Outcome:	The proposed framework outperforms existing LLM-based KT models in warm-start and few-shot settings.

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Seongho Joo, Hyukhun Koh, Kyomin Jung

Inducing Argument Facets for Faithful Opinion Summarization (2025.findings-emnlp)

Challenge:	In-context learning has shown remarkable performance across tasks without fine-tuning . however, recent studies have highlighted the risk of private data leakage through the prompt in ICL .
Approach:	They propose a private in-context learning algorithm that effectively balances privacy protection and model utility.
Outcome:	The proposed algorithm is robust against membership inference attacks and is robust to membership infertility attacks.

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Jian Wang, Yanjie Liang, Yuqing Sun, Bin Gong

Scaling Laws Are Unreliable for Downstream Tasks: A Reality Check (2025.findings-emnlp)

Challenge:	Faithful opinion summarization task involves generating a summary that covers the majority and minority opinions in documents.
Approach:	They propose a facets-guided opinion summarization method that induces facets and partitions documents into multiple facet-specific sets.
Outcome:	The proposed method outperforms state-of-the-art methods and multiple LLMs on two representative datasets and shows it can be used in specialty domains.

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Nicholas Lourie, Michael Y. Hu, Kyunghyun Cho

Challenge:	Downstream scaling laws aim to predict task performance at larger scales from the model’s performance at smaller scales.
Approach:	They conduct a meta-analysis of existing data on downstream scaling laws and find that predictable scaling only occurs in a minority of cases: 39% of the time.
Outcome:	The proposed scaling laws only occur in a minority of cases, and seemingly benign changes to the experimental setting can completely change the scaling behavior.

Familiarity-Aware Evidence Compression for Retrieval-Augmented Generation (2025.findings-emnlp)

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Dongwon Jung, Qin Liu, Tenghao Huang, Ben Zhou, Muhao Chen

O_O-VC: Synthetic Data-Driven One-to-One Alignment for Any-to-Any Voice Conversion (2025.findings-emnlp)

Challenge:	Retrieval-augmented generation (RAG) improves large language models by incorporating non-parametric knowledge through evidence retrieved from external sources.
Approach:	They propose a training-free evidence compression technique that makes retrieved evidence more familiar to the target model while seamlessly integrating parametric knowledge from the model.
Outcome:	The proposed technique outperforms the most recent evidence compression baselines across open-domain QA datasets while achieving high compression rates.

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Huu Tuong Tu, Huan Vu, Cuong Tien Nguyen, Dien Hy Ngo, Nguyen Thi Thu Trang

Simple Factuality Probes Detect Hallucinations in Long-Form Natural Language Generation (2025.findings-emnlp)

Challenge:	Traditional voice conversion methods attempt to separate speaker identity and linguistic information into distinct representations, but this method often leads to information loss during training.
Approach:	They propose a method that leverages synthetic speech data generated by a pretrained model . synthetic data pairs that share the same linguistic content are used as input-output pairs .
Outcome:	The proposed method outperforms state-of-the-art methods in speaker-to-voice conversions.

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Jiatong Han, Neil Band, Muhammed Razzak, Jannik Kossen, Tim G. J. Rudner, Yarin Gal

CESRec: Constructing Pseudo Interactions for Sequential Recommendation via Conversational Feedback (2025.findings-emnlp)

Challenge:	Current approaches to detect hallucination require many samples from the LLM generator . current methods require multiple samples, which is computationally infeasible .
Approach:	They propose a simple baseline for detecting hallucinations in long-form LLM generations . they show that LLM hidden states are highly predictive of factuality in long form natural language generation .
Outcome:	The proposed method is comparable to expensive multi-sample approaches while drawing only a single sample from the LLM generator.

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Yifan Wang, Shen Gao, Jiabao Fang, Rui Yan, Billy Chiu, Shuo Shang

TTPA: Token-level Tool-use Preference Alignment Training Framework with Fine-grained Evaluation (2025.findings-emnlp)

Challenge:	Existing Sequential Recommendation Systems (SRS) rely on collaborative filtering signals and fail to capture real-time user preferences.
Approach:	They propose a framework that integrates the long-term preference modeling of SRS with the real-time preference elicitation of CRS.
Outcome:	The proposed framework integrates the long-term preference modeling of SRS with the real-time preference elicitation of CRS.

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Chengrui Huang, Shen Gao, Zhengliang Shi, Dongsheng Wang, Shuo Shang

Avoiding Knowledge Edit Skipping in Multi-hop Question Answering with Guided Decomposition (2025.findings-emnlp)

Challenge:	Existing tool-learning methods often overlook fine-grained optimization of internal tool call details.
Approach:	They propose a training paradigm for constructing token-level tool-use preference datasets . reversed dataset construction is a method for creating high-quality, multi-turn tool-user datasets by reversing the generation flow.
Outcome:	a new training paradigm improves tool-using performance and generalizes results.

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Yi Liu, Xiangrong Zhu, Xiangyu Liu, Wei Wei, Wei Hu

Bridging the Creativity Understanding Gap: Small-Scale Human Alignment Enables Expert-Level Humor Ranking in LLMs (2025.findings-emnlp)

Challenge:	Existing methods for knowledge editing fail to work in multi-hop question answering due to 'edit skipping' edit skipping occurs due to the mismatch between the granularity of LLMs in problem-solving and the facts in the edited memory.
Approach:	They propose a retrieval-augmented generation-based method that edits knowledge without modifying parameters without retraining LLMs.
Outcome:	The proposed method outperforms state-of-the-art methods for KE in multi-hop question answering.

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Kuan Lok Zhou, Jiayi Chen, Siddharth Suresh, Reuben Narad, Timothy T. Rogers, Lalit K Jain, Robert D Nowak, Bob Mankoff, Jifan Zhang

Commonsense Reasoning

Challenge:	Large Language Models (LLMs) have shown significant limitations in understanding creative content, as demonstrated by Hessel et al. (2023)’s influential work on the New Yorker Cartoon Caption Contest.
Approach:	They propose to decompose humor understanding into three components and improve each by enhancing visual understanding through improved annotation and utilizing LLM-generated humor reasoning and explanations.
Outcome:	The proposed approach achieves 82.4% accuracy in caption ranking, significantly better than the previous 67% benchmark and matches the performance of world-renowned human experts in this domain.

SMARTMiner: Extracting and Evaluating SMART Goals from Low-Resource Health Coaching Notes (2025.findings-emnlp)

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Iva Bojic, Qi Chwen Ong, Stephanie Hilary Xinyi Ma, Lin Ai, Zheng Liu, Ziwei Gong, Julia Hirschberg, Andy Hau Yan Ho, Andy W. H. Khong

GRIL: Knowledge Graph Retrieval-Integrated Learning with Large Language Models (2025.findings-emnlp)

Challenge:	SMARTMiner extracts specific, measurable, attainable, relevant, time-bound (SMART) goals from unstructured health coaching notes.
Approach:	They propose a framework for extracting and evaluating specific, measurable, attainable, relevant, time-bound (SMART) goals from unstructured health coaching notes.
Outcome:	The framework extracts behavior change goal spans and categorizes their SMARTness.

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Jialin Chen, Houyu Zhang, Seongjun Yun, Alejandro Mottini, Rex Ying, Xiang Song, Vassilis N. Ioannidis, Zheng Li, Qingjun Cui

Exploring Deductive and Inductive Reasoning Capabilities of Large Language Models in Procedural Planning (2025.findings-emnlp)

Challenge:	Existing graph RAGs decouple retrieval and reasoning processes, preventing adaptability . existing graph Raggings depend heavily on ground-truth entities, which are often unavailable in open-domain settings.
Approach:	They propose a graph retriever that is trained end-to-end with large-scale graphs . structure and semantic features are encoded via soft tokens and the verbalized graph .
Outcome:	The proposed approach improves the performance of large-scale graph retrieval models by grounding it with external knowledge.

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Jiabao Kang, Xinye Li, Liyan Xu, Qingbin Liu, Xi Chen, Zhiying Tu, Dianhui Chu, Dianbo Sui

KELE: A Multi-Agent Framework for Structured Socratic Teaching with Large Language Models (2025.findings-emnlp)

Challenge:	Deductive and inductive reasoning are fundamental components of human cognition . authors present a benchmark to assess their performance in procedural planning .
Approach:	They propose a benchmark to assess the deductive and inductive reasoning abilities of LLMs . they propose IMSE to enable LLM to generate multiple similar procedural plans .
Outcome:	The proposed method improves inductive reasoning abilities of LLMs, the authors show . they show that LLM models show excellent deductive reasoning capabilities but suboptimal inductive performance.

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Xian Peng, Pan Yuan, Dong Li, Junlong Cheng, Qin Fang, Zhi Liu

VisualEDU: A Benchmark for Assessing Coding and Visual Comprehension through Educational Problem-Solving Video Generation (2025.findings-emnlp)

Challenge:	Socratic teaching places high demands on teachers’ expertise and real-time feedback capabilities, making it difficult to scale in large educational settings.
Approach:	They propose a multi-agent framework for structured Socratic teaching with LLMs that integrates a structured SocRule and a consultant-teacher collaborative teaching mechanism.
Outcome:	The proposed framework outperforms existing LLMs in natural language generation and dialogue comprehension in the classroom.

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Hao Chen, Tianyu Shi, Pengran Huang, Zeyuan Li, Jiahui Pan, Qianglong Chen, Lewei He

OkraLong: A Flexible Retrieval-Augmented Framework for Long-Text Question Answering (2025.findings-emnlp)

Challenge:	VisualEDU is a benchmark to evaluate VLMs' ability to produce coherent video from text . advanced proprietary models show promise, but struggle with increasing task complexity .
Approach:	VisualEDU is a benchmark to evaluate VLMs' ability to produce coherent video from text . it integrates meta-prompt learning, visual and code feedback, and a drawing toolkit to enhance output quality.
Outcome:	VisualEDU is a benchmark to evaluate VLMs' ability to produce coherent video from text . it integrates meta-prompt learning, visual and code feedback, and a drawing toolkit to improve output quality.

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Yulong Hui, Yihao Liu, Yao Lu, Huanchen Zhang

VerifiAgent: a Unified Verification Agent in Language Model Reasoning (2025.findings-emnlp)

Challenge:	a new framework for large language models addresses long-text questions . context compression and dynamic retrieval loops sacrifice critical details or incur iterative costs .
Approach:	a new framework is proposed to optimize the entire processing workflow . it uses synergistic components to analyzer, organizer and executor to optimize workflow a .
Outcome:	OkraLong improves answer accuracy by 5.7%-41.2% and saves 1.3x-4.7x .

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Jiuzhou Han, Wray Buntine, Ehsan Shareghi

DrKGC: Dynamic Subgraph Retrieval-Augmented LLMs for Knowledge Graph Completion across General and Biomedical Domains (2025.findings-emnlp)

Challenge:	Existing verification methods are typically model-specific or domain-restricted . Existing methods require significant computational resources and lack scalability .
Approach:	a unified verification agent integrates two levels of verification: meta-verification and tool-based adaptive verification.
Outcome:	The proposed agent outperforms baseline verification methods among reasoning tasks.

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Yongkang Xiao, Sinian Zhang, Yi Dai, Huixue Zhou, Jue Hou, Jie Ding, Rui Zhang

Understanding the Language Model to Solve the Symbolic Multi-Step Reasoning Problem from the Perspective of Buffer Mechanism (2025.findings-emnlp)

Challenge:	Knowledge graph completion (KGC) aims to predict missing triples in knowledge graphs . current approaches encode graph context in textual form, which fails to exploit its potential .
Approach:	a new method is proposed to predict missing triples in knowledge graphs by leveraging existing triples and textual information.
Outcome:	The proposed model learns structural embeddings and logical rules within the KG and extracts a subgraph for each query guided by the learned rules.

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Zhiwei Wang, Yunji Wang, Zhongwang Zhang, Zhangchen Zhou, Hui Jin, Tianyang Hu, Jiacheng Sun, Zhenguo Li, Yaoyu Zhang, Zhi-Qin John Xu

TwT: Thinking without Tokens by Habitual Reasoning Distillation with Multi-Teachers’ Guidance (2025.findings-emnlp)

Challenge:	Large language models struggle with complex reasoning tasks, such as mathematical problem-solving.
Approach:	They constructed a symbolic multi-step reasoning task to investigate the information propagation mechanisms in Transformer models when solving the task through direct answering and Chain-of-Thought (CoT) reasoning.
Outcome:	The proposed algorithm improves on 7 multi-step reasoning datasets, while introducing only 132 trainable parameters.

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Jingxian Xu, Mengyu Zhou, Weichang Liu, Hanbing Liu, Shi Han, Dongmei Zhang

DAVIS: Planning Agent with Knowledge Graph-Powered Inner Monologue (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have made significant strides in problem-solving by incorporating reasoning processes, but this enhanced reasoning capability results in an increased number of output tokens during inference, leading to higher computational costs.
Approach:	They propose a method that internalizes explicit reasoning into the model’s habitual behavior through a Teacher-Guided compression strategy inspired by human cognition.
Outcome:	The proposed method reduces inference-time costs while maintaining high performance while preserving high quality and diversity of the distillation dataset.

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Minh Pham Dinh, Michael G Yankoski, Munira Syed, Trenton W. Ford

Knowledge Representation

Challenge:	Existing approaches to designing a generalist scientific agent fail to address multifaceted requirements of scientific tasks.
Approach:	DAVIS is a generalist scientific agent capable of performing tasks in laboratory settings to assist researchers.
Outcome:	DAVIS performs better on ScienceWorld benchmarks compared to previous approaches on 8 out of 9 elementary science subjects.

When Instructions Multiply: Measuring and Estimating LLM Capabilities of Multiple Instructions Following (2025.findings-emnlp)

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Keno Harada, Yudai Yamazaki, Masachika Taniguchi, Edison Marrese-Taylor, Takeshi Kojima, Yusuke Iwasawa, Yutaka Matsuo

FormosanBench: Benchmarking Low-Resource Austronesian Languages in the Era of Large Language Models (2025.findings-emnlp)

Challenge:	a large number of languages are increasingly used to evaluate their ability to follow multiple instructions simultaneously.
Approach:	They propose two benchmarks to evaluate LLMs' ability to follow multiple instructions simultaneously . they use many instruction-following eval and style-aware Mostly Basic programming problems .
Outcome:	The proposed models predict performance on unseen instruction combinations and not used during training with 10% error.

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Kaiying Kevin Lin, Hsi-Yu Chen, Haopeng Zhang

SeaPO: Strategic Error Amplification for Robust Preference Optimization of Large Language Models (2025.findings-emnlp)

Challenge:	Existing LLMs consistently underperform across all tasks, with 10-shot learning and fine-tuning offering only limited improvements.
Approach:	They introduce FormosanBench, a benchmark for evaluating LLMs on low-resource Austronesian languages.
Outcome:	The proposed benchmark covers three endangered Formosan languages: Atayal, Amis, and Paiwan . existing LLMs consistently underperform across all tasks, with 10-shot learning and fine-tuning offering only limited improvements.

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Jun Rao, Yunjie Liao, Xuebo Liu, Zepeng Lin, Lian Lian, Dong Jin, Shengjun Cheng, Jun Yu, Min Zhang

FigEx: Aligned Extraction of Scientific Figures and Captions (2025.findings-emnlp)

Challenge:	Existing methods for preference optimization of large language models use pairs of positive and negative samples, but the quality of positive samples may become similar during training, complicating preference learning.
Approach:	SeaPO introduces error types commonly occurring in large language models to improve preference learning.
Outcome:	SeaPO introduces error types into model Preference Optimization to improve model performance . negative samples are more erroneous than positive samples, and preference-based training mitigates errors .

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Jifeng Song, Arun Das, Ge Cui, Yufei Huang

PATIMT-Bench: A Multi-Scenario Benchmark for Position-Aware Text Image Machine Translation in Large Vision-Language Models (2025.findings-emnlp)

Challenge:	FigEx is a vision-language model to extract aligned pairs of subfigures and subcaptions from scientific papers.
Approach:	They propose a vision-language model to extract aligned pairs of subfigures and subcaptions from scientific papers.
Outcome:	The proposed model improves subfigure detection APb over Grounding DINO by 0.023 and boosts caption separation BLEU over Llama-2-13B by 0.465.

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Wanru Zhuang, Wenbo Li, Zhibin Lan, Xu Han, Peng Li, Jinsong Su

Safeguard Fine-Tuned LLMs Through Pre- and Post-Tuning Model Merging (2025.findings-emnlp)

Challenge:	Current TIMT studies focus on providing translations for all text within an image, neglecting to provide bounding boxes and covering limited scenarios.
Approach:	They extend traditional TIMT into position-aware TIMt to support fine-grained translation . they introduce an Adaptive Image OCR Refinement Pipeline to refine results .
Outcome:	The proposed model supports fine-grained and layout-preserving translation . the experimental data highlight the scalability and generalizability of the model.

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Hua Farn, Hsuan Su, Shachi H. Kumar, Saurav Sahay, Shang-Tse Chen, Hung-yi Lee

Self-Ensemble: Mitigating Confidence Distortion for Large Language Models (2025.findings-emnlp)

Challenge:	Fine-tuning large language models for downstream tasks often leads to catastrophic forgetting, notably degrading the safety of original alignments.
Approach:	They propose to merge the weights of pre- and post-fine-tuned models to improve safety while enhancing performance.
Outcome:	Experiments across different downstream tasks and models validate the method’s practicality and effectiveness.

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Zicheng Xu, Guanchu Wang, Guangyao Zheng, Yu-Neng Chuang, Alex Szalay, Xia Hu, Vladimir Braverman

Annotation-Efficient Language Model Alignment via Diverse and Representative Response Texts (2025.findings-emnlp)

Challenge:	Large Language Models exhibit a confidence distortion problem on multichoice question-answering . Self-Ensemble solves this problem by splitting the choices into several groups .
Approach:	They propose a method that splits LLM choices into several groups and ensembles them to reach a final decision.
Outcome:	The proposed method outperforms standard inference and baseline methods on MCQA.

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Yuu Jinnai, Ukyo Honda

Explainable Chain-of-Thought Reasoning: An Empirical Analysis on State-Aware Reasoning Dynamics (2025.findings-emnlp)

Challenge:	obtaining large amount of preference annotations is difficult in many applications . obtaining a large amount is difficult, so a preference dataset needs limited annotation budget .
Approach:	They propose annotating preference over a subset of responses that maximizes diversity and representativeness from available responses and then annotates preference over the selected ones.
Outcome:	The proposed method outperforms baselines with the same annotation budget.

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Sheldon Yu, Yuxin Xiong, Junda Wu, Xintong Li, Tong Yu, Xiang Chen, Ritwik Sinha, Jingbo Shang, Julian McAuley

DecisionFlow: Advancing Large Language Model as Principled Decision Maker (2025.findings-emnlp)

Challenge:	Recent advances in chain-of-thought prompting have demonstrated the ability of large language models to perform multi-step reasoning.
Approach:	They propose a framework to analyze latent dynamics of CoT trajectories for interpretability . they segment generated CoT into discrete reasoning steps and abstract each step into a spectral embedding based on token-level Gram matrices .
Outcome:	The proposed framework segments generated CoT steps into discrete reasoning steps, abstracts each step into a spectral embedding based on token-level Gram matrices, and clusters these embeddements into semantically meaningful latent states.

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Xiusi Chen, Shanyong Wang, Cheng Qian, Hongru Wang, Peixuan Han, Heng Ji

M-Ped: Multi-Prompt Ensemble Decoding for Large Language Models (2025.findings-emnlp)

Challenge:	Current language models lack the structured deliberation needed for high-stakes tasks such as healthcare and finance.
Approach:	They propose a decision-making framework that guides models to reason over structured representations of actions, attributes, and constraints.
Outcome:	The proposed framework achieves up to 30% accuracy gains over strong prompting baselines and enhances alignment in outcomes.

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Jiaxin Guo, Daimeng Wei, Yuanchang Luo, Hengchao Shang, Zongyao Li, Jinlong Yang, Zhanglin Wu, Zhiqiang Rao, Shimin Tao, Hao Yang

Butterfly Effects in Toolchains: A Comprehensive Analysis of Failed Parameter Filling in LLM Tool-Agent Systems (2025.findings-emnlp)

Challenge:	a new ensemble decoding approach enhances the performance of Large Language Models.
Approach:	They propose a multi-prompt ensemble decoding approach to enhance LLM performance . they submit n variations of prompts with X to LLMs in batch mode to decode and derive probability distributions .
Outcome:	The proposed method improves pass@k rates, LENS metrics and BLEU scores on diverse NLP tasks.

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Qian Xiong, Yuekai Huang, Ziyou Jiang, Zhiyuan Chang, Yujia Zheng, Tianhao Li, Mingyang Li

FinLFQA: Evaluating Attributed Text Generation of LLMs in Financial Long-Form Question Answering (2025.findings-emnlp)

Challenge:	emergence of tool agent paradigm has broadened capability boundaries of the Large Language Model (LLM) but effectiveness of tool agents limited due to parameter failure during execution .
Approach:	They propose a parameter failure taxonomy to investigate parameter failure . they propose suggestions for standardizing tool return formats and improving error feedback mechanisms .
Outcome:	The proposed model is based on a tool agent invocation chain and a mainstream tool agent . it shows that parameter name hallucination failure stems from inherent limitations .

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Yitao Long, Tiansheng Hu, Yilun Zhao, Arman Cohan, Chen Zhao

BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models (2025.findings-emnlp)

Challenge:	Existing benchmarks focus on simple attribution that retrieves textual evidence as references.
Approach:	They propose a benchmark to evaluate the ability of large language models to generate reliable attributions.
Outcome:	The proposed benchmark evaluates the ability of LLMs to generate long-form answers with reliable and nuanced attributions.

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Xu Huang, Wenhao Zhu, Hanxu Hu, Conghui He, Lei Li, Shujian Huang, Fei Yuan

Assessing the Sensitivity and Alignment of FOL Closeness Metrics (2025.findings-emnlp)

Challenge:	Existing multilingual benchmarks focus primarily on language understanding tasks.
Approach:	They develop a multi-way multilingual benchmark that measures critical capabilities of large language models across languages.
Outcome:	Extensive experiments on BenchMAX reveal uneven utilization of core capabilities across languages, emphasizing the performance gaps that scaling model size alone does not resolve.

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Ramya Keerthy Thatikonda, Wray Buntine, Ehsan Shareghi

FoodSafeSum: Enabling Natural Language Processing Applications for Food Safety Document Summarization and Analysis (2025.findings-emnlp)

Challenge:	a recent paradigm of logical reasoning with tool-augmented large language models has been successful, but the correctness of FOL statements often go unverified due to the lack of a reliable evaluation metric.
Approach:	They compare sensitivity of existing metrics and their alignment with LLM to evaluate FOLs.
Outcome:	The proposed metrics improve the sensitivity and robustness of the proposed models.

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Juli Bakagianni, Korbinian Randl, Guido Rocchietti, Cosimo Rulli, Franco Maria Nardini, Salvatore Trani, Aron Henriksson, Anna Romanova, John Pavlopoulos

Self-adaptive Dataset Construction for Real-World Multimodal Safety Scenarios (2025.findings-emnlp)

Challenge:	a lack of structured datasets hinders natural language processing research . a new dataset of food safety documents and related metadata is presented .
Approach:	They present a dataset of human-written and Large Language Model (LLM)-generated food safety documents . they evaluate their utility on three NLP tasks directly reflecting food safety practices .
Outcome:	The proposed dataset performs comparably or better than human summaries on three NLP tasks . it also shows clustering of summary for event tracking and compliance monitoring .

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Jingen Qu, Lijun Li, Bo Zhang, Yichen Yan, Jing Shao

EnDive: A Cross-Dialect Benchmark for Fairness and Performance in Large Language Models (2025.findings-emnlp)

Challenge:	Existing dataset construction methods fail to cover the complexity of multimodal safety scenarios . lack of a unified evaluation metric makes them unproven .
Approach:	They propose a risk-oriented image-oriented self-adaptive dataset construction method for RMS . they automatically generate an RMS dataset comprising 35,610 image–text pairs with guidance responses .
Outcome:	The proposed method automatically generates an RMS dataset comprising 35,610 image–text pairs with guidance responses.

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Abhay Gupta, Jacob Cheung, Philip Meng, Shayan Sayyed, Kevin Zhu, Austen Liao, Sean O’Brien

FAEDKV: Infinite-Window Fourier Transform for Unbiased KV Cache Compression (2025.findings-emnlp)

Challenge:	Existing benchmarks often overlook intra-language variations, leaving speakers of non-standard dialects underserved.
Approach:	EnDive evaluates seven state-of-the-art large language models across tasks . human evaluations confirm high translation quality, with average scores of at least 6.02/7 .
Outcome:	EnDive evaluates state-of-the-art large language models across language understanding, reasoning, mathematics, logic tasks.

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Runchao Li, Yao Fu, Mu Sheng, Xianxuan Long, Haotian Yu, Pan Li

Dynamic Injection of Entity Knowledge into Dense Retrievers (2025.findings-emnlp)

Challenge:	Current compression strategies, including token eviction and learned projections, often lead to biased representations and may require costly model retraining.
Approach:	They propose a training-free KV cache compression framework that equalizes the contribution of all tokens to the compressed representation.
Outcome:	The proposed framework ensures unbiased information retention in the KV cache.

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Ikuya Yamada, Ryokan Ri, Takeshi Kojima, Yusuke Iwasawa, Yutaka Matsuo

When Personalization Meets Reality: A Multi-Faceted Analysis of Personalized Preference Learning (2025.findings-emnlp)

Challenge:	Dense retrievers struggle with queries involving less-frequent entities due to limited entity knowledge.
Approach:	They propose a BERT-based retriever enhanced with a context-entity attention layer and dynamically updatable entity embeddings.
Outcome:	The proposed retriever incorporates external entity knowledge without retraining.

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Yijiang River Dong, Tiancheng Hu, Yinhong Liu, Ahmet Üstün, Nigel Collier

Challenge:	Reinforcement Learning from Human Feedback assumes homogeneous preferences across users . personalization can introduce up to 20% safety misalignment .
Approach:	They propose a framework to assess personalized preference learning by tailoring preferences for users . they compare eight personalization methods across three preference datasets .
Outcome:	The proposed framework measures performance, fairness, unintended effects, adaptability across preferences . performance differences between personalization methods could reach 36% when users strongly disagree .

MASTER: Multi-Agent Security Through Exploration of Roles and Topological Structures - A Comprehensive Framework (2025.findings-emnlp)

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Yifan Zhu, Chao Zhang, Xin Shi, Xueqiao Zhang, Yi Yang, Yawei Luo

MONAQ: Multi-Objective Neural Architecture Querying for Time-Series Analysis on Resource-Constrained Devices (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs)-based Multi-Agent Systems (MAS) exhibit remarkable problem-solving and task planning capabilities across diverse domains .
Approach:	They propose a security research framework for LLM-based multi-agent systems . they propose corresponding defense strategies to address MAS security risks .
Outcome:	The proposed framework amplifies the severity of security risks under MAS attacks . it offers an automated construction process for different MAS setups and an interaction paradigm .

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Patara Trirat, Jae-Gil Lee

StandUp4AI: A New Multilingual Dataset for Humor Detection in Stand-up Comedy Videos (2025.findings-emnlp)

Challenge:	Recent efforts in hardware-aware neural architecture search (NAS) automate architecture discovery for specific platforms; however, none focus on general time-series analysis with edge deployment.
Approach:	They propose a framework that reformulates NAS into *Multi-Objective Neural Architecture Q*uerying tasks.
Outcome:	Experiments on 15 datasets show that the proposed framework outperforms both handcrafted models and NAS baselines while being more efficient.

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Valentin Barriere, Nahuel Gomez, Léo Hemamou, Sofia Callejas, Brian Ravenet

Does Visual Grounding Enhance the Understanding of Embodied Knowledge in Large Language Models? (2025.findings-emnlp)

Challenge:	a new multimodal dataset of stand-up comedies is proposed to improve humor detection . the dataset is the biggest available for this type of task, and the most diverse .
Approach:	They propose a method to enhance the automatic laughter detection based on Audio Speech Recognition errors.
Outcome:	The proposed method improves existing models of humor detection by using audio speech recognition errors.

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Zhihui Yang, Yupei Wang, Kaijie Mo, Zhe Zhao, Renfen Hu

Semantic Contribution-Aware Adaptive Retrieval for Black-Box Models (2025.findings-emnlp)

Challenge:	Despite significant progress in multimodal language models, it remains unclear whether visual grounding enhances their understanding of embodied knowledge compared to text-only models.
Approach:	They propose to assess vision-language models’ perceptual abilities across different sensory modalities through vector comparison and question-answering tasks with over 1,700 questions.
Outcome:	The proposed benchmark assesses the models’ perceptual abilities across different sensory modalities through vector comparison and question-answering tasks with over 1,700 questions.

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Qinhong Lin, Zhongliang Yang, Yuang Cai, Dingfu Yu, Xuan Xu, Yu Li, Linna Zhou

On Guardrail Models’ Robustness to Mutations and Adversarial Attacks (2025.findings-emnlp)

Challenge:	Existing approaches to retrieval-agmented generation fail to generalize effectively in black-box scenarios.
Approach:	They propose a framework that leverages the semantic importance of words to dynamically adjust retrieval thresholds and filter information.
Outcome:	The proposed framework achieves the highest score on four long-form, knowledge-intensive generation datasets.

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Elias Bassani, Ignacio Sanchez

IP-Dialog: Evaluating Implicit Personalization in Dialogue Systems with Synthetic Data (2025.findings-emnlp)

Challenge:	generative AI systems providing unsafe information has raised significant concerns, emphasizing the need for safety guardrails.
Approach:	They propose to evaluate 15 state-of-the-art guardrail models to assess their robustness to input mutations and adversarial attacks designed to bypass models’ safety alignment.
Outcome:	The proposed models are robust to input mutations and adversarial attacks that bypass models’ safety alignment.

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Bo Peng, Zhiheng Wang, Heyang Gong, Chaochao Lu

Zero-shot Graph Reasoning via Retrieval Augmented Framework with LLMs (2025.findings-emnlp)

Challenge:	Traditional data generation methods are labor-intensive, resource-demanding, and raise privacy concerns.
Approach:	They propose an automatic synthetic data generation approach and introduce the Implicit Personalized Dialogue benchmark along with a training dataset, covering 10 tasks and 12 user attribute types.
Outcome:	The proposed approach incorporates the Implicit Personalized Dialog**ue benchmark along with a training dataset, covering 10 tasks and 12 user attribute types.

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Hanqing Li, Sharika Mahadevan, Kiran Jyothi Sheena, Henry Liang, Diego Klabjan

Privacy in Action: Towards Realistic Privacy Mitigation and Evaluation for LLM-Powered Agents (2025.findings-emnlp)

Challenge:	Existing methods that require extensive finetuning or depend on predefined algorithms are limited by training.
Approach:	a new retrieval-augmented framework is proposed that harnesses retrieval and large language models to address graph reasoning tasks.
Outcome:	The proposed method achieves 100% accuracy on most graph reasoning tasks while maintaining consistent token costs regardless of graph sizes.

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Shouju Wang, Fenglin Yu, Xirui Liu, Xiaoting Qin, Jue Zhang, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan

Dissecting Logical Reasoning in LLMs: A Fine-Grained Evaluation and Supervision Study (2025.findings-emnlp)

Challenge:	Existing benchmarks for privacy performance of LLM agents are limited to static, simplified scenarios.
Approach:	They propose a model-agnostic, contextual integrity based mitigation approach that effectively reduces privacy leakage from 36.08% to 7.30% on DeepSeek-R1 and from 33.06% to 8.32% on GPT-4o.
Outcome:	The proposed approach reduces privacy leakage from 36.08% to 7.30% on DeepSeek-R1 and from 33.06% to 8.32% on GPT-4o while preserving task helpfulness.

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Yujun Zhou, Jiayi Ye, Zipeng Ling, Yufei Han, Yue Huang, Haomin Zhuang, Zhenwen Liang, Kehan Guo, Taicheng Guo, Xiangqi Wang, Xiangliang Zhang

ConciseRL: Conciseness-Guided Reinforcement Learning for Efficient Reasoning Models (2025.findings-emnlp)

Challenge:	Existing benchmarks that rely on final-answer accuracy fail to capture the quality of the reasoning process.
Approach:	They propose a fine-grained evaluation framework that assesses logical reasoning across three dimensions: overall accuracy, stepwise soundness, and representation-level probing.
Outcome:	The proposed framework assesses logical reasoning across three dimensions: overall accuracy, stepwise soundness, and representation-level probing.

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Razvan-Gabriel Dumitru, Darius Peteleaza, Vikas Yadav, Liangming Pan

Faster and Better LLMs via Latency-Aware Test-Time Scaling (2025.findings-emnlp)

Challenge:	Large language models excel at complex tasks by breaking down problems into structured reasoning steps.
Approach:	They propose a hyperparameter-free conciseness score to guide models toward generating correct reasoning traces.
Outcome:	The proposed method reduces token usage by 31x on simple problems while improving accuracy by 7% on the hardest problems.

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Zili Wang, Tianyu Zhang, Haoli Bai, Lu Hou, Xianzhi Yu, Wulong Liu, Shiming Xiang, Lei Zhu

Challenge:	Existing research has overlooked the efficiency of TTS from a latency-sensitive perspective.
Approach:	They propose two approaches to achieve latency-optimal TTS by branch-wise parallelism and sequence-wise parallelism.
Outcome:	The proposed approach achieves latency-optimal TTS for large models . branch-wise parallelism and sequence-wise parallelism are key approaches .

Reasoning-Augmented Conversation for Multi-Turn Jailbreak Attacks on Large Language Models (2025.findings-emnlp)

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Zonghao Ying, Deyue Zhang, Zonglei Jing, Yisong Xiao, Quanchen Zou, Aishan Liu, Siyuan Liang, Xiangzheng Zhang, Xianglong Liu, Dacheng Tao

Distilling Many-Shot In-Context Learning into a Cheat Sheet (2025.findings-emnlp)

Challenge:	Existing methods for implementing multi-turn jailbreaks struggle to balance semantic coherence with attack effectiveness, resulting in benign semantic drift or ineffective detection evasion.
Approach:	They propose a framework that reformulates harmful queries into benign reasoning tasks and leverages LLMs’ strong reasoning capabilities to compromise safety alignment.
Outcome:	The proposed framework achieves state-of-the-art attack effectiveness in complex conversational scenarios, with average ASRs increasing by up to 96%.

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Ukyo Honda, Soichiro Murakami, Peinan Zhang

Tracing Training Footprints: A Calibration Approach for Membership Inference Attacks Against Multimodal Large Language Models (2025.findings-emnlp)

Challenge:	Recent advances in large language models (LLMs) enable effective in-context learning with many-shot examples, but at the cost of high computational demand due to longer input tokens.
Approach:	proposed cheat-sheet ICL distills information from many-shot ICL into a concise textual summary . experiment shows cheat- sheet ICL achieves comparable or better performance than many- shot ICL .
Outcome:	Experiments on reasoning tasks show that cheat-sheet ICL achieves comparable or better performance than many-shot ICL with far fewer tokens.

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Xiaofan Zheng, Huixuan Zhang, Xiaojun Wan

PolBiX: Detecting LLMs’ Political Bias in Fact-Checking through X-phemisms (2025.findings-emnlp)

Challenge:	Existing methods to improve difficulty calibration for Multimodal Large Language Models only consider text input . visual embeddings in training data reduce effectiveness of these methods .
Approach:	They propose a method to detect member samples in poorly generalized local manifolds by visual embeddings.
Outcome:	The proposed method surpasses existing methods.

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Charlott Jakob, David Harbecke, Patrick Parschan, Pia Wenzel Neves, Vera Schmitt

URO-Bench: Towards Comprehensive Evaluation for End-to-End Spoken Dialogue Models (2025.findings-emnlp)

Challenge:	a few models show tendencies of political bias, but this is not mitigated by explicitly calling for objectivism in prompts.
Approach:	They investigate political bias by exchanging words with euphemisms or dysphemismas in German claims.
Outcome:	The proposed model shows that political bias influences truthfulness assessment more than political leaning .

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Ruiqi Yan, Xiquan Li, Wenxi Chen, Zhikang Niu, Chen Yang, Ziyang Ma, Kai Yu, Xie Chen

Speech & Audio in NLP

Challenge:	a lack of comprehensive evaluations for SDMs in speech-to-speech (S2S) scenarios is a major challenge for end-to end spoken dialogue models.
Approach:	They propose to provide an extensive evaluation framework for end-to-end spoken dialogue models (SDMs) that includes both cognitive dimensions and paralinguistic cues .
Outcome:	The proposed benchmark is divided into two difficulty levels: basic track and pro track, each comprising 20 test sets, evaluating the spoken dialogue model’s abilities in Understanding, Reasoning, and O**ral conversation.

Low-Hallucination and Efficient Coreference Resolution with LLMs (2025.findings-emnlp)

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Yujian Gan, Yuan Liang, Jinxia Xie, Yanni Lin, Juntao Yu, Massimo Poesio

Your Mileage May Vary: How Empathy and Demographics Shape Human Preferences in LLM Responses (2025.findings-emnlp)

Challenge:	Large Language Models have shown promising results in coreference resolution, but they face a critical issue: hallucinations.
Approach:	They propose a low-hallucination and efficient solution to the problem of hallucinations . they propose efficient constrained decoding for coreference resolution .
Outcome:	The proposed approach achieves better performance on the English OntoNotes development set.

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Yishan Wang, Amanda Cercas Curry, Flor Miriam Plaza-del-Arco

Challenge:	large language models (LLMs) increasingly assist subjective decision-making . prior work uses aggregate human judgments, but demographic variation and its linguistic drivers remain underexplored.
Approach:	They analyze how demographic background and empathy level correlate with LLM-generated dilemma responses . they also identify markers that predict group-level differences .
Outcome:	The authors show that demographic background and empathy level correlate with LLM preferences . their findings highlight the need for demographically informed LLM evaluations.

Diving into Mitigating Hallucinations from a Vision Perspective for Large Vision-Language Models (2025.findings-emnlp)

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Weihang Wang, Xinhao Li, Ziyue Wang, Yan Pang, Jielei Zhang, Peiyi Li, Qiang Zhang, Longwen Gao

PhysicsArena: The First Multimodal Physics Reasoning Benchmark Exploring Variable, Process, and Solution Dimensions (2025.findings-emnlp)

Challenge:	Existing benchmarks focus on coarse-grained hallucination detection and fail to capture hallucinics . vision encoders exhibit unique hallucinian characteristics, but suboptimal of simple feature fusion.
Approach:	They propose a visual encoder that employs different training paradigms to instill inductive biases in visual encoded models.
Outcome:	The proposed system reduces hallucinations and improves model performance.

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Song Dai, Yibo Yan, Jiamin Su, Zihao Dongfang, Yubo Gao, Yonghua Hei, Jungang Li, Junyan Zhang, Sicheng Tao, Zhuoran Gao, Xuming Hu

Ko-LongRAG: A Korean Long-Context RAG Benchmark Built with a Retrieval-Free Approach (2025.findings-emnlp)

Challenge:	Current physics benchmarks focus on text-only inputs or only on problem-solving . current physics reasoning benchmarks neglect critical intermediate steps of variable identification and process formulation.
Approach:	a new benchmark evaluates multimodal large language models in physics reasoning . the benchmark measures variables, process formulations, and solution derivation .
Outcome:	PhysicsArena is the first multimodal physics reasoning benchmark . it evaluates MLLMs across three critical dimensions: variable identification, process formulation, and solution derivation.

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Yongil Kim, Heuiyeen Yeen, Hyeongu Yun, Jinsik Lee

Choosing a Model, Shaping a Future: Comparing LLM Perspectives on Sustainability and its Relationship with AI (2025.findings-emnlp)

Challenge:	Existing benchmarks for long-context RAG focus primarily on English . low-resource languages lack comprehensive evaluation frameworks limiting their progress in retrieval-based tasks.
Approach:	Ko-LongRAG is the first Korean long-context RAG benchmark . it adopts a retrieval-free approach designed around Specialized Content Knowledge (SCK) o1 model achieves the highest performance among proprietary models, while EXAONE 3.5 leads among open-sourced models .
Outcome:	the benchmark is based on a Korean language model with a retrieval-free approach . o1 model achieves the highest performance among proprietary models, while EXAONE 3.5 leads among open-sourced models.

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Annika Bush, Meltem Aksoy, Markus Pauly, Greta Ontrup

Optimising Factual Consistency in Summarisation via Preference Learning from Multiple Imperfect Metrics (2025.findings-emnlp)

Challenge:	Claude, DeepSeek, GPT, LLaMA, and Mistral conceptualize sustainability and its relationship with AI.
Approach:	They investigated how five state-of-the-art LLMs conceptualize sustainability and its relationship with AI.
Outcome:	The proposed models differed significantly between models, revealing significant biases and biase . Claude, DeepSeek, GPT, LLaMA, and Mistral conceptualize sustainability and its relationship with AI.

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Yuxuan Ye, Raul Santos-Rodriguez, Edwin Simpson

Judging with Many Minds: Do More Perspectives Mean Less Prejudice? On Bias Amplification and Resistance in Multi-Agent Based LLM-as-Judge (2025.findings-emnlp)

Challenge:	Existing evaluation metrics are unreliable for factual consistency tasks, limiting their effectiveness as signals for shaping model behaviour.
Approach:	They propose an automated training pipeline that improves factual consistency in summaries by aggregating scores from different weak metrics.
Outcome:	The proposed approach improves factual consistency in summaries by aggregating scores from weak metrics.

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Chiyu Ma, Enpei Zhang, Yilun Zhao, Wenjun Liu, Yaning Jia, Peijun Qing, Lin Shi, Arman Cohan, Yujun Yan, Soroush Vosoughi

Investigating the Impact of Conceptual Metaphors on LLM-based NLI through Shapley Interactions (2025.findings-emnlp)

Challenge:	LLM-as-Judge frameworks provide scalable alternative to human evaluation . but the question of how intrinsic biases manifest in these settings remains unexplored .
Approach:	They conduct systematic analysis of four bias types in multi-agent LLM-as-Judge frameworks . they find debate framework amplifies biases sharply after initial debate .
Outcome:	The proposed frameworks amplify biases after debate and show they are stronger in meta-judge scenarios.

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Meghdut Sengupta, Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier, Debanjan Ghosh, Henning Wachsmuth

Challenge:	Metaphorical language is prevalent in everyday communication, often used unconsciously, as in "rising crime" incorporating conceptual metaphors improves performance on downstream tasks that implicitly require correct metaphor interpretation, such as NLI.
Approach:	They extend metaphorical texts in an existing NLI dataset by source and target domains and conduct an ablation study using Shapley values and interactions to assess the extent to which LLMs interpret metaphorical language correctly in NLI.
Outcome:	The proposed method improves performance on natural language inference tasks with metaphoric input.

KurTail : Kurtosis-based LLM Quantization (2025.findings-emnlp)

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Mohammad Sadegh Akhondzadeh, Aleksandar Bojchevski, Evangelos Eleftheriou, Martino Dazzi

VIVA+: Human-Centered Situational Decision-Making (2025.findings-emnlp)

Challenge:	Outliers in quantization of large language models make uniform quantization schemes less effective . a new PTQ scheme that leverages Kurtosis-based rotation mitigates outliers .
Approach:	They propose a new post-training quantization scheme that leverages Kurtosis-based rotation to mitigate outliers in the activations of large language models.
Outcome:	The proposed method outperforms existing quantization methods with 13.3% boost in MMLU accuracy and 15.5% boost in Wiki perplexity.

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Zhe Hu, Yixiao Ren, Guanzhong Liu, Jing Li, Yu Yin

QuantAgents: Towards Multi-agent Financial System via Simulated Trading (2025.findings-emnlp)

Challenge:	Multimodal Large Language Models (MLLMs) show promising results in complex, human-centered environments, yet evaluating their capacity for nuanced, humanlike reasoning and decision-making remains challenging.
Approach:	They introduce VIVA+, a cognitively grounded benchmark for evaluating the reasoning and decision-making of MLLMs in human-centered situations.
Outcome:	The VIVA+ model is based on 1,317 real-world situations paired with 6,373 multiple-choice questions . it consists of three core abilities for decision-making: (1) Foundational Situation Comprehension, (2) Context-Driven Action Justification, and (3) Reflective Reasoning.

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Xiangyu Li, Yawen Zeng, Xiaofen Xing, Jin Xu, Xiangmin Xu

Challenge:	Existing LLM-based agent models exhibit significant deviations from real-world fund companies.
Approach:	They propose a multi-agent financial system that incorporates simulated trading . they propose simulated trades are evaluated without assuming actual risks .
Outcome:	The proposed system evaluates various investment strategies without assuming actual risks without involving real-world investors.

LLMs Reproduce Stereotypes of Sexual and Gender Minorities (2025.findings-emnlp)

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Ruby Ostrow, Adam Lopez

Accept or Deny? Evaluating LLM Fairness and Performance in Loan Approval across Table-to-Text Serialization Approaches (2025.findings-emnlp)

Challenge:	a large body of research has found substantial gender bias in NLP systems . authors show that LLMs generate stereotyped representations of sexual and gender minorities in this setting .
Approach:	They propose to use a stereotype content model to study gender bias in large language models . they show that LLMs generate stereotyped representations of sexual and gender minorities .
Outcome:	The proposed model generates negative stereotypes of sexual and gender minorities in English-language surveys .

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Israel Abebe Azime, Deborah D. Kanubala, Tejumade Afonja, Mario Fritz, Isabel Valera, Dietrich Klakow, Philipp Slusallek

Transfer-Aware Data Selection for Domain Adaptation in Text Retrieval (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) are increasingly employed in high-stakes decision-making tasks such as loan approvals.
Approach:	They evaluate the performance and fairness of LLMs on serialized loan approval datasets from Ghana, Germany, and the United States.
Outcome:	The model’s zero-shot and in-context learning (ICL) capabilities are evaluated on loan approval datasets from Ghana, Germany, and the United States.

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Linzhu Yu, Huan Li, Ke Chen, Lidan Shou

Understanding and Improving Information Preservation in Prompt Compression for LLMs (2025.findings-emnlp)

Challenge:	Existing methods to improve domain adaptation do not guarantee improved adaptability, but may negatively impact model performance.
Approach:	They propose a framework that can effectively improve model adaptability by selecting beneficial data without evaluating all source data.
Outcome:	The proposed framework improves model adaptability by selecting beneficial data without evaluating all source data.

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Weronika Łajewska, Momchil Hardalov, Laura Aina, Neha Anna John, Hang Su, Lluis Marquez

A Benchmark for Hindi Verb-Argument Structure Alternations (2025.findings-emnlp)

Challenge:	Recent advances in large language models have enabled their successful application to a broad range of tasks.
Approach:	They propose a framework that allows for in-depth analysis of prompt compression methods.
Outcome:	The proposed framework analyzes state-of-the-art soft and hard compression methods . it shows that some fail to preserve key details from the original prompt, limiting performance on complex tasks.

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Kanishka Jain, Ashwini Vaidya

Beyond Binary Preferences: Semi-Online Label-Free GRACE-KTO with Group-Wise Adaptive Calibration for High-Quality Long-Text Generation (2025.findings-emnlp)

Challenge:	Pretrained large language models (LLMs) can infer the frame-selectional properties of Hindi verbs.
Approach:	They propose a benchmark to test whether pretrained large language models can infer the frame-selectional properties of Hindi verbs.
Outcome:	The proposed model performs best when masked monolingual models fare poorly when compared to causal models.

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Jingyang Deng, Ran Chen, Jo-Ku Cheng, Jinwen Ma

Representation-based Broad Hallucination Detectors Fail to Generalize Out of Distribution (2025.findings-emnlp)

Challenge:	Generating high-quality long-text remains challenging for Large Language Models (LLMs), as conventional supervised fine-tuning fails to ensure overall quality due to its teacher-forcing nature.
Approach:	They propose a semi-online framework that transforms KTO’s binary signals into dynamically calibrated intra-group rewards.
Outcome:	The proposed framework transforms binary signals into dynamically calibrated intra-group rewards.

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Zuzanna Dubanowska, Maciej Żelaszczyk, Michał Brzozowski, Paolo Mandica, Michal P. Karpowicz

MAFMO: Multi-modal Adaptive Fusion with Meta-template Optimization for Vision-Language Models (2025.findings-emnlp)

Challenge:	a spurious correlation between hallucination detection methods and data is limiting the current SOTA.
Approach:	They propose a set of guidelines for hallucination detection and its evaluation.
Outcome:	The proposed method performs no better than supervised linear probes on the RAGTruth dataset .

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Mingrui Xie, Lulu Xu, Junliang Du

Multimodal UNcommonsense: From Odd to Ordinary and Ordinary to Odd (2025.findings-emnlp)

Challenge:	Existing approaches focus on single-modality adjustments, leading to suboptimal alignment and limited generalization.
Approach:	They propose a plug-and-play framework for visual recognition that integrates a Harmonic Cross-Modal Adapter and a Meta-Template Optimization module.
Outcome:	Extensive experiments across multiple fine-grained visual recognition benchmarks show that MAFMO consistently improves existing methods’ performance on both novel classes and harmonic mean while maintaining robustness under various challenging conditions with minimal computational overhead.

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Yejin Son, Saejin Kim, Dongjun Min, Youngjae Yu

Analyzing Gambling Addictions: A Spanish Corpus for Understanding Pathological Behavior (2025.findings-emnlp)

Challenge:	Multimodal UNcommonsense (MUN) is a benchmark designed to evaluate models’ ability to handle scenarios that deviate from typical visual or contextual expectations.
Approach:	They propose a retrieval-based in-context learning framework that transfers reasoning capabilities from larger models to smaller ones without additional training.
Outcome:	The proposed method improves on baseline ICL methods by 8.3% over previous methods.

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Manuel Couto, Marcos Fernández-Pichel, Mario Ezra Aragon, David E. Losada

Challenge:	a new study examines the interaction between natural language use and gambling disorders.
Approach:	They build a new corpus of sentences that are searched and compared using top-k pooling to form the assessment pools of sentences.
Outcome:	The proposed model is based on a new corpus of sentences in spanish .

Beyond Surface Alignment: Rebuilding LLMs Safety Mechanism via Probabilistically Ablating Refusal Direction (2025.findings-emnlp)

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Yuanbo Xie, Yingjie Zhang, Tianyun Liu, Duohe Ma, Tingwen Liu

Distributed LLM Serving on Consumer-Grade GPUs by Reconciling Computation and Communication (2025.findings-emnlp)

Challenge:	Jailbreak attacks pose persistent threats to large language models . current safety alignment methods have insufficient safety alignment depth and unrobust internal defense mechanisms.
Approach:	a new safety alignment framework is developed to overcome jailbreak attacks . the framework forces the model to dynamically rebuild its refusal mechanisms from jailbreak states .
Outcome:	a new safety alignment framework reduces attack success rates by approximately 95% on four open-source LLM families and six representative attacks.

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Lewei Jin, Kui Zhang, Yongqi Chen, null Zhuoyifan, Renjie Li, Yi Gao, Bowei Yang, Zhengong Cai, Wei Dong

SafeToolBench: Pioneering a Prospective Benchmark to Evaluating Tool Utilization Safety in LLMs (2025.findings-emnlp)

Challenge:	Large language models are reshaping internet services, and serving them is costly.
Approach:	They propose an efficient distributed LLM serving system that splits prefill and decode requests into smaller chunks .
Outcome:	The proposed system reduces TTFT, TPOT, and latency compared to the state-of-the-art system.

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Hongfei Xia, Hongru Wang, Zeming Liu, Qian Yu, Yuhang Guo, Haifeng Wang

Sparsifying Mamba (2025.findings-emnlp)

Challenge:	Existing approaches fail to fully capture all risks in tool utilization, resulting in financial loss or privacy leaking.
Approach:	They propose a framework to assess the safety of LLM tool utilization in a prospective manner, covering malicious user instructions and diverse practical toolsets.
Outcome:	The proposed framework significantly enhances LLMs’ self-awareness, enabling a more safer and trustworthy tool utilization.

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An Wang, Ruobing Xie, Shuaipeng Li, Xingwu Sun, Zhanhui Kang

Beneath the Facade: Probing Safety Vulnerabilities in LLMs via Auto-Generated Jailbreak Prompts (2025.findings-emnlp)

Challenge:	Existing attempts to integrate sparsification with Mamba fail to leverage Mamba's internal structure for fine-grained sparsifying.
Approach:	They propose to use Mamba to integrate sparsification into Mamba and propose a flexible and effective mechanism for parameter scalability.
Outcome:	The proposed framework can independently achieve parameter scalability and has stronger performance.

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Heehyeon Kim, Kyeongryul Lee, Joyce Jiyoung Whang

ET-MIER: Entity Type-guided Key Mention Identification and Evidence Retrieval for Document-level Relation Extraction (2025.findings-emnlp)

Challenge:	a gap exists in systematic assessment of real-world safety risks . a lack of evaluation frameworks to keep pace with the breadth and variability of real risk factors.
Approach:	They propose a framework that assesses security vulnerabilities by deriving scenario-driven jailbreak prompts using an adversarial agent.
Outcome:	The proposed framework detects vulnerabilities in scenario-driven jailbreak prompt generation . it supports plug-and-play jailbreak strategies and adapts seamlessly to multimodal settings.

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Xin Li, Huangming Xu, Fu Zhang, Jingwei Cheng

Position IDs Matter: An Enhanced Position Layout for Efficient Context Compression in Large Language Models (2025.findings-emnlp)

Challenge:	Existing work does not fully distinguish the contribution of different mentions to entity representation and the importance of mentions in evidence sentences.
Approach:	They propose a document-level relation extraction task that uses entity mentions to identify relations between entities in a text.
Outcome:	The proposed model achieves state-of-the-art on widely-adopted datasets.

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Runsong Zhao, Xin Liu, Xinyu Liu, Pengcheng Huang, Chunyang Xiao, Tong Xiao, JingBo Zhu

Can Role Vectors Affect LLM Behaviour? (2025.findings-emnlp)

Challenge:	Existing methods to compress context information ignore holistic contextual dependencies.
Approach:	They propose a method that adjusts position encodings to minimize the distance between context tokens and special tokens.
Outcome:	Enhanced Position Layout (EPL) improves compression of context information in large language models.

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Daniele Potertì, Andrea Seveso, Fabio Mercorio

Semantic Component Analysis: Introducing Multi-Topic Distributions to Clustering-Based Topic Modeling (2025.findings-emnlp)

Challenge:	a recent study has shown that personas influence LLM performance, but their direct impact remains unclear.
Approach:	They propose a novel approach to guiding LLM behaviour through role vectors . they construct 29 role vector derived from model activations and evaluate their impact .
Outcome:	The proposed approach improves in-domain task performance while yielding unexpected gains.

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Florian Eichin, Carolin M. Schuster, Georg Groh, Michael A. Hedderich

ThinkQE: Query Expansion via an Evolving Thinking Process (2025.findings-emnlp)

Challenge:	Existing methods for topic modeling fail to scale to large datasets or assume one topic per document.
Approach:	They propose a topic modeling technique that discovers multiple topics per sample . they evaluate SCA on Twitter datasets in English, Hausa and Chinese .
Outcome:	The proposed technique outperforms the LLM-based TopicGPT on Twitter datasets with similar compute budgets.

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Yibin Lei, Tao Shen, Andrew Yates

Hierarchical Reward Modeling for Fault Localization in Large Code Repositories (2025.findings-emnlp)

Challenge:	LLM-based methods often generate narrowly focused expansions that overlook these desiderata.
Approach:	They propose a test-time query expansion framework that promotes exploration and result diversity . ThinkQE encourages deeper and comprehensive semantic exploration and a corpus-interaction strategy that iteratively refines expansions .
Outcome:	The proposed framework outperforms prior approaches on diverse web search benchmarks.

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Jiwei Zhang, Jianxun Lian, Haiming Qin, Mingyang Zhou, KeZhong Lu, Rui Mao, Hao Liao

Challenge:	Large Language Models (LLMs) have limited fault localization capabilities due to limited context length.
Approach:	They propose a hierarchical localization reward model to evaluate and select the most accurate fault localization candidates from the outputs of LLMs.
Outcome:	The proposed model improves the final line-level localization recall by 12% on the SWE-Bench-Lite dataset.

Layer Duplication in LLMs (2025.findings-emnlp)

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Neo Eyal, Nachum Dershowitz, Kfir Bar

Challenge:	Duplicating multihead self-attention layers once or twice yields significant performance boosts.
Approach:	They investigate the effect of duplicating multihead self-attention layers in large language models .
Outcome:	The proposed method improves performance across a range of language tasks with or without fine-tuning.

Semantic-Aware Action Space Compression via LLM-DRL Synergy for Efficient Task-oriented Dialogue Policy Exploration (2025.findings-emnlp)

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Yangyang Zhao, Ben Niu, Yuxuan Tan, Shihan Wang, Libo Qin

Linear Steerability in Language Models: When It Emerges and How It Evolves (2025.findings-emnlp)

Challenge:	Pre-trained large language models (LLMs) with world knowledge and semantic understanding are promising for task-oriented dialogue systems.
Approach:	a framework that synergizes pre-trained large language models with DRL is proposed . a lightweight action pruning mechanism is employed to eliminate implausible actions .
Outcome:	a new framework synergizes pre-trained large language models with DRL to guide decision-making . the proposed framework eliminates semantically implausible or low-potential actions from multi-turn dialogue context .

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Jianshu She, Xinyue Li, Eric P. Xing, Zhengzhong Liu, Qirong Ho

A Comprehensive Survey on Learning from Rewards for Large Language Models: Reward Models and Learning Strategies (2025.findings-emnlp)

Challenge:	a new framework for steering language models reveals how concepts become linearly separable as training progresses .
Approach:	They propose a framework to analyze steerability in language models by using hidden state and representation analysis.
Outcome:	The proposed framework reveals how steerability evolves over training . concepts become linearly separable as training progresses, the framework shows .

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Xiaobao Wu

InFact: Informativeness Alignment for Improved LLM Factuality (2025.findings-emnlp)

Challenge:	Recent developments in Large Language Models have shifted from pre-training to post-training and test-time scaling.
Approach:	They present a comprehensive overview of learning from rewards from the perspective of reward models and learning strategies across training, inference, and post-inference stages.
Outcome:	The proposed paradigm enables the transition from passive learning from static data to active learning from dynamic feedback.

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Roi Cohen, Russa Biswas, Gerard de Melo

Challenge:	despite factual errors, LLMs tend to generate factual text that is factually correct but less informative than other, more informative choices.
Approach:	They propose an objective that prioritizes answers that are both correct and informative .
Outcome:	a new mechanism prioritizes correct and informative answers based on factual benchmarks . the proposed model improves both accuracy and factuality by maximizing the objective .

Large Language Model Agents in Finance: A Survey Bridging Research, Practice, and Real-World Deployment (2025.findings-emnlp)

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Yifei Dong, Fengyi Wu, Kunlin Zhang, Yilong Dai, Sanjian Zhang, Wanghao Ye, Sihan Chen, Zhi-Qi Cheng

Problem Solved? Information Extraction Design Space for Layout-Rich Documents using LLMs (2025.findings-emnlp)

Challenge:	a systematic review of large language models (LLMs) is conducted to better align their capabilities with real-world demands.
Approach:	They propose a functional taxonomy mapping financial domains to tasks, datasets, and institutional constraints. they catalog over 30 financial benchmarks and 20 representative models.
Outcome:	The proposed model frameworks are bridging financial practice and LLM research.

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Gaye Colakoglu, Gürkan Solmaz, Jonathan Fürst

Generation-Augmented Retrieval: Rethinking the Role of Large Language Models in Zero-Shot Relation Extraction (2025.findings-emnlp)

Challenge:	a new open-source layout-aware IE test suite is available for download at https://github.com/gayecolakoglu/layIE-LLM.
Approach:	They propose an open-source layout-aware IE test suite that provides a layout-based IE pipeline.
Outcome:	The proposed method achieves 13.3–37.5 F1 points more than a baseline configuration using the same LLM.

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Zehan Li, Fu Zhang, Tianyue Peng, He Liu, Jingwei Cheng

Following Occam’s Razor: Dynamic Combination of Structured Knowledge for Multi-Hop Question Answering using LLMs (2025.findings-emnlp)

Challenge:	Recent advances in Relation Extraction (RE) emphasize Zero-Shot methodologies, aiming to recognize unseen relations between entities with no annotated data.
Approach:	They propose a plug-in retrieval adjuster that allows rapid fine-tuning without accessing LLMs’ parameters.
Outcome:	The proposed model demonstrates comparable performance on multiple benchmarks.

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Wei Chen, Zhi Zheng, Lili Zhao, Huijun Hou, Tong Xu

Large Language Models as Reader for Bias Detection (2025.findings-emnlp)

Challenge:	Multi-hop question answering is a challenging task that requires capturing information from multiple positions in multiple documents.
Approach:	They propose a framework for integrating text-based and triple-based paradigms that incorporates structured knowledge into large-scale question answering.
Outcome:	The proposed framework improves multi-hop question answering by incorporating structured knowledge into the models.

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Xuan Luo, Jing Li, Zhong Wenzhong, Geng Tu, Ruifeng Xu

LOHRec: Leveraging Order and Hierarchy in Generative Sequential Recommendation (2025.findings-emnlp)

Challenge:	Traditional methods analyze text from the writer’s perspective, leaving the reader’s viewpoint underexplored.
Approach:	They investigate whether large language models can be leveraged as readers for bias detection by generating reader-perspective comments.
Outcome:	The proposed model performs comparable to GPT4's in detecting bias in media content.

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Jiawen Xie, Haiyang Wu, Deyi Ji, Yuekui Yang, Shaoping Ma

Challenge:	generative recommenders focus on maximizing the prediction probability of the next item in the temporal sequence, ignoring diverse potential items.
Approach:	They propose a learning framework that leverages order and hierarchy in generative recommendation using quantized identifiers to further explore performance ceiling of lightweight generative recommenders.
Outcome:	The proposed learning framework outperforms strong prior baselines across multiple datasets.

Biology-Instructions: A Dataset and Benchmark for Multi-Omics Sequence Understanding Capability of Large Language Models (2025.findings-emnlp)

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Haonan He, Yuchen Ren, Yining Tang, Ziyang Xu, Junxian Li, Minghao Yang, Di Zhang, Yuan Dong, Tao Chen, Shufei Zhang, Yuqiang Li, Nanqing Dong, Wanli Ouyang, Dongzhan Zhou, Peng Ye

AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science (2025.findings-emnlp)

Challenge:	Biology-Instructions is the first large-scale instruction-tuning dataset for multi-omics biological sequences.
Approach:	They propose a large-scale instruction-tuning dataset for multi-omics biological sequences . they propose 'chatMultiOmics' to overcome limitations of current LLMs on multi-ome tasks .
Outcome:	The proposed dataset bridges LLMs and complex biological sequence-related tasks while maintaining conversational fluency.

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An Luo, Xun Xian, Jin Du, Fangqiao Tian, Ganghua Wang, Ming Zhong, Shengchun Zhao, Xuan Bi, Zirui Liu, Jiawei Zhou, Jayanth Srinivasa, Ashish Kundu, Charles Fleming, Mingyi Hong, Jie Ding

Are you sure? Measuring models bias in content moderation through uncertainty (2025.findings-emnlp)

Challenge:	Large language models (LLMs) have advanced the automation of data science workflows, yet it remains unclear whether they can critically leverage external domain knowledge as human data scientists do in practice.
Approach:	They propose a benchmark to evaluate how large language models handle external domain knowledge in tabular prediction tasks.
Outcome:	The proposed model evaluates whether it can critically leverage external domain knowledge as human data scientists do in practice.

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Alessandra Urbinati, Mirko Lai, Simona Frenda, Marco Stranisci

FOSSIL: Harnessing Feedback on Suboptimal Samples for Data-Efficient Generalisation with Imitation Learning for Embodied Vision-and-Language Tasks (2025.findings-emnlp)

Challenge:	Language Model-based classifiers perpetuate racial and social biases in content moderation . et al., j. n. d., and j neil, e. c. (2005) measure the fairness of content moderated models .
Approach:	They propose an unsupervised approach that benchmarks models on their uncertainty . they use uncertainty as a proxy to analyze the bias of 11 models against women and non-whites .
Outcome:	The proposed method analyzes the bias of 11 models against women and non-white annotators . it shows that some pre-trained models predict with high accuracy the labels coming from minority groups .

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Sabrina McCallum, Amit Parekh, Alessandro Suglia

Assess and Prompt: A Generative RL Framework for Improving Engagement in Online Mental Health Communities (2025.findings-emnlp)

Challenge:	Current approaches to embodied AI tend to learn policies from expert demonstrations, but without a mechanism to evaluate the quality of demonstrated actions, they are limited to learning from optimal behaviour or risk replicating errors and inefficiencies.
Approach:	They propose to embed language feedback into a Transformer-based policy and optionally complement the traditional next action prediction objective with auxiliary self-supervised learning objectives for feedback prediction.
Outcome:	The proposed method improves agents’ compositional generalisation abilities and robustness on a range of embodied Vision-and-Language tasks in a custom babyAI-XGen environment.

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Bhagesh Gaur, Karan Gupta, Aseem Srivastava, Manish Gupta, Md Shad Akhtar

Logic: Long-form Outline Generation via Imitative and Critical Self-refinement (2025.findings-emnlp)

Challenge:	Empirical results across four notable language models demonstrate significant improvements in attribute elicitation and user engagement.
Approach:	They propose a framework that identifies and prompts users to enrich their posts by eliciting missing support attributes.
Outcome:	The proposed framework improves engagement and elicits missing information from posts.

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Hengwei Liu, Yongliang Shen, Zhe Zheng, Haoyuan Ma, Xingyu Wu, Yin Zhang, Weiming Lu

No Free Lunch: Retrieval-Augmented Generation Undermines Fairness in LLMs, Even for Vigilant Users (2025.findings-emnlp)

Challenge:	Existing methods for long-form outline generation have low knowledge density and lack detail . retrieval-augmented approaches struggle to maintain logical coherence across retrieved information .
Approach:	They propose a system that mimics human writers' refinement process by mimicking outlines through imitation and critical self-refinement.
Outcome:	The proposed system improves on the FreshWiki and WikiOutline datasets and establishes a coherent planning framework and structured knowledge base.

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Mengxuan Hu, Hongyi Wu, Ronghang Zhu, Zihan Guan, Dongliang Guo, Daiqing Qi, Sheng Li

LegoSLM: Connecting LLM with Speech Encoder using CTC Posteriors (2025.findings-emnlp)

Challenge:	Retrieval-augmented generation is widely adopted for its effectiveness and cost-efficiency in mitigating hallucinations.
Approach:	They propose a practical three-level threat model from the perspective of user fairness awareness.
Outcome:	The proposed model shows that RAG can undermine fairness alignment without fine-tuning or retraining.

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Rao Ma, Tongzhou Chen, Kartik Audhkhasi, Bhuvana Ramabhadran

Skeleton-Guided-Translation: A Benchmarking Framework for Code Repository Translation with Fine-Grained Quality Evaluation (2025.findings-emnlp)

Challenge:	Recent studies show that pre-trained speech encoders and large language models can perform suboptimal performance on a range of spoken language processing tasks.
Approach:	They propose to combine large-scale pre-trained speech encoders and large-language models for better performance on automatic speech recognition tasks.
Outcome:	The proposed model can get an average of 49% WER reduction over the baseline model on 8 MLS testsets.

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Xing Zhang, Jiaheng Wen, Fangkai Yang, Yu Kang, Pu Zhao, Junhao Wang, Maoquan Wang, Yufan Huang, Shengyu Fu, Elsie Nallipogu, Qingwei Lin, Yingnong Dang, Saravan Rajmohan, Dongmei Zhang

Parallel Communities Across the Surface Web and the Dark Web (2025.findings-emnlp)

Challenge:	Existing code translation benchmarks focus on individual functions, overlooking repository-level challenges like intermodule coherence and dependency management.
Approach:	They propose a framework for benchmarking Java-to-C# translation at the repository level . it uses a translation framework guided by skeletons and fine-grained quality evaluation .
Outcome:	The proposed framework improves Java-to-C# translation quality at the repository level.

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Wenchao Dong, Megha Sundriyal, Seongchan Park, Jaehong Kim, Meeyoung Cha, Tanmoy Chakraborty, Wonjae Lee

Challenge:	Sense of Community is a social motivation that is reflected in the social behavior of humans.
Approach:	They compile a large collection of parallel community datasets comprising over 7 million posts and comments from Reddit and 200,000 posts and comment from Dread, a dark web discussion forum, covering similar topics.
Outcome:	The results show that users on Reddit exhibit a stronger sense of community membership despite the dark web’s restricted accessibility.

Lemma Dilemma: On Lemma Generation Without Domain- or Language-Specific Training Data (2025.findings-emnlp)

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Olia Toporkov, Alan Akbik, Rodrigo Agerri

LlmFixer: Fix the Helpfulness of Defensive Large Language Models (2025.findings-emnlp)

Challenge:	Large language models (LLMs) can generate lemmas in context without prior fine-tuning.
Approach:	They compare in-context lemma generation with traditional fully supervised approaches . they use encoder-only supervised methods and cross-lingual methods .
Outcome:	The proposed model outperforms the traditional fully supervised approach in the context of lemmatization tasks.

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Zelong Yu, Xiaoming Zhang, Litian Zhang, Yu Yuan, Chaozhuo Li

Universal Acoustic Adversarial Attacks for Flexible Control of Speech-LLMs (2025.findings-emnlp)

Challenge:	Several defense strategies have been introduced to defend against jailbreak attacks, but these strategies weakened the usefulness of large language models.
Approach:	They propose a framework that acts on large language models equipped with any defense strategy to recover their usefulness.
Outcome:	The proposed framework can be used on large language models to recover their usefulness without updating the parameters of a defensive large language model.

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Rao Ma, Mengjie Qian, Vyas Raina, Mark Gales, Kate Knill

Probing Semantic Routing in Large Mixture-of-Expert Models (2025.findings-emnlp)

Challenge:	acoustic adversarial attacks on speech LLMs may make them more vulnerable to adversarials . flexible speech encoders and large language models have enabled speech Llms to handle a wide range of processing tasks.
Approach:	They investigate universal adversarial attacks on speech LLMs by pre-trained speech encoders and large language models.
Outcome:	The proposed model can handle a wide range of spoken language processing tasks.

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Matthew Lyle Olson, Neale Ratzlaff, Musashi Hinck, Man Luo, Sungduk Yu, Chendi Xue, Vasudev Lal

CMT-Eval: A Novel Chinese Multi-turn Dialogue Evaluation Dataset Addressing Real-world Conversational Challenges (2025.findings-emnlp)

Challenge:	large mixture-of-expert models have become increasingly common in the open domain . prior work has explored functional differentiation through routing behavior .
Approach:	They investigate whether expert routing in large mixture-of-expert models is influenced by the semantics of the inputs.
Outcome:	The results show that expert routing is influenced by the semantics of the inputs.

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Siyu Tian, Kaijie Mo, Yupei Wang, Renfen Hu

LastingBench: Defend Benchmarks Against Knowledge Leakage (2025.findings-emnlp)

Challenge:	Existing evaluation benchmarks fail to capture users’ evolving needs and how their diverse conversation styles affect the dialogue flow.
Approach:	They propose to use CMT-Eval to evaluate Chinese multi-turn dialogue systems.
Outcome:	The proposed dataset is the first dedicated dataset for fine-grained evaluation of Chinese multi-turn dialogue systems.

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Yixiong Fang, Tianran Sun, Yuling Shi, Min Wang, Xiaodong Gu

Learning API Functionality from In-Context Demonstrations for Tool-based Agents (2025.findings-emnlp)

Challenge:	Existing methods to detect and safeguard LLMs against knowledge leakage fail to address the long-term challenge of mitigating it.
Approach:	They propose a method to reinforce and safeguard existing benchmarks against knowledge leakage by perturbation-based detection and counterfactual rewriting to disrupt memorization while preserving original intent.
Outcome:	The proposed method reduces memorization effects in long-context QA benchmarks, providing a more accurate assessment of model reasoning and generalization abilities.

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Bhrij Patel, Ashish Jagmohan, Aditya Vempaty

Challenge:	Documentation is often missing, outdated, privatized, or inconsistent in tool-based agents.
Approach:	They propose a task that allows agents to learn API functionality directly from in-context demonstrations.
Outcome:	The proposed model improves the task success rate of an API-based agent by providing explicit function calls and natural language critiques.

Predicting Language Models’ Success at Zero-Shot Probabilistic Prediction (2025.findings-emnlp)

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Kevin Ren, Santiago Cortes-Gomez, Carlos Miguel Patiño, Ananya Joshi, Ruiqi Lyu, Jingjing Tang, Alistair Turcan, Khurram Yamin, Steven Wu, Bryan Wilder

GAMIC: Graph-Aligned Molecular In-context Learning for Molecule Analysis via LLMs (2025.findings-emnlp)

Challenge:	Recent work has investigated the capabilities of large language models (LLMs) as zero-shot models for generating individual-level characteristics.
Approach:	They conduct a large-scale empirical study of large language models’ zero-shot predictive capabilities across a wide range of tabular prediction tasks.
Outcome:	The results show that LLMs perform well on the base prediction task, and when they perform well, they are more likely to provide high-quality predictions.

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Ali Al Lawati, Jason S Lucas, Zhiwei Zhang, Prasenjit Mitra, Suhang Wang

Rethinking Sign Language Translation: The Impact of Signer Dependence on Model Evaluation (2025.findings-emnlp)

Challenge:	Current methods for retrieving large language models rely on molecule feature similarity, such as Morgan fingerprints, which do not adequately capture the global molecular and atom-binding relationships.
Approach:	They propose a self-supervised learning technique that embeds demonstration examples into the input prompt.
Outcome:	The proposed technique outperforms simple Morgan-based retrieval methods across tasks by up to 45%.

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Keren Artiaga, Sabyasachi Kamila, Haithem Afli, Conor Lynch, Mohammed Hasanuzzaman

Can Large Language Models Identify Implicit Suicidal Ideation? An Empirical Evaluation (2025.findings-emnlp)

Challenge:	Sign Language Translation evaluations remain largely signer-dependent, with overlapping signers across train/dev/test.
Approach:	We conduct signer-fold cross-validation on three leading SLT models . they find that under signer independent evaluation performance drops sharply .
Outcome:	a signer-dependent evaluation can substantially overestimate SLT capability, the authors say . they recommend adopting signer independent protocols to ensure generalisation to unseen signers .

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Tong Li, Shu Yang, Junchao Wu, Jiyao Wei, Lijie Hu, Mengdi Li, Derek F. Wong, Joshua R. Oltmanns, Di Wang

Adaptive Platt Scaling with Causal Interpretations for Self-Reflective Language Model Uncertainty Estimates (2025.findings-emnlp)

Challenge:	Existing data on suicidal ideation in private conversations are limited . a new dataset of 1,200 test cases is presented to address this gap .
Approach:	They propose a dataset of 1,200 test cases simulating implicit suicidal ideation in private contexts.
Outcome:	The proposed dataset includes 1,200 test cases simulating implicit suicidal ideation in dialogue scenarios.

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Anthony Sicilia, Malihe Alikhani

Treble Counterfactual VLMs: A Causal Approach to Hallucination (2025.findings-emnlp)

Challenge:	a new study examines the ability of large language models to self-monitor and ask for human intervention.
Approach:	They propose a formal analysis of LLM self-reflection for uncertainty estimation using domain adaptation theory.
Outcome:	The proposed method improves accuracy and human interpretation on reasoning tasks.

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Li Li, Jiashu Qu, Linxin Song, Yuxiao Zhou, Yuehan Qin, Tiankai Yang, Yue Zhao

Video-Skill-CoT: Skill-based Chain-of-Thoughts for Domain-Adaptive Video Reasoning (2025.findings-emnlp)

Challenge:	Existing studies link hallucination to data or representation biases, but their causal origins remain unclear.
Approach:	They propose a causal framework to analyze and mitigate hallucination in vision-language models by using counterfactual analysis to estimate the Natural Direct Effect (NDE) of each modality and their interaction.
Outcome:	The proposed framework significantly reduces hallucination while preserving task performance while retaining reliability.

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Daeun Lee, Jaehong Yoon, Jaemin Cho, Mohit Bansal

Glitter: A Multi-Sentence, Multi-Reference Benchmark for Gender-Fair German Machine Translation (2025.findings-emnlp)

Challenge:	Existing methods for chain-of-thought reasoning fail to adapt to domain-specific skills over video content.
Approach:	They propose a framework that automatically constructs and leverages skill-aware CoT supervisions for domain-adaptive video reasoning.
Outcome:	The proposed framework outperforms strong baselines on three video understanding benchmarks.

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A Pranav, Janiça Hackenbuchner, Giuseppe Attanasio, Manuel Lardelli, Anne Lauscher

From n-gram to Attention: How Model Architectures Learn and Propagate Bias in Language Modeling (2025.findings-emnlp)

Challenge:	Existing MT models are limited in size and often consist of single sentences or single gender-fair formulation types.
Approach:	They propose a benchmark for machine translation that features extended passages with professional translations implementing gender-fair alternatives: neutral rewording, typographical solutions and neologistic forms.
Outcome:	The proposed benchmark features extended passages with professional translations implementing three gender-fair alternatives: neutral rewording, typographical solutions (gender star), and neologistic forms (-ens forms).

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Mohsinul Kabir, Tasfia Tahsin, Sophia Ananiadou

SENTRA: Selected-Next-Token Transformer for LLM Text Detection (2025.findings-emnlp)

Challenge:	Current research on bias in language models focuses on data quality, not temporal influences of data.
Approach:	They propose a methodology to interpret the interaction between training data and model architecture in bias propagation during language modeling.
Outcome:	The proposed method analyzes the interaction between training data and model architecture in bias propagation during language modeling.

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Mitchell Plyler, Yilun Zhang, Alexander Tuzhilin, Saoud Khalifah, Sen Tian

Automate Strategy Finding with LLM in Quant Investment (2025.findings-emnlp)

Challenge:	SENTRA is a general-purpose, supervised LLM text detector . it detects LLM-generated text that is not explicitly declared as such .
Approach:	They propose a general-purpose LLM text detector that detects unlabeled text . they use a transformer-based encoder that leverages selected-next-token sequences .
Outcome:	The proposed classifier outperforms baselines on 24 domains of text.

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Zhizhuo Kou, Holam Yu, Junyu Luo, Jingshu Peng, Xujia Li, Chengzhong Liu, Juntao Dai, Lei Chen, Sirui Han, Yike Guo

Does Reasoning Introduce Bias? A Study of Social Bias Evaluation and Mitigation in LLM Reasoning (2025.findings-emnlp)

Challenge:	Experimental results demonstrate robust performance of the strategy in Chinese & US market regimes compared to established benchmarks.
Approach:	They propose a framework leveraging Large Language Models within a risk-aware multi-agent system for automate strategy finding in quantitative finance.
Outcome:	The proposed framework outperforms all benchmarks in Chinese & US market regimes with 53.17% cumulative return on SSE50.

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Xuyang Wu, Jinming Nian, Ting-Ruen Wei, Zhiqiang Tao, Hsin-Tai Wu, Yi Fang

MT-RewardTree: A Comprehensive Framework for Advancing LLM-Based Machine Translation via Reward Modeling (2025.findings-emnlp)

Challenge:	Recent advances in large language models have enabled automatic generation of chain-of-thought reasoning . however, when reasoning steps reflect social stereotypes, they can reinforce harmful associations and lead to misleading conclusions.
Approach:	They propose a method that detects how model predictions change across incremental reasoning steps.
Outcome:	The proposed method outperforms a stereotype-free baseline and improves accuracy.

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Zhaopeng Feng, Jiahan Ren, Jiayuan Su, Jiamei Zheng, Hongwei Wang, Zuozhu Liu

Bias after Prompting: Persistent Discrimination in Large Language Models (2025.findings-emnlp)

Challenge:	MT-RewardTree provides a framework for constructing, evaluating, and deploying process reward models in machine translation (MT)
Approach:	They propose a method for automatically generating token-level preference pairs using approximate Monte Carlo Tree Search.
Outcome:	The proposed framework achieves state-of-the-art performance in token-level evaluation and sequence-level analysis.

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Nivedha Sivakumar, Natalie Mackraz, Samira Khorshidi, Krishna Patel, Barry-John Theobald, Luca Zappella, Nicholas Apostoloff

CARVQ: Corrective Adaptor with Group Residual Vector Quantization for LLM Embedding Compression (2025.findings-emnlp)

Challenge:	a dangerous assumption is that biases do not transfer from pre-trained large language models to adapted models.
Approach:	They validate the bias transfer hypothesis by using prompt adaptations to study biases in causal models . they find that popular prompt-based mitigation methods do not consistently prevent biase transferring .
Outcome:	The results invalidate the assumption that biases do not transfer from pre-trained models to adapted models.

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Dayin Gou, Sanghyun Byun, Nilesh Malpeddi, Gabrielle De Micheli, Prathamesh Vaste, Jacob Song, Woo Seong Chung

Consistent Discourse-level Temporal Relation Extraction Using Large Language Models (2025.findings-emnlp)

Challenge:	Large Language Models typically rely on a large number of parameters for token embedding, leading to substantial storage requirements and memory footprints.
Approach:	They propose a corrective Adaptor with group Residual Vector Quantization that can be used to compress the embedding layer without requiring specialized hardware.
Outcome:	The proposed corrective adaptor can achieve lower average bitwidth-per-parameter while maintaining reasonable perplexity and accuracy compared to scalar quantization.

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Yi Fan, Michael Strube

MMPlanner: Zero-Shot Multimodal Procedural Planning with Chain-of-Thought Object State Reasoning (2025.findings-emnlp)

Challenge:	Recent advances in large language models (LLMs) have spurred research on temporal relation extraction tasks.
Approach:	They propose a framework to improve LLMs’ temporal relation extraction capabilities using context selection, prompts inspired by Allen’s interval algebra and reflection-based consistency learning.
Outcome:	The proposed framework improves LLMs’ extraction capabilities by focusing on context selection, prompts inspired by Allen’s interval algebra and reflection-based consistency learning.

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Afrina Tabassum, Bin Guo, Xiyao Ma, Hoda Eldardiry, Ismini Lourentzou

Internal states before wait modulate reasoning patterns (2025.findings-emnlp)

Challenge:	Existing approaches to multimodal planning use large language models to refine textual steps, but visual object-state alignment and systematic evaluation are underexplored.
Approach:	They propose a multimodal multimodal planning framework that uses a 'object-state reasoning chain-of-thought' system to model object-state transitions and generate accurate multimodal plans.
Outcome:	The proposed framework improves textual planning by +6.8% and cross-modal alignment by +11.9%.

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Dmitrii Troitskii, Koyena Pal, Chris Wendler, Callum Stuart McDougall

Sparsity May Be All You Need: Sparse Random Parameter Adaptation (2025.findings-emnlp)

Challenge:	Prior work has shown that a significant driver of performance in reasoning models is their ability to reason and self-correct.
Approach:	They address the question whether model’s latents preceding wait tokens contain relevant information for modulating the subsequent reasoning process.
Outcome:	The proposed model's latents preceding wait tokens contain relevant information for modulating the subsequent reasoning process.

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Jesus Rios, Pierre Dognin, Ronny Luss, Karthikeyan Natesan Ramamurthy

Learning to Align: Addressing Character Frequency Distribution Shifts in Handwritten Text Recognition (2025.findings-emnlp)

Challenge:	Parameter-Efficient Fine-Tuning (PEFT) methods aim at reducing computational and memory resources for fine-tuning large language models.
Approach:	They propose to train on a small number of parameters instead of all model parameters . they compare the method to LoRA and find it to be efficient .
Outcome:	The proposed method is competitive with LoRA when using a similar number of trainable parameters.

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Panagiotis Kaliosis, John Pavlopoulos

Challenge:	Character sets change over time and character frequency distributions shift across historical periods or regions . character distribution alignment can improve existing models at inference time without requiring retraining .
Approach:	They propose a loss function that incorporates the Wasserstein distance between predicted and target distributions.
Outcome:	The proposed method improves accuracy and robustness under temporal and contextual shifts.

MT-R1-Zero: Advancing LLM-based Machine Translation via R1-Zero-like Reinforcement Learning (2025.findings-emnlp)

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Zhaopeng Feng, Shaosheng Cao, Jiahan Ren, Jiayuan Su, Ruizhe Chen, Yan Zhang, Jian Wu, Zuozhu Liu

Discrete Minds in a Continuous World: Do Language Models Know Time Passes? (2025.findings-emnlp)

Challenge:	Large-scale reinforcement learning (RL) methods have proven effective in enhancing the reasoning abilities of large language models.
Approach:	They propose an open-source adaptation of the R1-Zero RL framework for machine translation (MT) their code is available at https://github.com/fzp0424/MT-R1-zero.
Outcome:	The proposed framework surpasses towerinstruct-7B-v0.2 on the english-chinese benchmark by 1.26 points.

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Minghan Wang, Ye Bai, Thuy-Trang Vu, Ehsan Shareghi, Gholamreza Haffari

DLTKG: Denoising Logic-based Temporal Knowledge Graph Reasoning (2025.findings-emnlp)

Challenge:	Large Language Models excel at temporal reasoning tasks, but their ability to perceive the passage of time remains unexplored.
Approach:	They propose a Token-Time Hypothesis to test whether LLMs perceive the passage of time . they also propose an interactive navigation challenge to examine how LLM responds to time pressure .
Outcome:	The proposed model can map discrete token counts to wall-clock time and validate this through a dialogue duration judgment task.

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Xiaoke Wang, Fu Zhang, Jingwei Cheng, Yiwen Chi, Jiashun Peng, Yingsong Ning

Knowledge Representation

Challenge:	Current approaches to temporal knowledge representation face limited generalization to unseen facts and insufficient interpretability of reasoning processes.
Approach:	They propose a framework that uses a denoising diffusion process to complete reasoning tasks . they propose introducing a noise source and historical conditionguiding mechanism to improve interpretability .
Outcome:	The proposed framework outperforms state-of-the-art methods on three benchmark datasets.

EMO-RL: Emotion-Rule-Based Reinforcement Learning Enhanced Audio-Language Model for Generalized Speech Emotion Recognition (2025.findings-emnlp)

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Pengcheng Li, Botao Zhao, Zuheng Kang, Junqing Peng, Xiaoyang Qu, Yayun He, Jianzong Wang

MANTA: A Scalable Pipeline for Transmuting Massive Web Corpora into Instruction Datasets (2025.findings-emnlp)

Challenge:	Recent advances in reinforcement learning (RL) have shown promise in improving LALMs’ reasoning abilities, but their performance in affective computing tasks remains suboptimal.
Approach:	They propose a framework incorporating reinforcement learning with two key innovations: Emotion Similarity-Weighted Reward (ESWR) and Explicit Structured Reasoning (ESR).
Outcome:	The proposed framework improves LALMs' reasoning abilities on MELD and IEMOCAP datasets and shows strong generalization.

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Heuiyeen Yeen, Seokhee Hong, Hyeongu Yun, Jinsik Lee

Fast Quiet-STaR: Thinking Without Thought Tokens (2025.findings-emnlp)

Challenge:	MANTA-1M generates high-quality large-scale instruction fine-tuning datasets from web corpora . scalability and diversity of the datasets are preserved, allowing expansion into domains requiring intensive knowledge.
Approach:	a team of researchers introduce a pipeline that fine-tunes large-scale instruction datasets from web corpora with minimal human intervention.
Outcome:	MANTA generates high-quality large-scale instruction fine-tuning datasets from web corpora . leveraging high-performance LLMs, MANTE outperforms other methods in knowledge-intensive tasks .

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Wei Huang, Yizhe Xiong, Xin Ye, Zhijie Deng, Hui Chen, Zijia Lin, Guiguang Ding

Lock on Target! Precision Unlearning via Directional Control (2025.findings-emnlp)

Challenge:	Large Language Models have achieved impressive performance across a range of tasks, but further gains require more than scaling up model sizes or training data.
Approach:	They propose a method that gradually reduces the number of thought tokens . this method allows models to internalize more abstract reasoning processes .
Outcome:	The proposed framework preserves the benefits of token-level reasoning while reducing computational cost.

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Yuntao Wen, Ruixiang Feng, Feng Guo, Yifan Wang, Ran Le, Yang Song, Shen Gao, Shuo Shang

Challenge:	Existing methods for unlearning harmful, sensitive, or outdated knowledge suffer from two critical limitations: (1) collateral forgetting, where erasing target data inadvertently removes related but desirable knowledge, and (2) generality forgetting degrades the model’s general capabilities.
Approach:	They propose a method that identifies and leverages a targeted "unlearning direction" in the model's parameter space and selectively updates along this direction.
Outcome:	Experiments show that the proposed method achieves state-of-the-art unlearning precision while preserving both related knowledge and general capabilities.

UniRAG: A Unified RAG Framework for Knowledge-Intensive Queries with Decomposition, Break-Down Reasoning, and Iterative Rewriting (2025.findings-emnlp)

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Gun Il Kim, Jong Wook Kim, Beakcheol Jang

One Shot Dominance: Knowledge Poisoning Attack on Retrieval-Augmented Generation Systems (2025.findings-emnlp)

Challenge:	Existing retrieval-augmented generation approaches struggle with query complexity, propagated reasoning errors, or rely on incomplete or noisy retrieval.
Approach:	a unified retrieval-augmented generation framework is developed to address query complexity . the framework decomposes queries into semantically coherent sub-queries . it explicitly verifies retrieved sub-facts and adaptively refines queries based on identified knowledge gaps.
Outcome:	a new framework improves answer completeness and reliability by decomposing queries into coherent sub-queries . the framework explicitly verifies retrieved sub-facts and adaptively refines queries based on identified knowledge gaps.

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Zhiyuan Chang, Mingyang Li, Xiaojun Jia, Junjie Wang, Yuekai Huang, Ziyou Jiang, Yang Liu, Qing Wang

From Generic Empathy to Personalized Emotional Support: A Self-Evolution Framework for User Preference Alignment (2025.findings-emnlp)

Challenge:	Existing knowledge poisoning attacks against RAG systems require multiple poisoned documents or can only function effectively on simplistic queries.
Approach:	They propose a more realistic knowledge poisoning attack that poisons only a single document while remaining effective for complex multi-hop questions involving complex relationships between multiple elements.
Outcome:	The proposed attack achieves success by poisoning only a single document while remaining effective for complex multi-hop questions involving complex relationships between multiple elements.

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Jing Ye, Lu Xiang, Yaping Zhang, Chengqing Zong

Challenge:	Large Language Models (LLMs) show great potential for expressing empathy, but often deliver generic responses that fail to address users’ specific needs.
Approach:	They propose a self-evolution framework to help LLMs improve their responses to better align with users’ implicit preferences concerning personality, emotional state, and specific context.
Outcome:	The proposed model significantly improves the model's performance in emotional support, reducing unhelpful responses and minimizing discrepancies between user preferences and model outputs.

MaskCD: Mitigating LVLM Hallucinations by Image Head Masked Contrastive Decoding (2025.findings-emnlp)

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Jingyuan Deng, Yujiu Yang

ClusterUCB: Efficient Gradient-Based Data Selection for Targeted Fine-Tuning of LLMs (2025.findings-emnlp)

Challenge:	LVLMs have shown remarkable performance in visual-language understanding for downstream multimodal tasks.
Approach:	They propose a method to alleviate hallucinations by masking the “image heads” in LVLMs .
Outcome:	The proposed method alleviates the phenomenon of hallucinations and retains the general capabilities of LVLMs.

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Zige Wang, Qi Zhu, Fei Mi, Minghui Xu, Ruochun Jin, Wenjing Yang

TrapDoc: Deceiving LLM Users by Injecting Imperceptible Phantom Tokens into Documents (2025.findings-emnlp)

Challenge:	Gradient-based data influence approximation is not feasible in practice.
Approach:	They propose a gradient-based data selection framework with clustering and a modified Upper Confidence Bound algorithm to solve this problem.
Outcome:	The proposed framework can achieve comparable results to the original gradient-based data selection methods while reducing computational consumption.

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Hyundong Jin, Sicheol Sung, Shinwoo Park, SeungYeop Baik, Yo-Sub Han

AraReasoner: Evaluating Reasoning-Based LLMs for Arabic NLP (2025.findings-emnlp)

Challenge:	a growing number of LLMs have been used to provide reasoning, writing, text-editing capabilities.
Approach:	They propose a method to inject imperceptible phantom tokens into LLMs to deceive users . the technique generates outputs that appear plausible to users but are in fact incorrect .
Outcome:	a new method injects imperceptible tokens into documents to deceive users . the proposed framework is compared to baselines to show its effectiveness .

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Ahmed Abul Hasanaath, Aisha Alansari, Ahmed Ashraf, Salmane Chafik, Hamzah Luqman, Saad Ezzini

Tales of Morality: Comparing Human- and LLM-Generated Moral Stories from Visual Cues (2025.findings-emnlp)

Challenge:	Large language models have shown remarkable progress in reasoning abilities and general natural language processing tasks, yet their performance on Arabic data remains underexplored.
Approach:	They compare reasoning-focused LLMs with deepSeek models across 15 Arabic NLP tasks . they use zero-shot, few-shot and fine-tuning to evaluate their capacity for linguistic reasoning .
Outcome:	The proposed models outperform strong models on Arabic datasets and are compared with other models.

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Rezvaneh Rezapour, Sullam Jeoung, Zhiwen You, Jana Diesner

AirRAG: Autonomous Strategic Planning and Reasoning Steer Retrieval Augmented Generation (2025.findings-emnlp)

Challenge:	a recent study has found that stories are central to how humans communicate moral values .
Approach:	They compare human- and LLM-generated moral narratives based on images annotated by humans for moral content . authors propose a framework for evaluating moral storytelling in vision-language models .
Outcome:	The proposed model compared human- and LLM-generated narratives on images . human stories reflect a balanced distribution of moral foundations and coherent narrative arcs, but LLMs emphasize Care foundation and lack emotional resolution.

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Wenfeng Feng, Chuzhan Hao, Yuewei Zhang, Guochao Jiang, Jingyi Song

Evaluating NL2SQL via SQL2NL (2025.findings-emnlp)

Challenge:	Experimental results show the effectiveness of AirRAG on complex question-answering datasets.
Approach:	They propose a new thinking pattern that integrates autonomous strategic planning with efficient reasoning actions.
Outcome:	The proposed approach significantly activates intrinsic reasoning capabilities and expands the solution space of specific tasks via Monte Carlo Tree Search.

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Mohammadtaher Safarzadeh, Afshin Oroojlooy, Dan Roth

DB-Explore: Automated Database Exploration and Instruction Synthesis for Text-to-SQL (2025.findings-emnlp)

Challenge:	Existing benchmarks do not address robustness to linguistic variation in NL2SQL models .
Approach:	They propose a schema-aligned paraphrasing framework that leverages SQL-to-NL to generate semantically equivalent, lexically diverse queries while maintaining alignment with the original schema and intent.
Outcome:	The proposed framework generates semantically equivalent, lexically diverse queries while maintaining alignment with the original schema and intent.

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Haoyuan Ma, Yongliang Shen, Hengwei Liu, Wenqi Zhang, Haolei Xu, Qiuying Peng, Jun Wang, Weiming Lu

Do BERT-Like Bidirectional Models Still Perform Better on Text Classification in the Era of LLMs? (2025.findings-emnlp)

Challenge:	Recent text-to-SQL systems that use large language models struggle with complex database structures and domain-specific queries.
Approach:	a framework that aligns large language models with database knowledge is proposed . DB-Explore constructs database graphs to capture complex relational schemas .
Outcome:	a new framework outperforms existing text-to-SQL systems by outperforming existing systems.

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Junyan Zhang, Yiming Huang, Shuliang Liu, Yubo Gao, Xuming Hu

Divide, Optimize, Merge: Scalable Fine-Grained Generative Optimization for LLM Agents (2025.findings-emnlp)

Challenge:	Rapid adoption of LLMs has overshadowed the potential advantages of traditional BERT-like models in text classification.
Approach:	They compare BERT-like models fine-tuning, LLM internal state utilization, and LLM zero-shot inference across six datasets.
Outcome:	The proposed method outperforms LLMs on six challenging datasets.

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Jiale Liu, Yifan Zeng, Shaokun Zhang, Chi Zhang, Malte Højmark-Bertelsen, Marie Normann Gadeberg, Huazheng Wang, Qingyun Wu

Evaluating Evaluation Metrics – The Mirage of Hallucination Detection (2025.findings-emnlp)

Challenge:	LLM-based generative optimization has shown remarkable potential in improving agentic systems, but the current approach of prompting with the trajectories on the whole training dataset becomes untenable as datasets grow.
Approach:	They propose a scalable framework that divides large optimization tasks into manageable subsets and performs targeted optimizations.
Outcome:	The proposed framework outperforms conventional approach by 1.6-8.6% while reducing average prompt token consumption by 56.3%.

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Atharva Kulkarni, Yuan Zhang, Joel Ruben Antony Moniz, Xiou Ge, Bo-Hsiang Tseng, Dhivya Piraviperumal, Swabha Swayamdipta, Hong Yu

The Progress Illusion: Revisiting meta-evaluation standards of LLM evaluators (2025.findings-emnlp)

Challenge:	a large-scale empirical evaluation of hallucination detection metrics is conducted . hallucinosity is a significant obstacle to the reliability and widespread adoption of language models .
Approach:	They conduct large-scale empirical evaluation of hallucination detection metrics . they compare hallucinian language models, language models and decoding methods .
Outcome:	The results show that the evaluations of hallucination detection metrics fail to align with human judgments, they say . they also show that evaluations with LLM-based evaluation yield the best overall results .

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Tianruo Rose Xu, Vedant Gaur, Liu Leqi, Tanya Goyal

Challenge:	LLM judges have gained popularity as an inexpensive and performant substitute for human evaluation.
Approach:	They revisit meta-evaluations of LLM evaluators under a setting that more closely aligns with practice by examining evaluers’ ability to distinguish test system pairs that are closer in capability.
Outcome:	The proposed meta-evaluation setting is significantly different from the use of human evaluations.

MidPO: Dual Preference Optimization for Safety and Helpfulness in Large Language Models via a Mixture of Experts Framework (2025.findings-emnlp)

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Yupeng Qi, Ziyu Lyu, Min Yang, Yanlin Wang, Lu Bai, Lixin Cui

From KMMLU-Redux to Pro: A Professional Korean Benchmark Suite for LLM Evaluation (2025.findings-emnlp)

Challenge:	Recent studies address safety-constrained online and offline preferences optimizations, but offline methods perform poorly in adaptively balancing safety and helpfulness.
Approach:	They propose a mixture of experts framework for safety-helpfulness dual Preference Optimization . they combine a single-preference enhanced direct preference optimization approach with a dynamic routing mechanism .
Outcome:	The proposed framework outperforms state-of-the-art methods in safety and helpfulness.

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Seokhee Hong, Sunkyoung Kim, Guijin Son, Soyeon Kim, Yeonjung Hong, Jinsik Lee

RealBench: A Chinese Multi-image Understanding Benchmark Close to Real-world Scenarios (2025.findings-emnlp)

Challenge:	Using Korean expert-level benchmarks, Large Language Models can be developed in real-world scenarios.
Approach:	They introduce two Korean expert-level benchmarks that reflect professional knowledge in Korea.
Outcome:	The proposed benchmarks represent professional knowledge in Korea.

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Fei Zhao, Chengqiang Lu, Yufan Shen, Qimeng Wang, Yicheng Qian, Haoxin Zhang, Yan Gao, null Yiwu, Yao Hu, Zhen Wu, Shangyu Xing, Xinyu Dai

The More, The Better? A Critical Study of Multimodal Context in Radiology Report Summarization (2025.findings-emnlp)

Challenge:	RealBench is the first Chinese multimodal multi-image dataset . the dataset contains 9393 samples and 69910 images .
Approach:	They propose to create a Chinese multimodal multi-image dataset using 21 models . they use closed-source models that support multi-inputs as well as open-source visual and video models a .
Outcome:	The first Chinese multimodal multi-image dataset contains 9393 samples and 69910 images.

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Mong Yuan Sim, Wei Emma Zhang, Xiang Dai, Biaoyan Fang, Sarbin Ranjitkar, Arjun Burlakoti, Jamie Taylor, Haojie Zhuang

Localizing Malicious Outputs from CodeLLM (2025.findings-emnlp)

Challenge:	Current multimodal summarization models often fail to utilize radiology images in summarizing Findings section.
Approach:	They conduct a thorough analysis to determine whether current multimodal summarization models can utilize radiology images in summarizing Findings section.
Outcome:	The Impression section plays a crucial role in communication between radiologists and physicians.

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Mayukh Borana, Junyi Liang, Sai Sathiesh Rajan, Sudipta Chattopadhyay

Knowing More, Acting Better: Hierarchical Representation for Embodied Decision-Making (2025.findings-emnlp)

Challenge:	Using FreqRank, we localize malicious components in outputs for triggered inputs and their corresponding backdoor triggers.
Approach:	They propose a mutation-based defense to localize malicious components in LLM outputs and their corresponding backdoor triggers.
Outcome:	The proposed defense has an average attack success rate (ASR) of 86.6% and can localize the backdoor triggers in 98% of cases.

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Chunhui Zhang, Zhongyu Ouyang, Xingjian Diao, Zheyuan Liu, Soroush Vosoughi

Culture is Everywhere: A Call for Intentionally Cultural Evaluation (2025.findings-emnlp)

Challenge:	Modern embodied AI uses multimodal large language models as policy models, predicting actions from final-layer hidden states.
Approach:	They propose a hierarchical action probing method that aggregates representations from all layers, mirroring the brain's multi-level organization.
Outcome:	Experiments show that hierarchical probing improves on last-layer embodied models and achieves a 46.6% success rate and a 62.5% gain in spatial reasoning tasks.

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Juhyun Oh, Inha Cha, Michael Saxon, Hyunseung Lim, Shaily Bhatt, Alice Oh

Challenge:	Existing approaches to evaluate cultural alignment of large language models are too trivial and focus on static facts and values.
Approach:	They argue for intentionally cultural evaluation: an approach that examines cultural assumptions . they characterize what, how, and circumstances by which culturally contingent considerations arise in evaluation .
Outcome:	The authors argue for intentionally cultural evaluation: an approach that examines cultural assumptions embedded in all aspects of evaluation, not just in explicitly cultural tasks.

Fairness in Automatic Speech Recognition Isn’t a One-Size-Fits-All (2025.findings-emnlp)

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Hend ElGhazaly, Bahman Mirheidari, Heidi Christensen, Nafise Sadat Moosavi

Uncovering Factor-Level Preference to Improve Human-Model Alignment (2025.findings-emnlp)

Challenge:	Pre-trained speech models like Whisper exhibit inconsistent group-level performance that varies across domains.
Approach:	They fine-tune a Whisper model on the Fair-Speech corpus using basic fine- tuning, demographic rebalancing, gender-swapped data augmentation and a novel contrastive learning objective.
Outcome:	The proposed method achieves stable, cross-domain fairness improvements without changes to the training data distribution and with minimal accuracy trade-offs.

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Juhyun Oh, Eunsu Kim, Jiseon Kim, Wenda Xu, Inha Cha, William Yang Wang, Alice Oh

Challenge:	Large language models exhibit tendencies that diverge from human preferences, such as favoring certain writing styles or producing overly verbose outputs.
Approach:	They propose a framework to uncover and measure factor-level preference alignment of humans and large language models (LLMs)
Outcome:	The proposed framework uncovers and measures factor-level preference alignment of humans and large language models.

Adaptive Preference Optimization with Uncertainty-aware Utility Anchor (2025.findings-emnlp)

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Xiaobo Wang, Zixia Jia, Jiaqi Li, Qi Liu, Zilong Zheng

GRAD: Generative Retrieval-Aligned Demonstration Sampler for Efficient Few-Shot Reasoning (2025.findings-emnlp)

Challenge:	Offline preference optimization methods are efficient for large language models (LLMs) alignment.
Approach:	They propose an offline preference optimization framework that estimates uncertainties from preference data . the method enables training even in scenarios where the data is unpaired .
Outcome:	The proposed method enables training even in scenarios where the data is unpaired .

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Oussama Gabouj, Kamel Charaf, Ivan Zakazov, Nicolas Baldwin, Robert West

IoTMigrator: LLM-driven Embedded IoT Code Migration across Different OSes for Cloud-device Integration (2025.findings-emnlp)

Challenge:	Retrieval-Augmented Generation (RAG) enriches prompts with external information, but its reliance on static databases constrains adaptability and can result in irrelevant demonstrations.
Approach:	They propose a Generative Retrieval-Aligned Demonstrator (GRAD) that trains an LLM model to generate input-specific concise demonstrations.
Outcome:	The proposed model outperforms strong baselines on Qwen2.5-14B across mathematical reasoning and advanced STEM questions.

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null Yq, Kaijie Gong, Yi Gao, Hao Wang, Wei Dong

Challenge:	Neither outline-based code generation nor common code translation techniques can adequately address this challenge, despite their prevalence in existing systems.
Approach:	They have developed an algorithm that employs a multi-agent pipeline to handle embedded code migration under the TSL paradigm.
Outcome:	The proposed algorithm outperforms the baseline by 50.5% for pass rate and 13.0% for completeness across all tasks in RIOT and Zephyr.

ClueAnchor: Clue-Anchored Knowledge Reasoning Exploration and Optimization for Retrieval-Augmented Generation (2025.findings-emnlp)

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Hao Chen, Yukun Yan, Sen Mei, Wanxiang Che, Zhenghao Liu, Qi Shi, Xinze Li, Yuchun Fan, Pengcheng Huang, Qiushi Xiong, Zhiyuan Liu, Maosong Sun

BAGELS: Benchmarking the Automated Generation and Extraction of Limitations from Scholarly Text (2025.findings-emnlp)

Challenge:	Existing RAG systems often underutilize the retrieved documents, authors say . they fail to extract and integrate key clues needed to support faithful and interpretable reasoning .
Approach:	a new framework extracts key clues from retrieved content and generates multiple reasoning paths . the framework optimizes the model by selecting the most appropriate reasoning path .
Outcome:	Experiments show that ClueAnchor outperforms baseline RAG frameworks in completeness and robustness.

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Ibrahim Al Azher, Miftahul Jannat Mokarrama, Zhishuai Guo, Sagnik Ray Choudhury, Hamed Alhoori

Dense Retrievers Can Fail on Simple Queries: Revealing The Granularity Dilemma of Embeddings (2025.findings-emnlp)

Challenge:	a growing number of scientific publications have limitations as a source of uncertainty.
Approach:	They propose a computational architecture for extracting and generating limitations from scholarly papers using a novel Retrieval Augmented Generation technique.
Outcome:	The proposed architecture extracts limitations from ACL, NeurIPS, and PeerJ papers and supplementes them with external reviews.

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Liyan Xu, Zhenlin Su, Mo Yu, Jiangnan Li, Fandong Meng, Jie Zhou

Over-Generation and Compaction: A Prompting Strategy for Procedural Text Adaptation with Large Language Models (2025.findings-emnlp)

Challenge:	a limited number of text encoders are able to recognize fine-grained entities or events within encoded semantics.
Approach:	They propose a new evaluation dataset to examine embeddings' ability to recognize fine-grained entities or events within encoded semantics.
Outcome:	The proposed dataset shows embeddings struggle with fine-grained matching . the proposed encoder outperforms the state-of-the-art 7B model in a small sample .

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HyeongSik Kim, Xu Yanheng, Chaoqun Dong, Fei Du

TransBERT: A Framework for Synthetic Translation in Domain-Specific Language Modeling (2025.findings-emnlp)

Challenge:	Existing prompting strategies for large language models often yield superficial or erroneous adaptations due to alignmentinduced biases and the inherent complexity of procedural editing.
Approach:	They propose an overgenerationandcompaction prompting strategy that leverages the model’s latent knowledge and compacts them into concise, coherent adaptations.
Outcome:	The proposed approach improves adaptation consistency and feasibility compared to baseline prompting methods without additional fine-tuning or curated training resources.

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Julien Knafou, Luc Mottin, Anaïs Mottaz, Alexandre Flament, Patrick Ruch

Beyond Fixed-Length Calibration for Post-Training Compression of LLMs (2025.findings-emnlp)

Challenge:	TransBERT framework for pre-training language models using exclusively synthetically translated text is limited in specialized domains.
Approach:	They propose a framework for pre-training language models using exclusively synthetically translated text . they also introduce a scalable translation toolkit that leverages synthetically trained data .
Outcome:	The proposed framework can be used to train language models using synthetically translated text . transCorpus toolkit can be scalable to the life sciences domain in french .

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Jaehoon Oh, Dokwan Oh

Challenge:	a recent study has demonstrated that the sequence length of calibration data plays a crucial role in the effectiveness of post-training compression methods.
Approach:	They propose a calibration technique that applies masking along the sequence axis to normalized hidden states.
Outcome:	The proposed method improves perplexity and zero-shot downstream tasks performance.

Attributes as Textual Genes: Leveraging LLMs as Genetic Algorithm Simulators for Conditional Synthetic Data Generation (2025.findings-emnlp)

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Guangzeng Han, Weisi Liu, Xiaolei Huang

ReCoVeR the Target Language: Language Steering without Sacrificing Task Performance (2025.findings-emnlp)

Challenge:	Genetic Prompt combines genetic algorithms with Large Language Models to augment synthetic data generation.
Approach:	They propose a framework that combines genetic algorithms with LLMs to augment synthetic data generation.
Outcome:	The proposed framework outperforms state-of-the-art models and shows robust performance across generator models.

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Hannah Sterz, Fabian David Schmidt, Goran Glavaš, Ivan Vulić

Challenge:	Large Language Models exhibit more language confusion as they become multilingual . authors propose a lightweight approach for reducing language confusion based on language-specific steering vectors .
Approach:	They propose a lightweight approach to reduce language confusion by using language-specific steering vectors.
Outcome:	The proposed approach reduces language confusion in large language models . it leverages language-specific steering vectors for effective LLM steering .

LC-Eval: A Bilingual Multi-Task Evaluation Benchmark for Long-Context Understanding (2025.findings-emnlp)

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Sheikh Jubair, Arwa Omayrah, Amal Alshammari, Alhanoof Althnian, Abdulhamed Alothaimen, Norah A. Alzahrani, Shahad D. Alzaidi, Nora Al-Twairesh, Abdulmohsen Al-Thubaity

OVFact: Measuring and Improving Open-Vocabulary Factuality for Long Caption Models (2025.findings-emnlp)

Challenge:	Recent advances in Large Language Models (LLMs) have demonstrated sophisticated capabilities, including the ability to process and comprehend extended contexts.
Approach:	They propose a bilingual, multi-task evaluation benchmark designed to evaluate long-context understanding in English and Arabic.
Outcome:	The proposed benchmark targets context lengths ranging from 4k to over 128k tokens.

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Monika Wysoczańska, Shyamal Buch, Anurag Arnab, Cordelia Schmid

GRPO-Guided Modality Selection Enhanced LoRA-Tuned LLMs for Multimodal Emotion Recognition (2025.findings-emnlp)

Challenge:	Large vision-language models struggle to generate long and factual captions . traditional measures for hallucination and factuality are not well suited for longer captions.
Approach:	They propose a method for measuring caption factuality of long captions that leverages open-vocabulary visual grounding and tool-based verification without relying on human annotations.
Outcome:	The proposed method improves agreement with human judgements and captures both caption descriptiveness and factual precision in the same metric.

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Yang Chen, Shuwan Yang, Yan Xiang, Ran Song, Yuxin Huang, Zhengtao Yu

Defending against Indirect Prompt Injection by Instruction Detection (2025.findings-emnlp)

Challenge:	Multimodal emotion recognition in conversation (MERC) aims to identify speakers’ emotional states by utilizing text, audio, and visual modalities.
Approach:	They propose an adaptive modality selection framework for multimodal emotion recognition in conversation that integrates all available modalities into one .
Outcome:	The proposed framework outperforms existing methods on multimodal dialogue datasets and is available at https://github.com/youflyaway/Modality-Selection-Enhanced-LoRA-Tuned-LLMs.

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Tongyu Wen, Chenglong Wang, Xiyuan Yang, Haoyu Tang, Yueqi Xie, Lingjuan Lyu, Zhicheng Dou, Fangzhao Wu

MUG-Eval: A Proxy Evaluation Framework for Multilingual Generation Capabilities in Any Language (2025.findings-emnlp)

Challenge:	Indirect Prompt Injection attacks can be exploited by LLMs that are embedded with external data.
Approach:	They propose a detection-based approach that leverages the behavioral states of LLMs to identify potential IPI attacks.
Outcome:	The proposed approach reduces the success rate of attacks to 0.03% on the BIPIA benchmark.

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Seyoung Song, Seogyeong Jeong, Eunsu Kim, Jiho Jin, Dongkwan Kim, Jay Shin, Alice Oh

CAC-CoT: Connector-Aware Compact Chain-of-Thought for Efficient Reasoning Data Synthesis Across Dual-System Cognitive Tasks (2025.findings-emnlp)

Challenge:	Evaluating text generation capabilities of large language models (LLMs) is challenging, especially for low-resource languages where methods for direct assessment are scarce.
Approach:	They propose a framework that transforms existing benchmarks into conversational tasks and measures LLMs’ accuracies on those tasks.
Outcome:	The proposed framework correlates strongly with established benchmarks while enabling standardized comparisons across languages and models.

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Sunguk Choi, Yonghoon Kwon, Heondeuk Lee

On the Versatility of Sparse Autoencoders for In-Context Learning (2025.findings-emnlp)

Challenge:	Long chain-of-thought (CoT) prompting often slows or even degrades performance on fast, intuitive "System-1" tasks.
Approach:	They introduce a method that deliberately restricts reasoning to a small, fixed set of connector phrases, steering the model toward concise and well-structured explanations.
Outcome:	The method achieves 85% on GSM8K and 40% on GPQA while also surpassing the baseline by over 20%.

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Ikhyun Cho, Gaeul Kwon, Julia Hockenmaier

More Documents, Same Length: Isolating the Challenge of Multiple Documents in RAG (2025.findings-emnlp)

Challenge:	Sparse autoencoders (SAEs) are emerging as a key analytical tool in interpretability for large language models.
Approach:	They propose to use SAEs to extract knowledge from billions of tokens for sparse reconstruction.
Outcome:	The proposed model can extract knowledge from billions of tokens for sparse reconstruction.

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Shahar Levy, Nir Mazor, Lihi Shalmon, Michael Hassid, Gabriel Stanovsky

CLEAR: A Comprehensive Linguistic Evaluation of Argument Rewriting by Large Language Models (2025.findings-emnlp)

Challenge:	Retrieval-Augmented Generation (RAG) enhances the accuracy of Large Language Models by leveraging relevant external documents during generation.
Approach:	They evaluate various language models on custom datasets derived from QA tasks . they keep context length and position of relevant information constant while varying the number of documents .
Outcome:	The proposed method improves the accuracy of large language models by leveraging external documents . increasing document count reduces performance by up to 20%, the authors find .

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Thomas Huber, Christina Niklaus

ALRPHFS: Adversarially Learned Risk Patterns with Hierarchical Fast & Slow Reasoning for Robust Agent Defense (2025.findings-emnlp)

Challenge:	Argument Improvement (ArgImp) is a text rewriting task that requires LLMs to shorten texts while increasing word length and merging sentences.
Approach:	They propose to use a pipeline to evaluate LLMs' behavior in a text rewriting setting . they use four linguistic levels to examine the qualities of argumentative texts .
Outcome:	The proposed evaluation pipeline compares LLMs on argumentative texts and their improvement on a broad set of argumentation corpora.

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Shiyu Xiang, Tong Zhang, Ronghao Chen

Stop Playing the Guessing Game! Evaluating Conversational Recommender Systems via Target-free User Simulation (2025.findings-emnlp)

Challenge:	Existing safety checks fail to capture complex semantic risks posed by harmful user inputs or unsafe agent behaviors.
Approach:	They propose a framework to bridge the semantic gap between safety checks and real-world risks.
Outcome:	The proposed framework achieves superior overall performance compared to existing baselines.

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SungHwan Kim, Kwangwook Seo, Tongyoung Kim, Jinyoung Yeo, Dongha Lee

Out-of-Context Reasoning in Large Language Models (2025.findings-emnlp)

Challenge:	despite advances in CRSs, reliably assessing their ability to elicit preferences remains a challenge.
Approach:	They propose a user-CRS evaluation protocol with target-free user simulators . they show that current evaluation metrics emphasize single-turn recall of target items .
Outcome:	The proposed evaluation protocol is based on a simulation-based evaluation environment.

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Jonathan Shaki, Emanuele La Malfa, Michael J. Wooldridge, Sarit Kraus

CodeComplex: Dataset for Worst-Case Time Complexity Prediction (2025.findings-emnlp)

Challenge:	a lightweight technique trains only new token embeddings on axioms and evaluates them on unseen tasks.
Approach:	They propose a lightweight technique that trains only new token embeddings on axioms . they train only new embeddables and evaluate them on unseen tasks .
Outcome:	The proposed technique trains only new token embeddings on axioms and evaluates them on unseen tasks.

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SeungYeop Baik, Joonghyuk Hahn, Jungin Kim, null Aditi, Mingi Jeon, Yo-Sub Han, Sang-Ki Ko

Weak2Wise: An Automated, Lightweight Framework for Weak-LLM-Friendly Reasoning Synthesis (2025.findings-emnlp)

Challenge:	Reasoning ability of large language models (LLMs) is crucial in complex decision-making tasks.
Approach:	They propose to use code time complexity prediction to assess LLMs' reasoning ability.
Outcome:	The proposed dataset comprises 4,900 Java codes and an equivalent number of Python codes.

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Jianing Lin, Yuanfang Guo, Shunning Liu, Zeming Liu, Yunhong Wang

From Tower to Spire: Adding the Speech Modality to a Translation-Specialist LLM (2025.findings-emnlp)

Challenge:	Existing approaches to finetuning large language models rely on expensive manual annotations or auxiliary models and fail to address the unique constraints of smaller "weak" LLMs.
Approach:	Weak2Wise is a fully automated framework for synthesizing highquality, weak-LLM-friendly reasoning traces.
Outcome:	Weak2Wise is a fully automated, lightweight framework for synthesizing highquality, weak-LLM-friendly reasoning traces.

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Kshitij Ambilduke, Ben Peters, Sonal Sannigrahi, Anil Keshwani, Tsz Kin Lam, Bruno Martins, Andre Martins, Marcely Zanon Boito

LLM Agents at the Roundtable: A Multi-Perspective and Dialectical Reasoning Framework for Essay Scoring (2025.findings-emnlp)

Challenge:	Spire is a speech-augmented language model capable of translating speech input into 10 languages and transcribing text input in both directions.
Approach:	They introduce a speech-augmented language model capable of translating speech input into 10 languages . they integrate the model into existing multilingual LMs via speech discretization .
Outcome:	Spire integrates speech-augmented language model into existing multilingual model using speech discretization and pre-training using only 42.5 K hours of speech.

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Jinhee Jang, Ayoung Moon, Minkyoung Jung, YoungBin Kim, Seung Jin Lee

DeepNote: Note-Centric Deep Retrieval-Augmented Generation (2025.findings-emnlp)

Challenge:	a new framework for automated essay scoring is needed to achieve multi-perspective understanding and judgment.
Approach:	They propose a roundtable essay scoring framework that performs precise and human-aligned scoring under a zero-shot setting.
Outcome:	The proposed framework outperforms previous zero-shot AES approaches by enabling collaboration among agents with diverse evaluation perspectives.

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Ruobing Wang, Qingfei Zhao, Yukun Yan, Daren Zha, Yuxuan Chen, Shi Yu, Zhenghao Liu, Yixuan Wang, Shuo Wang, Xu Han, Zhiyuan Liu, Maosong Sun

NormAL LoRA: What is the perfect size? (2025.findings-emnlp)

Challenge:	. - (EN)
Approach:	. - (EN)
Outcome:	. - (EN)

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null Aastik, Topu Sai Meghana, Chinmay Prakash Kulkarni, Pragya Paramita Sahu

Inclusive Leadership in the Age of AI: A Dataset and Comparative Study of LLMs vs. Real-Life Leaders in Workplace Action Planning (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) are crucial for enabling intelligent experiences across applications.
Approach:	They propose a low-rank adaptive localization method that uses rank-norm regularization to determine the optimal rank for each weight matrix.
Outcome:	NormAL LoRA reduces adapter parameters by 37% while preserving full fine-tuning performance.

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Vindhya Singh, Sabine Schulte im Walde, Ksenia Keplinger

Challenge:	a new study compares LLMs and human leaders in workplace action planning tasks . the leader success bot guides real-life leaders in generating inclusive workplace action plans .
Approach:	They propose a leader success bot that guides leaders in generating inclusive workplace action plans.
Outcome:	The Leader Success Bot guides real-life leaders in generating inclusive workplace action plans.

Token Preference Optimization with Self-Calibrated Visual-Anchored Rewards for Hallucination Mitigation (2025.findings-emnlp)

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Jihao Gu, Yingyao Wang, Meng Cao, Pi Bu, Jun Song, Bo Zheng, Yancheng He, Shilong Li

EZ-VC: Easy Zero-shot Any-to-Any Voice Conversion (2025.findings-emnlp)

Challenge:	Existing methods for predicting hallucinations suffer from two drawbacks: Lack of scalable token-level rewards and Neglect of visual-anchored tokens.
Approach:	They propose a Token Preference Optimization model with self-calibrated rewards . they propose based on visual-anchored tokens and visual-aware training objective .
Outcome:	The proposed model improves hallucination performance by focusing on visual-anchored tokens without fine-grained annotations.

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Advait Joglekar, Divyanshu Singh, Rooshil Rohit Bhatia, Srinivasan Umesh

Length Representations in Large Language Models (2025.findings-emnlp)

Challenge:	Current voice conversion methods struggle in zero-shot cross-lingual settings . authors develop a method that can be used in zero shot cross-linguistic settings despite advances in technology .
Approach:	They propose a voice-conversion model that combines discrete speech representations with a non-autoregressive speech decoder.
Outcome:	The proposed approach excels in zero-shot cross-lingual settings even for unseen languages and accents.

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Sangjun Moon, Dasom Choi, Jingun Kwon, Hidetaka Kamigaito, Manabu Okumura

MultiLingPoT: Boosting Mathematical Reasoning in LLMs through Multilingual Program Integration (2025.findings-emnlp)

Challenge:	Large language models (LLMs) have shown remarkable capabilities across various tasks, that are learned from massive amounts of text-based data.
Approach:	They propose to scale hidden units within the model to control output sequence length without losing the informativeness of the generated text.
Outcome:	The output sequence length is controlled by multiple head attention mechanisms, which can be adjusted in a disentangled manner.

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Nianqi Li, Zujie Liang, Siyu Yuan, Jiaqing Liang, Feng Wei, Yanghua Xiao

Simulating Identity, Propagating Bias: Abstraction and Stereotypes in LLM-Generated Text (2025.findings-emnlp)

Challenge:	Program-of-Thought is an important way for LLMs to solve mathematical problems.
Approach:	They propose a multilingual programme reasoning method that uses program instead of natural language in reasoning and proposes to integrate multilingual integration into the training and inference.
Outcome:	The proposed method improves individual language’s reasoning accuracy by 2.5% and improves performance by 8%.

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Pia Sommerauer, Giulia Rambelli, Tommaso Caselli

Do LVLMs Know What They Know? A Systematic Study of Knowledge Boundary Perception in LVLMs (2025.findings-emnlp)

Challenge:	Persona-prompting is a growing strategy to personalize outputs, but its impact on how LLMs represent social groups remains underexplored.
Approach:	They investigate whether persona-prompting leads to different levels of linguistic abstraction . they compare 11 persona driven responses to those of a generic AI assistant .
Outcome:	The proposed method can be used to personalize outputs, but its impact on how LLMs represent social groups remains underexplored.

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Zhikai Ding, Shiyu Ni, Keping Bi

Benchmarking Large Language Models for Cryptanalysis and Side-Channel Vulnerabilities (2025.findings-emnlp)

Challenge:	Large Vision-Language Models (LVLMs) demonstrate strong visual question answering (VQA) capabilities but are shown to hallucinate.
Approach:	They propose three confidence-based methods to enhance LVLMs' perception . they propose probabilistic and consistency-based signals are more reliable indicators .
Outcome:	Experiments on three LVLMs across three VQA datasets show that LVLs possess a reasonable perception level but there is room for improvement.

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Utsav Maskey, Chencheng Zhu, Usman Naseem

MTabVQA: Evaluating Multi-Tabular Reasoning of Language Models in Visual Space (2025.findings-emnlp)

Challenge:	Recent advances in Large Language Models (LLMs) have transformed natural language understanding and generation, leading to extensive benchmarking across diverse tasks.
Approach:	They evaluate the cryptanalytic potential of stateoftheart LLMs on ciphertexts produced by a range of cryptographic algorithms.
Outcome:	The proposed model can decrypt plaintexts produced by a range of cryptographic algorithms using zeroshot and fewshot settings along with chainofthought prompting.

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Anshul Singh, Chris Biemann, Jan Strich

TurnBench-MS: A Benchmark for Evaluating Multi-Turn, Multi-Step Reasoning in Large Language Models (2025.findings-emnlp)

Challenge:	Existing benchmarks address single tables or non-visual data, leaving a critical gap . MTabVQA comprises 3,745 complex question-answer pairs .
Approach:	They propose a benchmark specifically designed for multi-tabular visual question answering that measures the ability to parse diverse table images and correlate information across them.
Outcome:	The proposed benchmarks show that fine-tuning VLMs with MTabVQA-Instruct significantly improves their reasoning abilities.

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Yiran Zhang, Mo Wang, Xiaoyang Li, Kaixuan Ren, Chencheng Zhu, Usman Naseem

Assessing LLM Reasoning Steps via Principal Knowledge Grounding (2025.findings-emnlp)

Challenge:	Existing benchmarks focus on single-turn or single-step tasks, failing to capture iterative reasoning in real-world settings.
Approach:	They propose a benchmark that evaluates multi-turn, multi-step reasoning through an interactive code-breaking task inspired by the "Turing Machine Board Game" the best model achieves 84% accuracy in Classic mode, but performance drops to 18% in Nightmare mode.
Outcome:	The new benchmark evaluates multi-turn, multi-step reasoning through an interactive code-breaking task inspired by the "Turing Machine Board Game" the best model achieves 84% accuracy in Classic mode, but performance drops to 18% in Nightmare mode.

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Hyeon Hwang, Yewon Cho, Chanwoong Yoon, Yein Park, Minju Song, Kyungjae Lee, Gangwoo Kim, Jaewoo Kang

Stratified Selective Sampling for Instruction Tuning with Dedicated Scoring Strategy (2025.findings-emnlp)

Challenge:	Step-by-step reasoning has become a standard approach for large language models to tackle complex tasks.
Approach:	They propose a framework that assesses the knowledge grounding of intermediate reasoning by using a large-scale repository of atomic knowledge essential for reasoning.
Outcome:	The evaluation suite identifies missing or misapplied knowledge elements and provides crucial insights for uncovering fundamental reasoning deficiencies in LLMs.

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Paramita Mirza, Lucas Weber, Fabian Küch

CoTD-PO: Chain-of-Thought Distillation with Preference Optimization (2025.findings-emnlp)

Challenge:	Recent work shows that post-training datasets can be substantially downsampled without noticeably deteriorating performance.
Approach:	They propose a method that efficiently bins data into groups and scores difficulty using specialized models.
Outcome:	The proposed method can be efficient and universally applied to post-training datasets.

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Lujie Niu, Haochen Sun, Fangkun Zhao, Sheng Chen, Zimeng Bai, Jiawei Zhang, Caixia Yuan, Xiaojie Wang

Intelligent Document Parsing: Towards End-to-end Document Parsing via Decoupled Content Parsing and Layout Grounding (2025.findings-emnlp)

Challenge:	Existing methods for chain-of-thought distillation suffer from a distribution mismatch between teacher-generated training trajectories and the student model's own generative distribution.
Approach:	They propose a framework that shifts the training paradigm from passive imitation to active trajectory exploration by allowing students to sample their own answer paths.
Outcome:	The proposed method outperforms standard CoT distillation baselines while mitigating mode collapse and preserving semantic diversity.

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Hangdi Xing, Feiyu Gao, Qi Zheng, Zhaoqing Zhu, Zirui Shao, Ming Yan

Feel the Difference? A Comparative Analysis of Emotional Arcs in Real and LLM-Generated CBT Sessions (2025.findings-emnlp)

Challenge:	Existing methods fragment document parsing into pipeline of separated subtasks, resulting in incomplete semantics and error propagation.
Approach:	They propose an end-to-end document parsing framework that leverages vision-language priors of MLLMs.
Outcome:	The proposed method surpasses existing methods significantly in document parsing . it leverages the vision-language priors of MLLMs to decouple parse and layout grounding based on visual information.

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Xiaoyi Wang, Jiwei Zhang, Guangtao Zhang, Honglei Guo

Beyond Single-User Dialogue: Assessing Multi-User Dialogue State Tracking Capabilities of Large Language Models (2025.findings-emnlp)

Challenge:	Synthetic therapy dialogues generated by large language models (LLMs) lack the nuanced emotional dynamics of real therapy.
Approach:	They introduce a dataset of authentic cognitive behavioral therapy dialogues and analyze emotional arcs between real and LLM-generated CBT sessions.
Outcome:	The proposed dataset is a comparative analysis of emotional arcs between real and LLM-generated CBT sessions.

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Sangmin Song, Juhwan Choi, JungMin Yun, YoungBin Kim

All-in-one: Understanding and Generation in Multimodal Reasoning with the MAIA Benchmark (2025.findings-emnlp)

Challenge:	Large language models have demonstrated remarkable performance in zero-shot dialogue state tracking (DST), reducing the need for task-specific training.
Approach:	They extend existing DST dataset by generating utterances of a second user based on speech act theory.
Outcome:	The proposed model incorporates utterances of a second user into conversations, enabling a controlled evaluation of LLMs in multi-user settings.

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Davide Testa, Giovanni Bonetta, Raffaella Bernardi, Alessandro Bondielli, Alessandro Lenci, Alessio Miaschi, Lucia Passaro, Bernardo Magnini

Triangulating LLM Progress through Benchmarks, Games, and Cognitive Tests (2025.findings-emnlp)

Challenge:	MAIA evaluates visual language models on video-related tasks using reasoning categories that aim to disentangle language and vision relations.
Approach:	a native-italian benchmark is designed for fine-grained investigation of the reasoning abilities of visual language models on videos.
Outcome:	The benchmark evaluates visual language models on two aligned tasks and a visual question-answering task.

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Filippo Momentè, Alessandro Suglia, Mario Giulianelli, Ambra Ferrari, Alexander Koller, Oliver Lemon, David Schlangen, Raquel Fernández, Raffaella Bernardi

Challenge:	MMLU and BBH are three evaluation paradigms for language learning models . interactive games are superior to standard benchmarks in discriminating models based on human cognitive assessments .
Approach:	They examine three evaluation paradigms: standard benchmarks, interactive games and cognitive tests . they examine whether interactive games are more effective at discriminating LLMs .
Outcome:	The results show that interactive games are superior to standard benchmarks in discriminating models.

Entity Profile Generation and Reasoning with LLMs for Entity Alignment (2025.findings-emnlp)

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Rumana Ferdous Munne, Md Mostafizur Rahman, Yuji Matsumoto

Re-FRAME the Meeting Summarization SCOPE: Fact-Based Summarization and Personalization via Questions (2025.findings-emnlp)

Challenge:	Entity alignment is a process of identifying and linking equivalent entities across knowledge graphs . only a small fraction of these entities are aligned .
Approach:	They propose a method that combines large language models with entity embeddings to align entities.
Outcome:	ProLEA is a method that combines large language models with entity embeddings to improve alignment accuracy, robustness, and explainability.

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Frederic Kirstein, Sonu Kumar, Terry Ruas, Bela Gipp

Summarization

Challenge:	FRAME reframes summarization as a semantic enrichment task . SCOPE is a reason-out-loud protocol that has the model build a reasoning trace .
Approach:	They propose a modular pipeline that reframes summarization as a semantic enrichment task.
Outcome:	The proposed pipeline reduces hallucinations and omissions by 2 out of 5 points . SCOPE improves knowledge fit and goal alignment over prompt-only baselines .

Attack as Defense: Safeguarding Large Vision-Language Models from Jailbreaking by Adversarial Attacks (2025.findings-emnlp)

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Chongxin Li, Hanzhang Wang, Yuchun Fang

Emphasising Structured Information: Integrating Abstract Meaning Representation into LLMs for Enhanced Open-Domain Dialogue Evaluation (2025.findings-emnlp)

Challenge:	adversarial vulnerabilities in vision-language systems pose a challenge to reliability of large systems . typographic manipulations and adversarial perturbations can bypass language model defenses .
Approach:	They propose a method that embeds perturbations in vision to disrupt attacks . they use cross-modal interactions to enhance adversarial robustness through perturbations .
Outcome:	The proposed approach reduces attack success rates for typographic attacks and adversarial perturbations by integrating visual defenses into the model.

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Bohao Yang, Kun Zhao, Dong Liu, Chen Tang, Liang Zhan, Chenghua Lin

Differentiated Vision: Unveiling Entity-Specific Visual Modality Requirements for Multimodal Knowledge Graph (2025.findings-emnlp)

Challenge:	Existing evaluation metrics struggle to evaluate adversarial negative examples . existing metrics struggle in handling adversarials, resulting in low correlations with human judgments.
Approach:	They propose a framework that integrates AMR and domain-specific language models for automatic open-domain dialogue evaluation.
Outcome:	The proposed evaluation framework achieves strong correlations with human judgments across multiple datasets.

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Minghang Liu, Yinghan Shen, Zihe Huang, Yuanzhuo Wang, Xuhui Jiang, Huawei Shen

Post Persona Alignment for Multi-Session Dialogue Generation (2025.findings-emnlp)

Challenge:	Existing methods to extract features from images of entities overlook varying relevance of visual information across entities.
Approach:	a new model integrates structural and multimodal information of entities into a multimodal knowledge graph . a model evaluates the necessity of visual modality for each entity based on its attributes .
Outcome:	The proposed model improves on existing methods by adjusting visual data to different entity types.

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Yi-Pei Chen, Noriki Nishida, Hideki Nakayama, Yuji Matsumoto

MASSIVE-Agents: A Benchmark for Multilingual Function-Calling in 52 Languages (2025.findings-emnlp)

Challenge:	Existing methods for multi-session persona-based dialogue generation typically retrieve persona information before response generation, which can constrain diversity and result in generic outputs.
Approach:	They propose a two-stage framework that reverses the process of retrieving persona information before response generation.
Outcome:	Experiments on multi-session persona-based dialogue data show that the proposed framework outperforms existing methods in consistency, diversity, and persona relevance.

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Mayank Kulkarni, Vittorio Mazzia, Judith Gaspers, Chris Hench, Jack FitzGerald

Crafting Customisable Characters with LLMs: A Persona-Driven Role-Playing Agent Framework (2025.findings-emnlp)

Challenge:	Using the original dataset, we cleaned up the MASSIVE dataset and reformatted it for evaluation within the Berkeley Function-Calling Leaderboard framework.
Approach:	They present a new benchmark for assessing multilingual function calling across 52 languages . they clean the original MASSIVE dataset and reformat it for evaluation .
Outcome:	The new benchmark covers 55 functions and 286 arguments in 52 languages.

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Bohao Yang, Dong Liu, Chenghao Xiao, Kun Zhao, Chen Tang, Chao Li, Lin Yuan, Yang Guang, Chenghua Lin

Challenge:	Large Language Models (LLMs) are capable of generating human-like text, but the potential for freely customisable characters remains underexplored.
Approach:	They propose a framework which employs Large Language Models to create freely customisable characters through personalised characteristic feature injection.
Outcome:	The proposed framework provides valuable insights for developing more accurate and customisable human simulacra.

Can LLMs Express Personality Across Cultures? Introducing CulturalPersonas for Evaluating Trait Alignment (2025.findings-emnlp)

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Priyanka Dey, Aayush Bothra, Yugal Khanter, Jieyu Zhao, Emilio Ferrara

Challenge:	Recent studies have explored personality evaluation of LLMs, but they largely overlook the interplay between culture and personality.
Approach:	They propose a large-scale benchmark for evaluating LLMs’ personality expression in culturally grounded, behaviorally rich contexts.
Outcome:	The proposed benchmark improves alignment with country-specific human personality distributions and elicits more expressive, culturally coherent outputs compared to existing benchmarks.

Exploring the Hidden Reasoning Process of Large Language Models by Misleading Them (2025.findings-emnlp)

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Guanyu Chen, Peiyang Wang, Yizhou Jiang, Yuqian Liu, Chujie Zhao, Ying Fang, Tianren Zhang, Feng Chen

When Models Reason in Your Language: Controlling Thinking Language Comes at the Cost of Accuracy (2025.findings-emnlp)

Challenge:	Existing large language models can perform abstract reasoning tasks but are they actually engaging in rule-based reasoning beyond mere memorization?
Approach:	They propose a method to examine whether large language models perform abstract reasoning . they fine-tune the model to learn those contradictory rules and assess its generalization ability .
Outcome:	The proposed approach examines whether large language models perform abstract reasoning by altering their original understanding of fundamental rules.

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Jirui Qi, Shan Chen, Zidi Xiong, Raquel Fernández, Danielle Bitterman, Arianna Bisazza

The Role of Model Confidence on Bias Effects in Measured Uncertainties for Vision-Language Models (2025.findings-emnlp)

Challenge:	Recent Large Reasoning Models (LRMs) with thinking traces have shown strong performance on English reasoning tasks.
Approach:	They evaluate two leading LRMs with thinking traces on established benchmark XReasoning and propose directions for future research.
Outcome:	The proposed models often revert to English or produce fragmented reasoning in other languages, revealing a substantial gap in the capability of thinking in non-English languages.

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Xinyi Liu, Weiguang Wang, Hangfeng He

GAttention: Gated Attention for the Detection of Abusive Language (2025.findings-emnlp)

Challenge:	Quantifying epistemic uncertainty in open-ended tasks is challenging due to the presence of aleatoric uncertainty, which arises from multiple valid answers.
Approach:	They conduct experiments on visual question answering tasks and find that mitigating prompt-introduced bias improves uncertainty quantification.
Outcome:	The proposed approach reduces uncertainty quantification in visual question answering tasks by mitigating prompt-introduced biases.

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Horacio Jarquín Vásquez, Hugo Jair Escalante, Manuel Montes, Mario Ezra Aragon

Towards Low-Resource Alignment to Diverse Perspectives with Sparse Feedback (2025.findings-emnlp)

Challenge:	Abusive language online creates toxic environments and exacerbates social tensions, underscoring the need for robust NLP models to interpret nuanced linguistic cues.
Approach:	They propose a Gated Attention mechanism that combines the strengths of Contextual attention and Self-attention mechanisms to address the limitations of existing attention models within the text classification task.
Outcome:	The novel gated attention mechanism addresses the limitations of existing attention models within the text classification task.

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Chu Fei Luo, Samuel Dahan, Xiaodan Zhu

ProtoXTM: Cross-Lingual Topic Modeling with Document-Level Prototype-based Contrastive Learning (2025.findings-emnlp)

Challenge:	popular training paradigms for language models often assume there is one optimal answer for every query.
Approach:	They propose to enhance pluralistic alignment of language models using pluralistic decoding and model steering methods.
Outcome:	The proposed methods improve pluralistic alignment of language models in a low-resource setting . the proposed methods decrease false positives in several high-stakes tasks .

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Seung-Won Seo, Soon-Sun Kwon

One More Question is Enough, Expert Question Decomposition (EQD) Model for Domain Quantitative Reasoning (2025.findings-emnlp)

Challenge:	a recent study has demonstrated that cross-lingual topic modeling can extract aligned and semantically coherent topics from bilingual corpora.
Approach:	They propose a document-level prototype-based contrastive learning paradigm for cross-lingual topic modeling .
Outcome:	The proposed approach achieves state-of-the-art performance on cross-lingual and mono-lingual benchmarks.

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Mengyu Wang, Sotirios Sabanis, Miguel de Carvalho, Shay B Cohen, Tiejun Ma

When Punctuation Matters: A Large-Scale Comparison of Prompt Robustness Methods for LLMs (2025.findings-emnlp)

Challenge:	Domain-specific quantitative reasoning remains a challenge for large language models . we propose an approach to balance domain knowledge with computational efficiency .
Approach:	They propose an approach to balance domain knowledge with computational efficiency . it uses a two-step fine-tuning framework and a reward function to measure sub-questions' effectiveness .
Outcome:	The proposed approach outperforms state-of-the-art domain-tuned models and advanced prompting strategies in the financial domain.

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Mikhail Seleznyov, Mikhail Chaichuk, Gleb Ershov, Alexander Panchenko, Elena Tutubalina, Oleg Somov

RAR2: Retrieval-Augmented Medical Reasoning via Thought-Driven Retrieval (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) are sensitive to subtle, non-semantic variations in prompt phrasing and formatting.
Approach:	They propose to evaluate 4 methods for improving prompt robustness within a unified experimental framework.
Outcome:	The proposed methods are compared to 8 models from Llama, Qwen and Gemma families and are generalized against multiple types of distribution shifts.

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Kaishuai Xu, Wenjun Hou, Yi Cheng, Wenjie Li

The Security Threat of Compressed Projectors in Large Vision-Language Models (2025.findings-emnlp)

Challenge:	Existing methods focus on refining queries without modeling the reasoning process, limiting their ability to retrieve and integrate clinically relevant knowledge.
Approach:	They propose a joint learning framework that improves Reasoning-Augmented Retrieval and Retri-Agmented Reasoning.
Outcome:	The proposed model outperforms RAG baselines on biomedical question answering datasets.

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Yudong Zhang, Ruobing Xie, Xingwu Sun, Jiansheng Chen, Zhanhui Kang, Di Wang, Yu Wang

NarratEX Dataset: Explaining the Dominant Narratives in News Texts (2025.findings-emnlp)

Challenge:	Mainstream VLPs have significant security implications, but their security implications have not been thoroughly examined.
Approach:	a study evaluates the security of visual language projectors by comparing them to uncompressed projector.
Outcome:	The evaluation reveals significant differences in security profiles between compressed and uncompressed projectors.

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Nuno Guimarães, Purificação Silvano, Ricardo Campos, Alipio Jorge, Ana Filipa Pacheco, Dimitar Iliyanov Dimitrov, Nikolaos Nikolaidis, Roman Yangarber, Elisa Sartori, Nicolas Stefanovitch, Preslav Nakov, Jakub Piskorski, Giovanni Da San Martino

Radical Allomorphy: Phonological Surface Forms without Phonology (2025.findings-emnlp)

Challenge:	a dataset is created to explain the choice of the dominant narrative in a news article . the dataset is intended to address discourse polarization and propaganda detection .
Approach:	They propose a dataset for explaining the choice of the dominant narrative in a news article . the dataset is annotated manually with a dominant narrative and sub-narrative labels .
Outcome:	The proposed dataset is designed to explain the choice of the dominant narrative in a news article.

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Salam Khalifa, Nizar Habash, Owen Rambow

Model Calibration for Emotion Detection (2025.findings-emnlp)

Challenge:	Recent work typically frames morphophonology as generating surface forms from abstract underlying representations (URs) this theory-laden assumption is expensive to annotate, especially in low-resource settings.
Approach:	a new approach frames morphophonology as generating surface forms from abstract underlying representations by applying phonological rules or constraints.
Outcome:	The proposed model removes the need to posit or label URs and lets the model exploit the surface evidence directly.

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Mihaela Petre-Vlad, Cornelia Caragea, Florentina Hristea

From Benchmark to Better Embeddings: Leveraging Synonym Substitution to Enhance Multimodal Models in Ukrainian (2025.findings-emnlp)

Challenge:	a MixUp method is used to calibrate emotion detection models based on knowledge distillation and the MixUp data augmentation technique.
Approach:	They propose a method that uses knowledge distillation and the MixUp data augmentation technique to enhance the trustworthiness of emotion detection models.
Outcome:	The proposed method improves the accuracy of the teacher models and the student models.

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Volodymyr Mudryi, Yurii Laba

Context Copying Modulation: The Role of Entropy Neurons in Managing Parametric and Contextual Knowledge Conflicts (2025.findings-emnlp)

Challenge:	SSA for Ukrainian degrades more than English in multimodal retrieval . we propose synonym-augmented fine-tuning to mitigate this problem .
Approach:	They evaluate dictionary-based and LLM-based methods for Ukrainian multimodal retrieval . they propose a Hybrid method that filters dictionary candidates with an LLM to preserve sense and grammar .
Outcome:	The proposed method boosts robustness without harming original performance.

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Zineddine Tighidet, Andrea Mogini, Hedi Ben younes, Jiali Mei, Patrick Gallinari, Benjamin Piwowarski

A Generalizable Rhetorical Strategy Annotation Model Using LLM-based Debate Simulation and Labelling (2025.findings-emnlp)

Challenge:	Recent work has identified in autoregressive transformer models a class of neurons that produce a significant effect on the model output entropy while having an overall moderate impact on the ranking of the predicted tokens.
Approach:	They identify a class of neurons that produce significant effects on the model output entropy while having an overall moderate impact on the ranking of the predicted tokens.
Outcome:	The entropy neurons suppressed context copying behavior in autoregressive transformer models while having moderate impact on the ranking of predicted tokens.

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Shiyu Ji, Farnoosh Hashemi, Joice Chen, Juanwen Pan, Weicheng Ma, Hefan Zhang, Sophia Pan, Ming Cheng, Shubham Mohole, Saeed Hassanpour, Soroush Vosoughi, Michael Macy

Challenge:	Rhetorical strategies are important to persuasive communication, but their analysis relies on human annotation, which is costly, inconsistent and difficult to scale.
Approach:	They propose a framework that leverages large language models to generate and label debate data . they fine-tune transformer-based classifiers on this dataset and validate it against human data a .
Outcome:	The proposed model achieves high performance and strong generalization across topical domains.

SecDecoding: Steerable Decoding for Safer LLM Generation (2025.findings-emnlp)

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Jiayou Wang, Rundong Liu, Yue Hu, Huijia Wu, Zhaofeng He

GENUINE: Graph Enhanced Multi-level Uncertainty Estimation for Large Language Models (2025.findings-emnlp)

Challenge:	Existing decoding-time defense methods suffer from limited generalization, high computational overhead, or significant utility degradation.
Approach:	They propose a decoding-time defense framework that leverages a pair of small contrastive models to estimate token-level safety signals by measuring divergence in their output distributions.
Outcome:	The proposed framework achieves near-zero attack success rates against a wide spectrum of advanced jailbreak attacks while maintaining the model’s helpfulness with minimal degradation.

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Tuo Wang, Adithya Kulkarni, Tyler Cody, Peter A. Beling, Yujun Yan, Dawei Zhou

ReviewEval: An Evaluation Framework for AI-Generated Reviews (2025.findings-emnlp)

Challenge:	Existing methods for estimation of uncertainty overlook semantic dependencies, authors say . genUINE: Graph ENhanced mUlti-level uncertainty Estimation for Large Language Models leverages dependency parse trees and hierarchical graph pooling .
Approach:	They propose a graph-enhanced mUlti-level uncertaINty estimation framework that leverages dependency parse trees and hierarchical graph pooling to refine uncertainty quantification.
Outcome:	The proposed framework achieves higher AUROC and lower calibration errors than existing methods.

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Madhav Krishan Garg, Tejash Prasad, Tanmay Singhal, Chhavi Kirtani, Murari Mandal, Dhruv Kumar

Challenge:	escalating volume of academic research necessitates innovative approaches to peer review . authors propose reviewEval, ReviewAgent and ReviewEval to improve on existing reviews .
Approach:	They propose a framework for AI-generated reviews that measures alignment with human assessments . they propose 'reviewAgent' that iteratively optimizes its intermediate outputs and external improvement loops .
Outcome:	The proposed framework improves actionable insights and analytical depth by 6.78% and 47.62% over baselines and expert reviews.

Overcoming Black-box Attack Inefficiency with Hybrid and Dynamic Select Algorithms (2025.findings-emnlp)

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Abhinay Shankar Belde, Rohit Ramkumar, Jonathan Rusert

GmSLM : Generative Marmoset Spoken Language Modeling (2025.findings-emnlp)

Challenge:	Existing black-box attack methods require large number of queries, making them inefficient and impractical for researchers.
Approach:	proposed new attack selection strategies combine strengths of previous selection algorithms . Hybrid Select merges generalized BinarySelect techniques with GreedySelection . Dynamic Select learns which lengths of texts each selection method should be applied to .
Outcome:	proposed methods combine the strengths of previous selection algorithms to improve performance.

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Talia Sternberg, Michael London, David Omer, Yossi Adi

QA‐LIGN: Aligning LLMs through Constitutionally Decomposed QA (2025.findings-emnlp)

Challenge:	Generative Marmoset Spoken Language Modeling (GmSLM) is an optimized spoken language model pipeline for Marmosaet vocal communication.
Approach:	They propose an optimized spoken language model pipeline for Marmoset vocal communication.
Outcome:	The proposed framework can be used to link vocal communication with brain activity in Marmoset monkeys.

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Jacob Dineen, Aswin Rrv, Qin Liu, Zhikun Xu, Xiao Ye, Ming Shen, Zhaonan Li, Shijie Lu, Chitta Baral, Muhao Chen, Ben Zhou

Characterizing Positional Bias in Large Language Models: A Multi-Model Evaluation of Prompt Order Effects (2025.findings-emnlp)

Challenge:	QA-LIGN decomposes monolithic rewards into interpretable principle-specific evaluations . scalar rewards obscure which objectives drive the training signal .
Approach:	a new method decomposes monolithic rewards into interpretable principle-specific evaluations . QA-LIGN reduces attack success rates by up to 68.7% while maintaining a 0.67% false refusal rate .
Outcome:	QA-LIGN reduces attack success rates by up to 68.7% while maintaining a 0.67% false refusal rate . the results outperform DPO and GRPO with state-of-the-art reward models given equivalent training .

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Patrick Schilcher, Dominik Karasin, Michael Schöpf, Haisam Saleh, Antonela Tommasel, Markus Schedl

You Only Use Reactive Attention Slice When Retrieving From Long Context (2025.findings-emnlp)

Challenge:	Large Language Models can be influenced by various forms of biases, says a new study . positional bias affects how LLMs interpret and weigh information, the authors say .
Approach:	a new study examines the impact of positional bias on large language models . positional biased models prioritize items based on their position rather than content or quality .
Outcome:	a new study shows that LLMs prioritize items based on their position rather than content or quality . the positional bias affects how LLM interpret and weigh information, the authors say .

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Yun Joon Soh, Hanxian Huang, Yuandong Tian, Jishen Zhao

Fine-Tuned Thoughts: Leveraging Chain-of-Thought Reasoning for Industrial Asset Health Monitoring (2025.findings-emnlp)

Challenge:	Existing retrieval techniques for language models are limited due to their reliance on lexical similarity and are computationally expensive to train.
Approach:	They propose a training-free and fine-tuning-free attention-based retrieval technique that uses a reaction score heuristic to quantify how an LM’s self-attention “reacts” to a user query.
Outcome:	The proposed approach improves QA task accuracy by 15% and inference throughput by 31% compared to embedding-based retrieval.

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Shuxin Lin, Dhaval C Patel, Christodoulos Constantinides

CoViPAL: Layer-wise Contextualized Visual Token Pruning for Large Vision-Language Models (2025.findings-emnlp)

Challenge:	Small Language Models (SLMs) are becoming increasingly popular in specialized fields such as industrial applications.
Approach:	They propose a framework which transfers reasoning capabilities via Chain-of-Thought distillation from Large Language Models (LLMs) to smaller, more efficient models (SLMs)
Outcome:	The proposed framework outperforms the base models in Industry 4.0 by a significant margin.

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Zicong Tang, Ziyang Ma, Suqing Wang, Zuchao Li, Lefei Zhang, Hai Zhao, Yun Li, Qianren Wang

Large Language Models with Temporal Reasoning for Longitudinal Clinical Summarization and Prediction (2025.findings-emnlp)

Challenge:	Existing methods to prune redundant vision tokens struggle in shallow layers due to the lack of contextual information.
Approach:	They propose a layer-wise contextualized visual token pruning method that uses a plug-and-play Pruning Module to prune redundant vision tokens.
Outcome:	The proposed method outperforms training-free pruning methods under equal token budgets and surpasses training based methods with comparable supervision.

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Maya Kruse, Shiyue Hu, Nicholas Derby, Yifu Wu, Samantha Stonbraker, Bingsheng Yao, Dakuo Wang, Elizabeth M. Goldberg, Yanjun Gao

TransAlign: Machine Translation Encoders are Strong Word Aligners, Too (2025.findings-emnlp)

Challenge:	Recent advances in large language models have shown potential in clinical text summarization, but their ability to handle long patient trajectories with multi-modal data spread across time remains underexplored.
Approach:	They evaluate open-source large language models, their Retrieval Augmented Generation variants and chain-of-thought prompting on long-context clinical summarization and prediction.
Outcome:	The proposed models can synthesize structured and unstructured EHR data while reasoning over temporal coherence.

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Benedikt Ebing, Christian Goldschmied, Goran Glavaš

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs (2025.findings-emnlp)

Challenge:	translation-based approaches to cross-lingual transfer (XLT) are limited.
Approach:	They propose a word aligner that utilizes the encoder of a massively multilingual MT model.
Outcome:	The proposed word aligner outperforms existing WA and state-of-the-art non-WA-based methods in token classification tasks.

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Yao Fu, Runchao Li, Xianxuan Long, Haotian Yu, Xiaotian Han, Yu Yin, Pan Li

Challenge:	Neural network pruning disrupts LLMs’ internal activation features crucial for lie detection . layer-wise pruning sparsity inadvertently removes crucial weights, failing to improve lie detection performance despite its reliance on the most crucial LLM layer.
Approach:	They propose a pruning approach that places greater emphasis on layers with more activation outliers and stronger discriminative features simultaneously.
Outcome:	The proposed approach improves the hallucination detection for pruned LLMs (achieving 88% accuracy at 50% sparsity) and enhances their performance on TruthfulQA.

Augment before You Try: Knowledge-Enhanced Table Question Answering via Table Expansion (2025.findings-emnlp)

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Yujian Liu, Jiabao Ji, Tong Yu, Ryan A. Rossi, Sungchul Kim, Handong Zhao, Ritwik Sinha, Yang Zhang, Shiyu Chang

Evaluating Large Language Models for Belief Inference: Mapping Belief Networks at Scale (2025.findings-emnlp)

Challenge:	Existing methods to integrate external information into a given table neglect the structured nature of the table.
Approach:	They propose a simple yet effective method to integrate external information into a given table by first building an augmenting table and then generating a SQL query over the two tables to answer the question.
Outcome:	The proposed method outperforms strong baselines on three table QA benchmarks.

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Trisevgeni Papakonstantinou, Antonina Zhiteneva, Ana Yutong Ma, Derek Powell, Zachary Horne

Distinguishing fair from unfair compositional generalization tasks (2025.findings-emnlp)

Challenge:	Beliefs are interconnected, influencing how people process and update what they think.
Approach:	They propose to use a finetuned GPT-4o model to infer belief structures from large-scale social media data.
Outcome:	The proposed model can recover belief structures from large social media data, allowing for a level of scalability and efficiency that is impossible using traditional survey methods.

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Ahmad Jabbar, Cleo Condoravdi, Christopher Potts

SA-CLIP: Language Guided Image Spatial and Action Feature Learning (2025.findings-emnlp)

Challenge:	Compositional generalization benchmarks assess learning agents' ability to combine familiar concepts in novel ways.
Approach:	They propose to use compositional generalization benchmarks to assess learning agents' ability to combine familiar concepts in novel ways.
Outcome:	The proposed tasks are easy and hard, but no present-day models get any traction.

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Guanlin Li, Wenhao Shao, Praboda Rajapaksha, Noel Crespi

Inefficiencies of Meta Agents for Agent Design (2025.findings-emnlp)

Challenge:	Contrastive language-image pretraining models struggle with real-world downstream tasks such as road traffic anomaly detection due to inability to effectively capture spatial and action relationships between objects within images.
Approach:	They compile and curate a dataset and train a Spatial and Action relationship aware CLIP model.
Outcome:	The proposed model performs well on the traffic anomaly detection task .

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Batu El, Mert Yuksekgonul, James Zou

SCoder: Progressive Self-Distillation for Bootstrapping Small-Scale Data Synthesizers to Empower Code LLMs (2025.findings-emnlp)

Challenge:	Recent work has automated the design of agentic systems using meta-agents . authors examine three key challenges in a common class of meta-gents.
Approach:	They examine how meta-agents learn across iterations and show performance improves with evolutionary approach.
Outcome:	The proposed meta-agents perform worse when iterating on multiple agents than human-designed agents.

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Xinyu Zhang, Changzhi Zhou, Linmei Hu, Luhao Zhang, Xiancai Chen, Haomin Fu, Yang Yang, Mengdi Zhang

Linguistically-Controlled Paraphrase Generation (2025.findings-emnlp)

Challenge:	Existing code large language models rely on large-scale instruction data distilled from proprietary LLMs for fine-tuning, which typically incurs high costs.
Approach:	They propose an iterative self-distillation approach to bootstrap small-scale LLMs . they use large-scale instruction data distilled from proprietary LLM for fine-tuning .
Outcome:	The proposed method reduces reliance on proprietary LLMs and minimizes costs.

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Mohamed Elgaar, Hadi Amiri

LAWCAT: Efficient Distillation from Quadratic to Linear Attention with Convolution across Tokens for Long Context Modeling (2025.findings-emnlp)

Challenge:	Controlled paraphrase generation produces paraphrases that preserve meaning while allowing precise control over linguistic attributes of output.
Approach:	They introduce an encoder-decoder framework that enables fine-grained control over 40 linguistic attributes in English.
Outcome:	The proposed framework reduces attribute error by up to 34% over existing models .

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Zeyu Liu, Souvik Kundu, Lianghao Jiang, Anni Li, Srikanth Ronanki, Sravan Babu Bodapati, Gourav Datta, Peter Anthony Beerel

Analyzing Dialectical Biases in LLMs for Knowledge and Reasoning Benchmarks (2025.findings-emnlp)

Challenge:	a novel linearization framework is proposed to reduce the cost of training transformers from scratch.
Approach:	They propose a linear attention framework that integrates pre-trained transformers into a performant linear attention architecture.
Outcome:	The proposed framework improves performance on mistral-7B with 1K-length sequences and BABILong benchmarks.

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Eileen Pan, Anna Seo Gyeong Choi, Maartje Ter Hoeve, Skyler Seto, Allison Koenecke

TreeBoN: Enhancing Inference-Time Alignment with Speculative Tree-Search and Best-of-N Sampling (2025.findings-emnlp)

Challenge:	Previous work has shown degraded performance of large language models for under-represented English dialects.
Approach:	They analyze the effects of typifying “standard” American English language questions as non-”standard” dialectal variants on multiple choice questions.
Outcome:	The results show that typifying “standard” American English language questions as non-”standard” dialectal variants can reduce performance 20% .

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Jiahao Qiu, Yifu Lu, Yifan Zeng, Jiacheng Guo, Jiayi Geng, Chenhao Zhu, Xinzhe Juan, Ling Yang, Huazheng Wang, Kaixuan Huang, Yue Wu, Mengdi Wang

CulturalFrames: Assessing Cultural Expectation Alignment in Text-to-Image Models and Evaluation Metrics (2025.findings-emnlp)

Challenge:	Best-of-N (BoN) sampling generates multiple responses and selects the best one, achieving improved performance but with a high computational cost.
Approach:	They propose a framework that integrates a speculative tree-search strategy into Best-of-N (BoN) Sampling.
Outcome:	The proposed framework outperforms Best-of-N (BoN) sampling but has high computational cost . tree-search strategy reduces computational overhead while maintaining high output quality .

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Shravan Nayak, Mehar Bhatia, Xiaofeng Zhang, Verena Rieser, Lisa Anne Hendricks, Sjoerd Van Steenkiste, Yash Goyal, Karolina Stanczak, Aishwarya Agrawal

Decoupled Proxy Alignment: Mitigating Language Prior Conflict for Multimodal Alignment in MLLMs (2025.findings-emnlp)

Challenge:	CulturalFrames is a benchmark designed for rigorous human evaluation of cultural representation in visual generations.
Approach:	They propose to quantify the alignment of T2I models and evaluation metrics with respect to both explicit (stated) and implicit (unstated, implied by the prompt’s cultural context) cultural expectations.
Outcome:	The proposed model is based on 983 prompts, 3637 images and 10k human annotations from 10 countries and 5 socio-cultural domains.

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Chenkun Tan, Pengyu Wang, Shaojun Zhou, Botian Jiang, Zhaowei Li, Dong Zhang, Xinghao Wang, Yaqian Zhou, Xipeng Qiu

Riemannian Optimization for LoRA on the Stiefel Manifold (2025.findings-emnlp)

Challenge:	Recent advances in multimodal large language models focus on improving performance . however, language prior conflict leads to suboptimal vision-language alignment .
Approach:	They propose a method to decouple the alignment process from language prior interference . they use a proxy LLM to detach from language interference during pretraining .
Outcome:	The proposed method improves training performance and generalizes training data.

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JuneYoung Park, Minjae Kang, Seongbae Lee, Haegang Lee, Seongwan Kim, Jaeho Lee

How Real Are Synthetic Therapy Conversations? Evaluating Fidelity in Prolonged Exposure Dialogues (2025.findings-emnlp)

Challenge:	powerful, large language models (LLMs) present significant fine-tuning challenges due to their size.
Approach:	They propose to optimize LoRA’s B matrix by imposing explicit orthogonality constraints that achieve near-perfect orthogonal and full effective rank.
Outcome:	The proposed method outperforms AdamW and LoRA in terms of parameter efficiency and representational capacity.

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Suhas Bn, Dominik O. Mattioli, Andrew M. Sherrill, Rosa I. Arriaga, Christopher Wiese, Saeed Abdullah

Large Language Models for Controllable Multi-property Multi-objective Molecule Optimization (2025.findings-emnlp)

Challenge:	Synthetic data adoption in healthcare is driven by privacy concerns, data access limitations, and high annotation costs.
Approach:	They compare real and synthetic PTSD therapy conversations using linguistic, structural, and protocol-specific metrics like turn-taking and treatment fidelity.
Outcome:	The proposed framework assesses clinical fidelity beyond surface fluency.

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Vishal Dey, Xiao Hu, Xia Ning

Measuring Lexical Diversity of Synthetic Data Generated through Fine-Grained Persona Prompting (2025.findings-emnlp)

Challenge:	Existing methods for molecule optimization fail to capture property-specific objectives . a series of instruction-tuned LLMs can perform targeted property-specific optimization .
Approach:	They propose a set of instruction-tuned LLMs that can perform targeted property-specific optimization.
Outcome:	a new instruction-tuned LLM can perform targeted property-specific optimization.

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Gauri Kambhatla, Chantal Shaib, Venkata S Govindarajan

Beyond Function-Level Search: Repository-Aware Dual-Encoder Code Retrieval with Adversarial Verification (2025.findings-emnlp)

Challenge:	Fine-grained personas have been used for generating ‘diverse’ synthetic data for pre-training and supervised fine-tuning of Large Language Models (LLMs).
Approach:	They measure the diversity of persona-driven synthetically generated prompts and responses with a suite of lexical diversity and redundancy metrics.
Outcome:	The proposed model is based on human-written prompts and responses, but human-generated prompts are significantly less diverse than human-created ones.

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Aofan Liu, Song Shiyuan, Haoxuan Li, Cehao Yang, Yiyan Qi

Watermark under Fire: A Robustness Evaluation of LLM Watermarking (2025.findings-emnlp)

Challenge:	escalating complexity of modern codebases has intensified the need for code retrieval systems capable of interpreting cross-component change intents.
Approach:	RepoAlignBench is a benchmark designed to evaluate repository-level code retrieval . the benchmark proposes an adversarial reflection-augmented dual-tower architecture .
Outcome:	The proposed framework achieves 12.2% Top-5 Accuracy and 7.1% Recall improvements over state-of-the-art benchmarks.

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Jiacheng Liang, Zian Wang, Spencer Hong, Shouling Ji, Ting Wang

PEPE: Long-context Extension for Large Language Models via Periodic Extrapolation Positional Encodings (2025.findings-emnlp)

Challenge:	Various watermarking methods have been proposed to identify LLM-generated texts . lack of unified evaluation platforms has left many critical questions unanswered .
Approach:	They systematize existing LLM watermarkers and watermark removal attacks and develop a unified platform that integrates them.
Outcome:	The proposed systematizes existing LLM watermarkers and watermark removal attacks, mapping out their design spaces.

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Jikun Hu, Dongsheng Guo, Yuli Liu, Qingyao Ai, Lixuan Wang, Xuebing Sun, Qilei Zhang, Quan Zhou, Cheng Luo

Beyond Self-Reports: Multi-Observer Agents for Personality Assessment in Large Language Models (2025.findings-emnlp)

Challenge:	Long-context extension attempts to extend contextual window in pre-trained LLMs . primary method involves expanding initial positional encodings, disrupting positional learning .
Approach:	They propose a new extension strategy based on Rotary Position Embedding to extend contextual window in pre-trained large language models.
Outcome:	The proposed method can extend the contextual window in pre-trained large language models . expansion disrupts positional encodings learned during pre-training, authors show .

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Yin Jou Huang, Rafik Hadfi

Controlled Retrieval-augmented Context Evaluation for Long-form RAG (2025.findings-emnlp)

Challenge:	Self-report questionnaires are used to assess LLM personality traits, but they fail to capture behavioral nuances due to biases and meta-knowledge contamination.
Approach:	They propose a multi-observer framework for personality trait assessments in LLM agents that draws on informant-report methods in psychology.
Outcome:	The proposed framework combines multiple observers with a subject LLM agent to assess its Big Five personality traits.

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Jia-Huei Ju, Suzan Verberne, Maarten de Rijke, Andrew Yates

Humanity’s Last Code Exam: Can Advanced LLMs Conquer Human’s Hardest Code Competition? (2025.findings-emnlp)

Challenge:	Retrieval-augmented generation (RAG) enhances large language models by incorporating context retrieved from external knowledge sources.
Approach:	They propose a Controlled Retrieval-aUgmented conteXt evaluation framework to directly assess retrieval-augmented contexts.
Outcome:	The proposed framework uses human-written summaries to control the information scope of knowledge.

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Xiangyang Li, Xiaopeng Li, Kuicai Dong, null Zhangquanhu, Rongju Ruan, Xinyi Dai, Yasheng Wang, Ruiming Tang

False Friends Are Not Foes: Investigating Vocabulary Overlap in Multilingual Language Models (2025.findings-emnlp)

Challenge:	o4-mini(high) and Gemini-2.5 Pro achieve pass@1 rates of only 15.9% and 11.4%, respectively.
Approach:	They propose a harmonized online–offline sandbox that guarantees fully reproducible evaluation.
Outcome:	The proposed test reflects the advanced reasoning and code generation ability of large language models.

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Julie Kallini, Dan Jurafsky, Christopher Potts, Martijn Bartelds

Rule-Guided Extraction: A Hierarchical Rule Optimization Framework for Document-Level Event Argument Extraction (2025.findings-emnlp)

Challenge:	Prior work has shown that token overlap facilitates cross-lingual transfer or introduces interference between languages?
Approach:	They devised a controlled experiment where they train bilingual autoregressive models on multiple language pairs under systematically varied vocabulary overlap settings.
Outcome:	The proposed model outperforms models with disjointed vocabularies on XNLI and XQuAD and shows that token overlap is beneficial for multilingual tokenizers.

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Yue Zuo, Yuxiao Fei, Wanting Ning, Jiayi Huang, Yubo Feng, Lishuang Li

SOPL: A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models (2025.findings-emnlp)

Challenge:	Document-level event argument extraction (EAE) is a critical task in natural language processing.
Approach:	They propose an LLM-driven HiErarchical Rule Optimization framework that iteratively generates and selects optimal hierarchical rules.
Outcome:	The proposed framework outperforms few-shot supervised methods and outperformed state-of-the-art prompting baselines.

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Shuyang Wang, Somayeh Moazeni, Diego Klabjan

CLIP-UP: A Simple and Efficient Mixture-of-Experts CLIP Training Recipe with Sparse Upcycling (2025.findings-emnlp)

Challenge:	Using automated prompt engineering to identify effective features is essential for large language models.
Approach:	They propose an optimal learning framework for automated prompt engineering for black-box models . feature-based method is used to express prompt templates, which broadens the search space .
Outcome:	The proposed learning framework outperforms benchmark strategies on instruction induction tasks with limited budgets.

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Xinze Wang, Chen Chen, Yinfei Yang, Hong-You Chen, Bowen Zhang, Aditya Pal, Xiangxin Zhu, Xianzhi Du

A Category-Theoretic Approach to Neural-Symbolic Task Planning with Bidirectional Search (2025.findings-emnlp)

Challenge:	Mixture-of-Experts (MoE) models are crucial for scaling model capacity while controlling inference costs.
Approach:	They propose an alternative training strategy that converts a dense CLIP model into a sparse MoE architecture.
Outcome:	The proposed training strategy outperforms dense models on COCO and Flickr30k benchmarks.

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Shuhui Qu, Jie Wang, Kincho Law

HEAL: An Empirical Study on Hallucinations in Embodied Agents Driven by Large Language Models (2025.findings-emnlp)

Challenge:	Empirical evaluations demonstrate that our method improves completion rates by up to 6.6% and action accuracy by 9.1% .
Approach:	They propose a Neural-Symbolic Task Planning framework that integrates Large Language Model (LLM) decomposition with category-theoretic verification for resource-aware, temporally consistent planning.
Outcome:	The proposed framework improves completion rates and action accuracy by up to 6.6% . it also eliminates resource violations while ensuring resource-awareness and consistency.

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Trishna Chakraborty, Udita Ghosh, Xiaopan Zhang, Fahim Faisal Niloy, Yue Dong, Jiachen Li, Amit Roy-Chowdhury, Chengyu Song

Can LLMs Judge Debates? Evaluating Non-Linear Reasoning via Argumentation Theory Semantics (2025.findings-emnlp)

Challenge:	Large language models are increasingly being adopted as the cognitive core of embodied agents.
Approach:	They propose a systematic study of hallucinations in large language models . they aim to understand to what extent hallucinos occur, what types trigger them .
Outcome:	The proposed model can induce hallucinations up to 40 higher than base prompts . the model fails to resolve scene-task inconsistencies, the study finds .

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Reza Sanayei, Srdjan Vesic, Eduardo Blanco, Mihai Surdeanu

How Jailbreak Defenses Work and Ensemble? A Mechanistic Investigation (2025.findings-emnlp)

Challenge:	Large Language Models excel at linear reasoning tasks but are underexplored on non-linear structures such as natural debates.
Approach:	They evaluate whether Large Language Models can approximate structured reasoning from Computational Argumentation Theory.
Outcome:	The proposed model performs well on dialogue-formatted debates without access to the underlying graph.

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Zhuohan Long, Siyuan Wang, Shujun Liu, Yuhang Lai

Visual Self-Refinement for Autoregressive Models (2025.findings-emnlp)

Challenge:	Jailbreak attacks, where harmful prompts bypass generative models’ built-in safety, raise serious concerns about model vulnerability.
Approach:	They propose to reframe the standard generation task as a binary classification problem to assess model refusal tendencies for both harmful and benign queries.
Outcome:	The proposed defenses improve model safety or optimize the trade-off between safety and helpfulness.

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Jiamian Wang, Ziqi Zhou, Chaithanya Kumar Mummadi, Sohail Dianat, Majid Rabbani, Raghuveer Rao, Chen Qiu, Zhiqiang Tao

Retrieval-Augmented Language Models are Mimetic Theorem Provers (2025.findings-emnlp)

Challenge:	Autoregressive models excel in sequential modeling but the spatial nature of visual signals conflicts with the sequential dependencies of next-token prediction, leading to suboptimal results.
Approach:	They propose a plug-and-play refinement module to enhance the spatial correspondence modeling within the generated visual sequence.
Outcome:	The proposed module enhances vision-language modeling under a shared sequential prediction framework.

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Wenjie Yang, Ruiyuan Huang, Jiaxing Guo, Zicheng Lyu, Tongshan Xu, Shengzhong Zhang, Lun Du, Da Zheng, Zengfeng Huang

LORE: Continual Logit Rewriting Fosters Faithful Generation (2025.findings-emnlp)

Challenge:	Large language models often fail to provide rigorous proof-based reasoning for research-level mathematics.
Approach:	They propose a simple yet effective RAG framework that augments retrieved proofs with queries and document contexts to improve retrieval performance.
Outcome:	The proposed framework improves retrieval performance by 34.19% . dual RAG can be used to prove research-level theorems in theoretical machine learning .

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Charles Yu, Qingyun Wang, Yuting Hu, Jinjun Xiong, Heng Ji

PRINCIPLES: Synthetic Strategy Memory for Proactive Dialogue Agents (2025.findings-emnlp)

Challenge:	Using aspect-oriented summarization as a case study, we propose LOgit REwriting, a new controlled generation paradigm which can be faithful to external knowledge and to the LLM’s intentions.
Approach:	They propose a controlled generation paradigm which can be faithful to external knowledge and to the LLM's intentions.
Outcome:	The proposed paradigm can be faithful to external knowledge and to the LLM's intentions while balancing that with accuracy.

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Namyoung Kim, Kai Tzu-iunn Ong, Yeonjun Hwang, Minseok Kang, Iiseo Jihn, Gayoung Kim, Minju Kim, Jinyoung Yeo

SLM-Bench: A Comprehensive Benchmark of Small Language Models on Environmental Impacts (2025.findings-emnlp)

Challenge:	Existing strategies for proactive dialogue face limitations such as limited strategy coverage and preference bias in planning.
Approach:	They propose a synthetic strategy memory for proactive dialogue agents based on large language models . PRINCIPLES is derived through offline self-play simulations and serves as reusable knowledge that guides strategy planning during inference.
Outcome:	PRINCIPLES is a synthetic strategy memory for proactive dialogue agents.

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Nghiem Thanh Pham, Tung Kieu, Duc Manh Nguyen, Son Ha Xuan, Nghia Duong-Trung, Danh Le-Phuoc

A Decoupled Multi-Agent Framework for Complex Text Style Transfer (2025.findings-emnlp)

Challenge:	SLMs offer computational efficiency and accessibility, yet a systematic evaluation of their performance and environmental impact remains lacking.
Approach:	SLM-Bench evaluates 15 SLMs on 9 NLP tasks using 23 datasets . compared accuracy, computational efficiency, and sustainability metrics .
Outcome:	SLM-Bench evaluates 15 SLMs on 9 NLP tasks using 23 datasets spanning 14 domains.

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Lingxi Zhang, Yu-Neng Chuang, Guanchu Wang, Ruixiang Tang, Xuanting Cai, Rajesh Shenoy, Xia Hu

Mamba Drafters for Speculative Decoding (2025.findings-emnlp)

Challenge:	Existing models for text style transfer struggle with complex styles . existing models perform well on simple styles like sentiment and formality .
Approach:	They propose a multi-agent self-check framework that includes a large language model as a planner for disentangling subtasks and expert agents for executing the subtask.
Outcome:	The proposed framework significantly improves style strength and content preservation on simple and complex style datasets.

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Daewon Choi, Seunghyuk Oh, Saket Dingliwal, Jihoon Tack, Kyuyoung Kim, Woomin Song, Seojin Kim, Insu Han, Jinwoo Shin, Aram Galstyan, Shubham Katiyar, Sravan Babu Bodapati

LongLLaVA: Scaling Multi-modal LLMs to 1000 Images Efficiently via a Hybrid Architecture (2025.findings-emnlp)

Challenge:	Existing drafters that use external drafters suffer from slower drafting while self-speculation methods use drafters tailored to the target model but require re-training.
Approach:	They propose a drafter based on a state space model, Mamba, as a solution that combines the best aspects of both approaches.
Outcome:	The proposed drafters outperform existing drafters while using less memory and maintaining their cross-model adaptability.

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Xidong Wang, Dingjie Song, Shunian Chen, Junying Chen, Zhenyang Cai, Chen Zhang, Lichao Sun, Benyou Wang

Think Clearly: Improving Reasoning via Redundant Token Pruning (2025.findings-emnlp)

Challenge:	Long-context Large Language Models (MLLMs) are critical for video understanding and image analysis.
Approach:	They propose a hybrid architecture that integrates Mamba and Transformer blocks . they introduce data construction methods that capture both temporal and spatial dependencies .
Outcome:	The proposed model achieves competitive results across various benchmarks while maintaining high throughput and low memory consumption.

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Daewon Choi, Jimin Lee, Jihoon Tack, Woomin Song, Saket Dingliwal, Sai Muralidhar Jayanthi, Bhavana Ganesh, Jinwoo Shin, Aram Galstyan, Sravan Babu Bodapati

A Systematic Survey of Claim Verification: Corpora, Systems, and Case Studies (2025.findings-emnlp)

Challenge:	Recent large language models show promising capabilities in long-form reasoning . however, they tend to include substantial redundancy in reasoning paths .
Approach:	They propose a structure-aware pruning method that prioritizes removing redundant tokens . they remove redundant token and then resume the reasoning generation .
Outcome:	The proposed method shows strong performance on reasoning-intensive benchmarks without training.

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Zhaxi Zerong, Chenxi Li, Xinyi Liu, Ju-hui Chen, Fei Xia

Automated Creativity Evaluation for Large Language Models: A Reference-Based Approach (2025.findings-emnlp)

Challenge:	This survey analyses 198 studies published between January 2022 and March 2025 .
Approach:	This survey synthesizes recent advances in CV corpus creation and system design.
Outcome:	The results of this study are synthesized from 198 studies published between January 2022 and March 2025.

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Ruizhe Li, Chiwei Zhu, Benfeng Xu, Xiaorui Wang, Zhendong Mao

LangProBe: a Language Program Benchmark (2025.findings-emnlp)

Challenge:	Existing methods for evaluating creativity of machine-generated texts rely on costly manual annotations or fail to align closely with human assessments.
Approach:	They propose an automated method based on the Torrance Test of Creative Writing (TTCW) .
Outcome:	The proposed method improves the alignment between LLM evaluations and human assessments.

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Shangyin Tan, Lakshya A Agrawal, Arnav Singhvi, Liheng Lai, Michael J Ryan, Dan Klein, Omar Khattab, Koushik Sen, Matei Zaharia

Exploring and Detecting Self-disclosure in Multi-modal posts on Chinese Social Media (2025.findings-emnlp)

Challenge:	Composing language models into multi-step language programs is a mainstream paradigm for building AI systems, but tradeoffs in this space have only scarcely been studied before.
Approach:	They propose a benchmarking tool to evaluate the architectures and optimization strategies for language programs . they find that optimized language programs offer strong cost-quality Pareto improvement .
Outcome:	The proposed framework evaluates the impact of program architectures and optimizers on quality and cost.

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Jingbao Luo, Ming Liu, Aoli Huo, Fujing Hu, Gang Li, Wupeng Njust

MV-CLAM: Multi-View Molecular Interpretation with Cross-Modal Projection via Language Model (2025.findings-emnlp)

Challenge:	Self-disclosure can provide psychological comfort but can also pose privacy concerns . a lack of high-quality corpora, analysis, and methods for detection is limiting research .
Approach:	They construct a high-quality text-image corpus on Chinese multimodal social media platforms . they analyze the distribution of self-disclosure types, modality preferences, user intent .
Outcome:	The proposed corpus analyzes self-disclosure behaviors on Chinese social media platforms . it fine-tunes five multimodal large language models to enhance self-discovery detection .

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Sumin Ha, Jun Hyeong Kim, Yinhua Piao, Changyun Cho, Sun Kim

Mind the Style Gap: Meta-Evaluation of Style and Attribute Transfer Metrics (2025.findings-emnlp)

Challenge:	Existing molecule-text models ignore complementary information in different molecular views and rely on single-view representations, limiting molecule structural understanding.
Approach:	They propose a framework that aligns multi-view molecular representations into a unified textual space using a multi-querying transformer.
Outcome:	The proposed framework ensures cross-view consistency while the proposed token-level contrastive loss preserves diverse molecular features across textual queries.

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Amalie Brogaard Pauli, Isabelle Augenstein, Ira Assent

ExtremeAIGC: Benchmarking LMM Vulnerability to AI-Generated Extremist Content (2025.findings-emnlp)

Challenge:	Large language models (LLMs) make it easy to rewrite a text in any style, but they are not straightforward when evaluating content preservation.
Approach:	They propose a large meta-evaluation of metrics for evaluating style and attribute transfer, focusing on content preservation.
Outcome:	The proposed method achieves higher alignment with human judgements than prompting a model of a similar size as an autorater.

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Bhavik Chandna, Mariam Aboujenane, Usman Naseem

Data Augmentation for Maltese NLP using Transliterated and Machine Translated Arabic Data (2025.findings-emnlp)

Challenge:	Existing datasets for evaluating LMM robustness lack exploration of extremist content . existing models lack diverse image generation models and comprehensive coverage of historical events .
Approach:	They propose a benchmark dataset to assess LMM models against extremist content . ExtremeAIGC simulates real-world events and malicious use cases .
Outcome:	a new benchmark dataset and evaluation framework assesses LMM models against extremist content.

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Kurt Micallef, Nizar Habash, Claudia Borg

Do LLMs Align Human Values Regarding Social Biases? Judging and Explaining Social Biases with LLMs (2025.findings-emnlp)

Challenge:	Maltese is a Semitic language that has evolved under extensive influence from Romance and Germanic languages, particularly Italian and English.
Approach:	They investigate whether Arabic-language resources can support Maltese natural language processing . they introduce transliteration schemes and machine translation approaches to align Arabic text with Maltesen .
Outcome:	The proposed techniques can significantly improve Maltese natural language processing tasks.

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Yang Liu, Chenhui Chu

CoEx – Co-evolving World-model and Exploration (2025.findings-emnlp)

Challenge:	Large language models can lead to undesired consequences when misaligned with human values . previous studies have shown misalignment of LLMs with human value using expert-designed or agent-based emulated bias scenarios .
Approach:	They investigate whether large language models (LLMs) are misaligned with human values . they find no significant differences in understanding of HVSB between LLMs .
Outcome:	The results show that large language models do not have lower misalignment rates and attack success rates . the study also shows that smaller language models have the ability to explain HVSB .

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Minsoo Kim, Seung-won Hwang

Challenge:	Existing LLM agents fail to assimilate new observations into dynamic updates of the world model, leading to divergent and erroneous plans.
Approach:	They propose a hierarchical agent architecture that allows LLM planning to co-evolve with a dynamically updated model of the world.
Outcome:	The proposed agent outperforms existing agent paradigms in planning and exploration.

BrainLoc: Brain Signal-Based Object Detection with Multi-modal Alignment (2025.findings-emnlp)

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Jiaqi Duan, Xiaoda Yang, Kaixuan Luan, Hongshun Qiu, Weicai Yan, Xueyi Zhang, Youliang Zhang, Zhaoyang Li, Donglin Huang, JunYu Lu, Ziyue Jiang, Xifeng Yang

PVTNL: Prompting Vision Transformers with Natural Language for Generalizable Person Re-identification (2025.findings-emnlp)

Challenge:	BrainLoc is a lightweight object detection model guided by fMRI signals.
Approach:	They propose a brain-based object detection model guided by fMRI signals . they employ a multi-modal alignment strategy that enhances fmr feature extraction .
Outcome:	The proposed model improves fMRI-based object detection accuracy and convenience.

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null Wangning, Lei Xie, Sanglu Lu, Shiwei Gan

RingFormer: Rethinking Recurrent Transformer with Adaptive Level Signals (2025.findings-emnlp)

Challenge:	Domain generalization person re-identification (DG-ReID) aims to train models on source domains and generalize to unseen target domains.
Approach:	They propose a framework to generalize person re-identification using a vision-language model . body-part cues are used to segment images into semantically coherent regions .
Outcome:	The proposed framework can generalize to unseen domains and generalize semantics to people . it leverages the pre-trained vision-language model BLIP to extract aligned visual and textual embeddings.

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Jaemu Heo, Eldor Fozilov, Hyunmin Song, Taehwan Kim

TriSPrompt: A Hierarchical Soft Prompt Model for Multimodal Rumor Detection with Incomplete Modalities (2025.findings-emnlp)

Challenge:	Transformers have shown strong performance in processing sequential data, but their parameters are larger . a novel approach to reduce the model parameters while maintaining high performance is proposed .
Approach:	They propose a transformer-based model that processes input repeatedly in a circular, ring-like manner.
Outcome:	The proposed approach reduces model parameters while maintaining high performance . the proposed approach is validated in the experiments.

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Jiajun Chen, Yangyang Wu, Xiaoye Miao, Mengying Zhu, Meng Xi

Evaluating Uncertainty Quantification Methods in Argumentative Large Language Models (2025.findings-emnlp)

Challenge:	Existing multimodal rumor detection methods focus on learning joint modality representations from complete multimodal training data, rendering them ineffective in addressing the common occurrence of missing modalities in real-world scenarios.
Approach:	They propose a hierarchical soft prompt model TriSPrompt which integrates three types of prompts to effectively detect rumors in incomplete multimodal data.
Outcome:	The proposed model achieves an accuracy gain of over 13% compared to state-of-the-art models.

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Kevin Zhou, Adam Dejl, Gabriel Freedman, Lihu Chen, Antonio Rago, Francesca Toni

CLAIMCHECK: How Grounded are LLM Critiques of Scientific Papers? (2025.findings-emnlp)

Challenge:	ArgLLMs are an explainable LLM framework for decision-making based on computational argumentation in which uncertainty quantification plays a critical role.
Approach:	They propose to integrate LLM UQ methods into argumentative LLMs to evaluate their performance on claim verification tasks.
Outcome:	The proposed method outperforms more complex approaches on claim verification tasks.

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Jiefu Ou, William Gantt Walden, Kate Sanders, Zhengping Jiang, Kaiser Sun, Jeffrey Cheng, William Jurayj, Miriam Wanner, Shaobo Liang, Candice Morgan, Seunghoon Han, Weiqi Wang, Chandler May, Hannah Recknor, Daniel Khashabi, Benjamin Van Durme

From Noise to Clarity: Filtering Real and LLM-Generated Samples for Enhanced Intent Detection (2025.findings-emnlp)

Challenge:	CLAIMCHECK is an annotated dataset of NeurIPS 2023 and 2024 submissions and reviews from OpenReview.
Approach:	They annotate NeurIPS 2023 and 2024 submissions and reviews for weaknesses and dispute them for fine-grained labels of validity, objectivity, and type of the identified weaknesses.
Outcome:	The proposed dataset is richly annotated by ML experts for weaknesses statements in the reviews and the claims that they dispute, as well as fine-grained labels of validity, objectivity, and type of the identified weaknesses.

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Junbao Huang, Weizhen Li, Peijie Huang, Yuhong Xu

Improving Language Model Personas via Rationalization with Psychological Scaffolds (2025.findings-emnlp)

Challenge:	Using large language models for generating synthetic samples for data augmentation can cause problems with the generalization ability of classification models.
Approach:	They propose an interpretable Sample Filter by Topic Modeling framework that allows for filtering by topic and a 'sampler by topic' framework.
Outcome:	The proposed framework reduces the quantity of real and synthetic samples while improving the performance of the classification models.

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Brihi Joshi, Xiang Ren, Swabha Swayamdipta, Rik Koncel-Kedziorski, Tim Paek

KBM: Delineating Knowledge Boundary for Adaptive Retrieval in Large Language Models (2025.findings-emnlp)

Challenge:	Existing approaches to building personas rely on a user’s demographic attributes and/or prior judgments, but not on any underlying reasoning behind a person’s judgments.
Approach:	They propose a framework that integrates rationales for why a user could have made a certain judgment into LM personas by incorporating potential rationale.
Outcome:	The proposed framework outperforms models conditioned on demographic attributes and/or prior judgments on public opinion and movie preference prediction tasks.

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Zhen Zhang, Xinyu Wang, Yong Jiang, Zile Qiao, Zhuo Chen, Guangyu Li, Feiteng Mu, Mengting Hu, Pengjun Xie, Fei Huang

TABARD: A Novel Benchmark for Tabular Anomaly Analysis, Reasoning and Detection (2025.findings-emnlp)

Challenge:	Retrieval-augmented generation (RAG) is employed to tackle these challenges . a Knowledge Boundary Model (KBM) is used to express the known/unknown of a given question .
Approach:	They propose a Knowledge Boundary Model to express the known/unknown of a given question . they find that not all questions need to trigger RAG to improve performance .
Outcome:	The proposed model reduces time and computational costs by retrieving parts of unknown knowledge . the proposed model can express the known/unknown of a given question and determine whether a RAG needs to be triggered .

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Manan Roy Choudhury, Anirudh Iyengar Kaniyar Narayana Iyengar, Shikhhar Siingh, Sugeeth Puranam, Vivek Gupta

Aspect-based Sentiment Analysis via Synthetic Image Generation (2025.findings-emnlp)

Challenge:	a new benchmark is constructed to evaluate the accuracy of large language models for tabular data . the benchmark uses direct, indirect, and Chain-of-Thought prompting .
Approach:	They propose a framework that uses prompting, self-verification and constraint-based rule execution to improve accuracy.
Outcome:	The proposed framework significantly improves accuracy and recall in tabular data.

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Ge Chen, Zhongqing Wang, Guodong Zhou

IntrEx: A Dataset for Modeling Engagement in Educational Conversations (2025.findings-emnlp)

Challenge:	Recent advances in Aspect-Based Sentiment Analysis (ABSA) have shown promising results, yet the semantics derived solely from textual data remain limited.
Approach:	They propose a supervised image generation framework to generate synthetic images with alignment to text and sentiment information.
Outcome:	The proposed approach significantly outperforms state-of-the-art methods on multiple benchmark datasets.

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Xingwei Tan, Mahathi Parvatham, Chiara Gambi, Gabriele Pergola

Bridging the Capability Gap: Joint Alignment Tuning for Harmonizing LLM-based Multi-Agent Systems (2025.findings-emnlp)

Challenge:	IntrEx is the first large dataset annotated for interestingness and expected interestingness in teacher-student interactions.
Approach:	They propose a large dataset annotated for interestingness and expected interestingness in teacher-student interactions.
Outcome:	The proposed dataset is the first large dataset annotated for interestingness and expected interestingness in teacher-student interactions.

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Minghang Zhu, Zhengliang Shi, Zhiwei Xu, Shiguang Wu, Lingjie Wang, Pengjie Ren, Zhaochun Ren, Zhumin Chen

Challenge:	Existing methods for fine-tuning agents are often inadequate . a multi-agent system can solve complex tasks by dividing responsibilities among specialized agents .
Approach:	a new framework is proposed to improve agents collaboration through iterative alignment.
Outcome:	The proposed framework outperforms state-of-the-art benchmarks on held-in and held-out tasks.

Safety Through Reasoning: An Empirical Study of Reasoning Guardrail Models (2025.findings-emnlp)

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Makesh Narsimhan Sreedhar, Traian Rebedea, Christopher Parisien

Context-Aware Reasoning On Parametric Knowledge for Inferring Causal Variables (2025.findings-emnlp)

Challenge:	Recent research shows that reasoning-based language models offer significant benefits for LLM safety and guardrail applications.
Approach:	They conduct an analysis of reasoning-based guardrail models for content moderation . they find reasoning models exhibit strong sample efficiency and inference efficiency .
Outcome:	The reasoning-based guardrail models show strong performance across domains . the models achieve competitive performance with significantly fewer training examples .

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Ivaxi Sheth, Sahar Abdelnabi, Mario Fritz

LoRE-Merging: Exploring Low-Rank Estimation For Large Language Model Merging (2025.findings-emnlp)

Challenge:	randomized experiments provide strong inferences, but are often infeasible due to ethical or practical constraints.
Approach:	They propose a benchmark where the objective is to complete a partial causal graph.
Outcome:	The proposed benchmarks show that they can hypothesize backdoor variables between a cause and its effect.

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Zehua Liu, Han Wu, Yuxuan Yao, Xiaojin Fu, Ruifeng She, Xiongwei Han, Tao Zhong, Mingxuan Yuan

Benchmarking Foundation Models with Retrieval-Augmented Generation in Olympic-Level Physics Problem Solving (2025.findings-emnlp)

Challenge:	a framework for model merging is proposed without additional training . task vectors from fine-tuned models exhibit a limited number of dominant singular values .
Approach:	They propose a framework for model merging based on low-rank estimation of task vectors without access to the base model.
Outcome:	The proposed framework improves models without additional training without additional inputs.

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Shunfeng Zheng, Yudi Zhang, Meng Fang, Zihan Zhang, Zhitan Wu, Mykola Pechenizkiy, Ling Chen

FiRST: Finetuning Router-Selective Transformers for Input-Adaptive Latency Reduction (2025.findings-emnlp)

Challenge:	a new study examines the potential of retrieval-augmented generation (RAG) with foundation models to enhance expert-level reasoning.
Approach:	They introduce PhoPile, a high-quality multimodal dataset specifically designed for Olympiad-level physics.
Outcome:	The proposed model can be used to solve Olympiad-level physics problems.

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Akriti Jain, Saransh Sharma, Koyel Mukherjee, Soumyabrata Pal

PolitiSky24: U.S. Political Bluesky Dataset with User Stance Labels (2025.findings-emnlp)

Challenge:	Existing approaches to improve latency via skipping layers have limitations . fiRST is a model-agnostic framework that reduces inference latency while maintaining quality .
Approach:	They propose a model-agnostic framework that skips transformer layers during decoding . it is fully compatible with KV caching, enabling faster decoding while maintaining quality .
Outcome:	a new framework reduces inference latency by using layer-specific routers to skip transformer layers during decoding.

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Peyman Rostami, Vahid Rahimzadeh, Ali Adibi, Azadeh Shakery

From Ground Trust to Truth: Disparities in Offensive Language Judgments on Contemporary Korean Political Discourse (2025.findings-emnlp)

Challenge:	Stance detection is a method of identifying the viewpoint expressed in text toward a specific target, such as a political figure.
Approach:	They present a dataset for the 2024 U.S. presidential election that includes 16,044 user-target stance pairs enriched with engagement metadata, interaction graphs, and user posting histories.
Outcome:	The proposed dataset comprises 16,044 user-target stance pairs enriched with engagement metadata, interaction graphs, and user posting histories.

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Seunguk Yu, JungMin Yun, Jinhee Jang, YoungBin Kim

Misalignment Attack on Text-to-Image Models via Text Embedding Optimization and Inversion (2025.findings-emnlp)

Challenge:	a recent study using LLMs has relied on outdated datasets and limited generalization ability on unseen texts.
Approach:	They construct a large-scale dataset of political discourse and use it to make three judgments . they identify distinct patterns and demonstrate tendencies of label agreement using a leave-one-out strategy.
Outcome:	The proposed approach is applicable in real-world settings with inherent constraints.

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Zhijie Du, Daizong Liu, Pan Zhou

Domain Pre-training Impact on Representations (2025.findings-emnlp)

Challenge:	Text embedding is a key component of modern NLP models but also poses additional risks.
Approach:	They propose a framework that optimizes embeddings and inverts them to obtain misaligned prompts.
Outcome:	The proposed framework exploits the continuity and distribution characteristics of text embeddings to obtain misaligned prompts of discrete tokens.

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Cesar Gonzalez-Gutierrez, Ariadna Quattoni

KoACD: The First Korean Adolescent Dataset for Cognitive Distortion Analysis via Role-Switching Multi-LLM Negotiation (2025.findings-emnlp)

Challenge:	a small, specialized corpus can produce effective representations, but the quality of pre-training is not affected by the choice of corpus.
Approach:	They focus on the representation quality achieved through pre-training alone .
Outcome:	The results show that pre-training on a small, specialized corpus produces effective representations.

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Jun Seo Kim, Hye Hyeon Kim

Challenge:	Cognitive distortions are negative thinking patterns that can lead to mental health issues in adolescents.
Approach:	They propose a multi-Large Language Model negotiation method to refine distortion classification . they also use cognitive clarification and cognitive balancing to improve label consistency .
Outcome:	The proposed dataset contains 108,717 instances of cognitive distortions in Korean adolescents.

Refined Assessment for Translation Evaluation: Rethinking Machine Translation Evaluation in the Era of Human-Level Systems (2025.findings-emnlp)

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Dmitry Popov, Vladislav Negodin, Ekaterina Enikeeva, Iana Matrosova, Nikolay Karpachev, Max Ryabinin

Pre-Storage Reasoning for Episodic Memory: Shifting Inference Burden to Memory for Personalized Dialogue (2025.findings-emnlp)

Challenge:	Currently, traditional evaluation methods struggle to detect subtle translation errors.
Approach:	They propose to use a dataset of human evaluations for English–Russian translations created by professional linguists to enable consistent and rich annotation.
Outcome:	The proposed protocol allows expert assessments without time pressure to yield substantially different results from standard evaluations.

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Sangyeop Kim, Yohan Lee, Sanghwa Kim, Hyunjong Kim, Sungzoon Cho

Temporal Consistency for LLM Reasoning Process Error Identification (2025.findings-emnlp)

Challenge:	Existing models place excessive reasoning burden on response generation, causing performance degradation . we present a new approach that shifts complex reasoning processes from inference to memory construction .
Approach:	a new approach shifts complex reasoning processes from inference to memory construction . PREMem extracts fine-grained memory fragments and establishes explicit relationships . small models achieve comparable results even with limited token budgets .
Outcome:	The proposed approach reduces the computational burden of response generation by capturing evolution patterns in memory items.

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Jiacheng Guo, Yue Wu, Jiahao Qiu, Kaixuan Huang, Xinzhe Juan, Ling Yang, Mengdi Wang

Quantifying Compositionality of Classic and State-of-the-Art Embeddings (2025.findings-emnlp)

Challenge:	Empirical evaluations show consistent performance improvements over baseline methods . 7B/8B distilled models outperform all 70B/72B models and GPT-4o on ProcessBench .
Approach:	They propose a temporal consistency method that leverages consistency in a sequence of self-reflection actions to improve verification accuracy.
Outcome:	The proposed method outperforms existing methods on three benchmarks . it leverages consistency in a sequence of self-reflection actions to improve accuracy .

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Zhijin Guo, Chenhao Xue, Zhaozhen Xu, Hongbo Bo, Yuxuan Ye, Janet B. Pierrehumbert, Martha Lewis

Presumed Cultural Identity: How Names Shape LLM Responses (2025.findings-emnlp)

Challenge:	Static word embeddings make strong claims about compositionality, but the SOTA generative models go too far in the other direction.
Approach:	a new study evaluates the compositionality of word embeddings by canonical correlation analysis . strong compositional signals are observed in later training stages across data modalities .
Outcome:	a new evaluation of compositional models shows that they exploit access meanings when justified . strong compositional signals are observed in later training stages and in deeper layers of the transformer-based model before a decline at the top layer.

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Siddhesh Milind Pawar, Arnav Arora, Lucie-Aimée Kaffee, Isabelle Augenstein

I-GUARD: Interpretability-Guided Parameter Optimization for Adversarial Defense (2025.findings-emnlp)

Challenge:	Names can be used as markers of individuality, cultural heritage, and personal history when interacting with chatbots.
Approach:	They propose to use names as cultural bias in chatbots to adapt to user input and task contexts.
Outcome:	The proposed method demonstrates that LLMs make cultural identity assumptions based on their users’ presumed backgrounds based upon their names .

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Mamta Mamta, Oana Cocarascu

DecoupledESC: Enhancing Emotional Support Generation via Strategy-Response Decoupled Preference Optimization (2025.findings-emnlp)

Challenge:	I-Guard leverages model interpretability to identify influential parameters responsible for adversarial misclassifications.
Approach:	They propose a framework to increase the robustness of transformer-based models against adversarial attacks.
Outcome:	The proposed framework improves model robustness against adversarial attacks.

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Chao Zhang, Xin Shi, Xueqiao Zhang, Yifan Zhu, Yi Yang, Yawei Luo

Local Normalization Distortion and the Thermodynamic Formalism of Decoding Strategies for Large Language Models (2025.findings-emnlp)

Challenge:	Existing ESC data entangles psychological strategies and response content, making it difficult to construct high-quality preference pairs.
Approach:	They propose a Decoupled ESC framework that decomposes the ESC task into two sequential subtasks: strategy planning and empathic response generation.
Outcome:	The proposed framework outperforms baselines, reducing preference bias and improving response quality.

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Tom Kempton, Stuart Burrell

BRIT: Bidirectional Retrieval over Unified Image-Text Graph (2025.findings-emnlp)

Challenge:	Existing decoding strategies for language models are based on heuristics and are difficult to improve.
Approach:	They propose a theory of decoding strategies for language models by expressing popular decoding algorithms as equilibrium states and stating objective functions they optimize.
Outcome:	The proposed method is based on ergodic theory and is difficult to improve . it shows that local normalization distortion is a fundamental defect of decoding strategies .

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Ainulla Khan, Moyuru Yamada, Srinidhi Akella

ReTAG: Retrieval-Enhanced, Topic-Augmented Graph-Based Global Sensemaking (2025.findings-emnlp)

Challenge:	RAG is a promising technique to enhance the quality and relevance of responses generated by large language models.
Approach:	They propose a multi-modal RAG framework that unifies various text-image connections in a document into a graph and retrieves the texts and images as a query-specific sub-graph.
Outcome:	The proposed framework unifies various text-image connections into a multi-modal graph and retrieves the images and texts as a query-specific sub-graph.

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Boyoung Kim, Dosung Lee, Sumin An, Jinseong Jeong, Paul Hongsuck Seo

Capturing Latent Modal Association For Multimodal Entity Alignment (2025.findings-emnlp)

Challenge:	a prior graph-based approach to global sensemaking lacks retrieval mechanisms, topic specificity, and incurs high inference costs.
Approach:	They propose a RetrievalEnhanced, Topic-Augmented Graph framework that retrieves relevant summaries from a topic.
Outcome:	The proposed framework improves response quality while significantly reducing inference time compared to the baseline.

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Yongquan Ji, Jingwei Cheng, Fu Zhang, Chenglong Lu

Explaining novel senses using definition generation with open language models (2025.findings-emnlp)

Challenge:	Existing methods for multimodal entity alignment overlook the quality of input modality embeddings during modality interaction, amplifying noise propagation while suppressing discriminative feature representations.
Approach:	They propose a model for capturing latent modal association for multimodal entity alignment using a self-attention mechanism to enhance salient information while attenuating noise within individual modality embeddings.
Outcome:	The proposed model achieves an absolute 3.1% higher Hits@1 score than the sota method.

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Mariia Fedorova, Andrey Kutuzov, Francesco Periti, Yves Scherrer

Can Code-Switched Texts Activate a Knowledge Switch in LLMs? A Case Study on English-Korean Code-Switching (2025.findings-emnlp)

Challenge:	We apply definition generators based on open-weights large language models to create explanations of novel senses.
Approach:	They apply open-weights large language models to create explanations of novel senses using target word usages as input.
Outcome:	The proposed definition generators perform on par with decoder-only models.

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Seoyeon Kim, Huiseo Kim, Chanjun Park, Jinyoung Yeo, Dongha Lee

Compositional Translation: A Novel LLM-based Approach for Low-resource Machine Translation (2025.findings-emnlp)

Challenge:	Recent large language models (LLMs) demonstrate multilingual abilities, yet they are English-centric due to dominance of English in training corpora.
Approach:	They propose to use a synthetic English-korean CS question-answering dataset to investigate this potential.
Outcome:	The proposed model can activate, identify and leverage knowledge for reasoning in low-resource languages.

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Armel Randy Zebaze, Benoît Sagot, Rachel Bawden

TopXGen: Topic-Diverse Parallel Data Generation for Low-Resource Machine Translation (2025.findings-emnlp)

Challenge:	generative large language models (LLMs) can perform in-context learning . machine translation (MT) has been shown to benefit from in-constitu examples .
Approach:	They propose a compositional translation paradigm that replaces naive few-shot MT with similarity-based demonstrations.
Outcome:	The proposed paradigm replaces naive few-shot MT with similarity-based demonstrations.

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Armel Randy Zebaze, Benoît Sagot, Rachel Bawden

Fast, Not Fancy: Rethinking G2P with Rich Data and Statistical Models (2025.findings-emnlp)

Challenge:	In-context learning and similarity search have been shown to improve LLMs' performance in machine translation, but they lag behind when dealing with low-resource languages.
Approach:	They propose a method that uses an LLM to generate topic-specific target-side data in the LRL.
Outcome:	The proposed approach boosts LLM translation performance during in-context learning and fine-tuning.

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Mahta Fetrat Qharabagh, Zahra Dehghanian, Hamid R. Rabiee

Personalized open world plan generation for safety-critical human centered autonomous systems: A case study on Artificial Pancreas (2025.findings-emnlp)

Challenge:	specific disambiguation strategies introduce additional latency, making them unsuitable for real-time applications.
Approach:	They propose a semi-automated pipeline for constructing homograph-focused datasets . they introduce a HomoRich dataset and advocate for a paradigm shift .
Outcome:	The proposed pipeline improves a state-of-the-art deep learning-based G2P system for Persian.

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Ayan Banerjee, Sandeep Gupta

Challenge:	Design-time safety guarantees for human-centered autonomous systems often break down in open-world deployment due to uncertain human interaction.
Approach:	They propose an LLM-based architecture that automatically generates personalized safety plans . by itself, the LLM fares poorly at producing safe usage plans, but coupling it with a safety verifier enables the discovery of safe plans.
Outcome:	The proposed architecture generates personalized safety plans that are safe for open-world use . the proposed architecture fares poorly at producing safe usage plans .

CaMMT: Benchmarking Culturally Aware Multimodal Machine Translation (2025.findings-emnlp)

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Emilio Villa-Cueva, Sholpan Bolatzhanova, Diana Turmakhan, Kareem Elzeky, Henok Biadglign Ademtew, Alham Fikri Aji, Vladimir Araujo, Israel Abebe Azime, Jinheon Baek, Frederico Belcavello, Fermin Cristobal, Jan Christian Blaise Cruz, Mary Dabre, Raj Dabre, Toqeer Ehsan, Naome A Etori, Fauzan Farooqui, Jiahui Geng, Guido Ivetta, Thanmay Jayakumar, Soyeong Jeong, Zheng Wei Lim, Aishik Mandal, Sofía Martinelli, Mihail Minkov Mihaylov, Daniil Orel, Aniket Pramanick, Sukannya Purkayastha, Israfel Salazar, Haiyue Song, Tiago Timponi Torrent, Debela Desalegn Yadeta, Injy Hamed, Atnafu Lambebo Tonja, Thamar Solorio

Training Text-to-Molecule Models with Context-Aware Tokenization (2025.findings-emnlp)

Challenge:	a human-curated benchmark of over 5,800 triples of images is used to evaluate multimodal translation systems.
Approach:	They introduce a human-curated benchmark of over 5,800 triples of images along with parallel captions in English and regional languages.
Outcome:	The results show that visual context improves translation quality in culturally-specific items .

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Seojin Kim, Hyeontae Song, Jaehyun Nam, Jinwoo Shin

Text Segmentation

Challenge:	Text-to-molecule models have shown great potential across chemical applications . however, they rely on atom-level tokenizations, which limiting the ability of models to capture global structural context within molecules.
Approach:	They propose a text-to-molecule model that uses substructure-level tokenizations to model global connectivity.
Outcome:	The proposed model outperforms state-of-the-art models using only 2% of training tokens.

Challenging the Evaluator: LLM Sycophancy Under User Rebuttal (2025.findings-emnlp)

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Sung Won Kim, Daniel Khashabi

Perspective-driven Preference Optimization with Entropy Maximization for Diverse Argument Generation (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) often exhibit sycophancy, distorting responses to align with user beliefs.
Approach:	They investigate why LLMs exhibit sycophancy when challenged in subsequent conversational turns, yet perform well when evaluating conflicting arguments presented simultaneously?
Outcome:	The proposed models are more likely to endorse a user’s counterargument when framed as a follow-up from a users, rather than when both responses are presented simultaneously for evaluation.

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Yilin Cao, Ruike Zhang, Penghui Wei, Qingchao Kong, Wenji Mao

Spoken Document Retrieval for an Unwritten Language: A Case Study on Gormati (2025.findings-emnlp)

Challenge:	Argument generation with diverse perspectives is essential for fostering balanced discourse and mitigating bias.
Approach:	They propose a Perspective-aware Preference Optimization with Entropy Maximization framework for diverse argument generation.
Outcome:	The proposed framework enhances perspective diversity through preference optimization based on the constructed preference dataset .

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Sanjay Booshanam, Kelly Chen, Ondrej Klejch, Thomas Reitmaier, Dani Kalarikalayil Raju, Electra Wallington, Nina Markl, Jennifer Pearson, Matt Jones, Simon Robinson, Peter Bell

M-Help: Using Social Media Data to Detect Mental Health Help-Seeking Signals (2025.findings-emnlp)

Challenge:	Speakers of unwritten languages have the potential to benefit from speech-based automatic information retrieval systems.
Approach:	They propose a speech embedding technique that facilitates a zero-shot speech-based automatic information retrieval system for unwritten languages.
Outcome:	The proposed method achieves a Top 5 retrieval rate of 87.9% on a corpus of Gormati, an unwritten language, that was collected in partnership with an agrarian Banjara community in Maharashtra State, India.

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Msvpj Sathvik, Zuhair Hasan Shaik, Vivek Gupta

Brittle Minds, Fixable Activations: Understanding Belief Representations in Language Models (2025.findings-emnlp)

Challenge:	Existing datasets for detecting mental health disorders do not identify individuals actively seeking help.
Approach:	This paper introduces a new social media dataset specifically designed to detect help-seeking behavior on social media.
Outcome:	The proposed dataset can detect help-seeking behavior on social media . it can address three key tasks: identifying help- seekkers, diagnosing mental health conditions .

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Matteo Bortoletto, Constantin Ruhdorfer, Lei Shi, Andreas Bulling

Golden Touchstone: A Comprehensive Bilingual Benchmark for Evaluating Financial Large Language Models (2025.findings-emnlp)

Challenge:	Despite growing interest in Theory of Mind (ToM) tasks for evaluating language models, little is known about how LMs internally represent mental states of self and others.
Approach:	They propose to investigate how LMs internally represent mental states of self and others .
Outcome:	The proposed model size and finetuning significantly improve LMs’ internal representations of others’ beliefs, which are structured - not mere by-products of spurious correlations - yet brittle to prompt variations.

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Xiaojun Wu, Junxi Liu, Huan-Yi Su, Zhouchi Lin, Yiyan Qi, Chengjin Xu, Jiajun Su, Jiajie Zhong, Fuwei Wang, Saizhuo Wang, Fengrui Hua, Jia Li, Jian Guo

Quantifying the Risks of LLM- and Tool-assisted Rephrasing to Linguistic Diversity (2025.findings-emnlp)

Challenge:	Existing financial benchmarks suffer from limited language and task coverage, low-quality datasets, and inadequate adaptability for LLM evaluation.
Approach:	They propose a bilingual benchmark for financial LLMs that assesses models’ language understanding and generation capabilities.
Outcome:	The proposed bilingual benchmark assesses models’ language understanding and generation capabilities.

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Mengying Wang, Andreas Spitz

Challenge:	Writing assistants and large language models are widely used in the creation of text content . little is known about their proclivity to change language or reduce its richness when adopted by a large user base.
Approach:	They propose to measure the semantic and vocabulary change enacted by rephrasing tools on a corpus of human-generated text.
Outcome:	The proposed tool reduces the richness of a large text corpus by reducing the risk of language change.

NUMINA: A Natural Understanding Benchmark for Multi-dimensional Intelligence and Numerical Reasoning Abilities (2025.findings-emnlp)

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Changyu Zeng, Yifan Wang, Zimu Wang, Wei Wang, Zhengni Yang, Muyi Bao, Jimin Xiao, Anh Nguyen, Yutao Yue

MoMentS: A Comprehensive Multimodal Benchmark for Theory of Mind (2025.findings-emnlp)

Challenge:	Existing 3D benchmarks lack fine-grained numerical reasoning task annotations, limiting MLLMs’ ability to perform precise spatial measurements and complex numerical reasoning.
Approach:	They propose a 3D-based benchmark to enhance indoor perceptual understanding by using multi-scale annotations and question-answer pairs.
Outcome:	The proposed benchmark improves indoor perceptual understanding by incorporating multi-scale annotations and question-answer pairs.

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Emilio Villa-Cueva, S M Masrur Ahmed, Rendi Chevi, Jan Christian Blaise Cruz, Kareem Elzeky, Fermin Cristobal, Alham Fikri Aji, Skyler Wang, Rada Mihalcea, Thamar Solorio

Code Like Humans: A Multi-Agent Solution for Medical Coding (2025.findings-emnlp)

Challenge:	MoMentS is a benchmark designed to assess the ToM capabilities of multimodal large language models (LLMs) in short films.
Approach:	They introduce a benchmark to assess the ToM capabilities of multimodal large language models (LLMs) through realistic, narrative-rich scenarios presented in short films.
Outcome:	The proposed benchmark features long video context windows and realistic social interactions that provide deeper insight into characters’ mental states.

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Andreas Geert Motzfeldt, Joakim Edin, Casper L. Christensen, Christian Hardmeier, Lars Maaløe, Anna Rogers

Challenge:	In medical coding, experts map unstructured clinical notes to alphanumeric codes for diagnoses and procedures.
Approach:	They introduce ‘Code Like Humans’: a new agentic framework for medical coding with large language models that implements official coding guidelines for human experts.
Outcome:	The proposed framework implements official coding guidelines for human experts and can support the full ICD-10 coding system (+70K labels).

Can Out-of-Distribution Evaluations Uncover Reliance on Prediction Shortcuts? A Case Study in Question Answering (2025.findings-emnlp)

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Michal Štefánik, Timothee Mickus, Michal Spiegel, Marek Kadlčík, Josef Kuchař

MEXA: Towards General Multimodal Reasoning with Dynamic Multi-Expert Aggregation (2025.findings-emnlp)

Challenge:	Existing work assesses models’ generalization capabilities through the lens of performance on out-of-distribution (OOD) datasets.
Approach:	They challenge this assumption by comparing OOD evaluations with failure modes documented in existing question-answering (QA) models.
Outcome:	The proposed evaluations show that the models' generalization capabilities are under-performing on out-of-distribution datasets, while others are underperforming on in-difference datasets.

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Shoubin Yu, Yue Zhang, Ziyang Wang, Jaehong Yoon, Mohit Bansal

Lifelong Knowledge Editing requires Better Regularization (2025.findings-emnlp)

Challenge:	MEXA is a training-free framework that performs modality- and task-aware aggregation of multiple expert models to enable effective multimodal reasoning across diverse domains.
Approach:	MEXA is a training-free framework that performs modality- and task-aware aggregation of multiple expert models.
Outcome:	MEXA performs modality- and task-aware aggregation of multiple expert models . it generates interpretable textual reasoning outputs and reasons over them using a Large Reasoning Model (LRM) MEX A consistently delivers performance improvements over strong multimodal benchmarks .

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Akshat Gupta, Phudish Prateepamornkul, Maochuan Lu, Ahmed Alaa, Thomas Hartvigsen, Gopala Anumanchipalli

Lost in Embeddings: Information Loss in Vision–Language Models (2025.findings-emnlp)

Challenge:	Knowledge editing is a promising way to improve factuality in large language models, but recent studies have shown significant model degradation during sequential editing.
Approach:	They formalize locate-then-edit methods as a two-step fine-tuning process . they show that model degradation occurs due to over-optimization of internal activations .
Outcome:	The proposed methods reduce time and improve factuality by 42-61%.

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Wenyan Li, Raphael Tang, Chengzu Li, Caiqi Zhang, Ivan Vulić, Anders Søgaard

Assessing the Role of Data Quality in Training Bilingual Language Models (2025.findings-emnlp)

Challenge:	Experiments reveal connectors substantially distort the local geometry of visual representations, with k-nearest neighbors diverging by 40–60% post-projection, correlating with degradation in retrieval performance.
Approach:	They propose two approaches to examine and quantify information loss by analyzing latent representation space.
Outcome:	The proposed model improves retrieval performance by analyzing changes in k-nearest neighbor relationships between image representations before and after projection.

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Skyler Seto, Maartje Ter Hoeve, Maureen de Seyssel, David Grangier

DORM: Preference Data Weights Optimization for Reward Modeling in LLM Alignment (2025.findings-emnlp)

Challenge:	a recent study shows that adding more languages can degrade performance for some languages while improving others.
Approach:	They propose a data filtering strategy to select high-quality bilingual training data with only high quality English data.
Outcome:	The proposed approach improves bilingual model performance by 2–4% and reduces bilingual models performance gaps to 1%.

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Rongzhi Zhang, Chenwei Zhang, Xinyang Zhang, Liang Qiu, Haoming Jiang, Yuchen Zhuang, Qingru Zhang, Hyokun Yun, Xian Li, Bing Yin, Tuo Zhao, Chao Zhang

Challenge:	Existing approaches to align large language models with human preferences are noisy and varying in importance of preference samples.
Approach:	a new method enhances reward modeling by learning to dynamically weigh preference data.
Outcome:	a new method improves the performance of large language models with human preferences . it initializes data importance and iteratively refines them to maximize validation performance.

Enhancing Domain-Specific Encoder Models with LLM-Generated Data: How to Leverage Ontologies, and How to Do Without Them (2025.findings-emnlp)

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Marc Felix Brinner, Tarek Al Mustafa, Sina Zarrieß

Aligning Dialogue Agents with Global Feedback via Large Language Model Multimodal Reward Decomposition (2025.findings-emnlp)

Challenge:	a new method for continual pretraining transformer encoder models is proposed for specialized domains with limited training data.
Approach:	They propose to use LLM-generated data to enrich domain-specific ontologies and pretrain transformer encoder models as an ontology-informed embedding model for concept definitions.
Outcome:	The proposed method improves on standard MLM pretraining on invasion biology domains.

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Dong Won Lee, Hae Won Park, Cynthia Breazeal, Louis-Philippe Morency

UrduFactCheck: An Agentic Fact-Checking Framework for Urdu with Evidence Boosting and Benchmarking (2025.findings-emnlp)

Challenge:	a large language model is used to decompose global feedback into a lightweight reward model.
Approach:	They propose a large language model based reward decomposition framework for dialogue agents . they use a frozen large language modeling framework to decompose global feedback .
Outcome:	The proposed framework infers fine-grained local rewards from a single session-level feedback signal.

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Sarfraz Ahmad, Hasan Iqbal, Momina Ahsan, Numaan Naeem, Muhammad Ahsan Riaz Khan, Arham Riaz, Muhammad Arslan Manzoor, Yuxia Wang, Preslav Nakov

Echoes of Agreement: Argument Driven Sycophancy in Large Language models (2025.findings-emnlp)

Challenge:	Existing automated fact-checking systems are predominantly developed for English . Existing systems focus on claim verification, but UrduFactQA targets factuality .
Approach:	They propose two hand-annotated benchmarks to enable fact-checking and factual consistency evaluation in Urdu.
Outcome:	The proposed benchmarks are the first of their kind for Urdu and are available online.

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Avneet Kaur

Rethinking NLP for Chemistry: A Critical Look at the USPTO Benchmark (2025.findings-emnlp)

Challenge:	Existing evaluations of political biases in Large Language Models outline the high sensitivity to prompt formulation.
Approach:	They investigate how argumentative prompts induce sycophantic behaviour in Large Language Models in a political context.
Outcome:	The proposed model sycophancy is observed in single and multi-turn interactions and its intensity correlates with argument strength.

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Derin Ozer, Nicolas Gutowski, Benoit Da Mota, Thomas Cauchy, Sylvain Lamprier

Challenge:	Natural Language Processing (NLP) has revolutionized computer-aided synthesis planning by reframing chemical synthesis prediction as a sequence-to-sequence modeling problem over molecular string representations like SMILES.
Approach:	They propose to reframe chemical synthesis prediction as a sequence-to-sequence modeling problem over molecular string representations like SMILES.
Outcome:	The proposed framework yields impressive benchmark scores on the USPTO dataset, a large corpus of reactions extracted from US patents.

Investigating Dictionary Expansion for Video-based Sign Language Dictionaries (2025.findings-emnlp)

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Aashaka Desai, Daniela Massiceti, Richard Ladner, Hal Daumé Iii, Danielle Bragg, Alex Xijie Lu

From Insight to Exploit: Leveraging LLM Collaboration for Adaptive Adversarial Text Generation (2025.findings-emnlp)

Challenge:	Currently, most dictionary retrieval methods only work with fixed vocabularies, and it is unclear how they might support dictionary expansion without retraining.
Approach:	They propose to use a representation-based method to explore the feasibility of dictionary expansion for sign language dictionaries.
Outcome:	The proposed method improves sign language dictionaries by varying number of signs added and amount of data for newly added signs.

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Najrin Sultana, Md Rafi Ur Rashid, Kang Gu, Shagufta Mehnaz

Beyond Contrastive Learning: Synthetic Data Enables List-wise Training with Multiple Levels of Relevance (2025.findings-emnlp)

Challenge:	LLMs can provide substantial zero-shot performance on diverse tasks, but it is crucial to assess their robustness against adversarial inputs.
Approach:	They introduce Static Deceptor and Dynamic Deceptr to generate adversarial examples . they produce subtle and natural-looking adversarials that preserve semantic similarity to text .
Outcome:	The proposed attacks are based on two LLM-based attacks that generate natural-looking examples that deceive the target LLM.

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Reza Esfandiarpoor, George Zerveas, Ruochen Zhang, Macton Mgonzo, Carsten Eickhoff, Stephen Bach

Instability in Downstream Task Performance During LLM Pretraining (2025.findings-emnlp)

Challenge:	a new approach to training with binary relevance labels uses synthetic data . contrastive learning with binary correlations leaves out subtle nuances useful for ranking .
Approach:	They propose to use waterstein distance as a loss function for training transformer-based retrievers with graduated relevance labels instead of real documents.
Outcome:	The proposed method outperforms conventional training with InfoNCE by a large margin on MARCO and BEIR benchmarks without using real documents.

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Yuto Nishida, Masaru Isonuma, Yusuke Oda

A Comparison of Independent and Joint Fine-tuning Strategies for Retrieval-Augmented Generation (2025.findings-emnlp)

Challenge:	a study of large language models shows that task scores fluctuate throughout training .
Approach:	They empirically analyze the stability of downstream task performance in an LLM .
Outcome:	The proposed methods improve performance stability without changes to the training procedure.

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Neal Gregory Lawton, Alfy Samuel, Anoop Kumar, Daben Liu

mrCAD: Multimodal Communication to Refine Computer-aided Designs (2025.findings-emnlp)

Challenge:	Multiple fine-tuning strategies exist with different costs and benefits for RAG pipelines.
Approach:	They evaluate several RAG fine-tuning strategies with different costs and benefits . embedding and generator models can be fine- tuned to increase performance .
Outcome:	The proposed techniques improve quality metrics, but have different computational costs.

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William P McCarthy, Saujas Vaduguru, Karl D.d. Willis, Justin Matejka, Judith E Fan, Daniel Fried, Yewen Pu

MOCHA: Are Code Language Models Robust Against Multi-Turn Malicious Coding Prompts? (2025.findings-emnlp)

Challenge:	generative AI excels at creating artifacts in a single turn but can struggle to make precise refinements that match our design intent.
Approach:	They propose to use multi-turn interactions to iterate and refine computer-aided designs (CADs) they use text and drawing to communicate with each other over multiple rounds of interaction .
Outcome:	mrCAD consists of 6,082 communication games, 15,163 instruction-execution rounds, played between 1,092 pairs of humans.

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Muntasir Wahed, Xiaona Zhou, Kiet A. Nguyen, Tianjiao Yu, Nirav Diwan, Gang Wang, Dilek Hakkani-Tür, Ismini Lourentzou

How Can Input Reformulation Improve Tool Usage Accuracy in a Complex Dynamic Environment? A Study on tau-bench (2025.findings-emnlp)

Challenge:	Recent advances in Large Language Models have significantly enhanced their code generation capabilities, but their robustness against adversarial misuse remains underexplored.
Approach:	They introduce a code decomposition attack where a malicious coding task is broken down into subtasks across multiple conversational turns to evade safety filters.
Outcome:	The proposed code decomposition attacks exploits multi-turn malicious coding prompts . the proposed model improves rejection rates while preserving coding ability .

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Venkatesh Mishra, Amir Saeidi, Satyam Raj, Mutsumi Nakamura, Gaowen Liu, Ali Payani, Jayanth Srinivasa, Chitta Baral

Evaluating Fairness in Large Vision-Language Models Across Diverse Demographic Attributes and Prompts (2025.findings-emnlp)

Challenge:	Recent advances in reasoning and planning capabilities of large language models have enabled their potential as autonomous agents capable of tool use in dynamic environments.
Approach:	They propose an input-reformulation multi-agent framework that reformulates user queries .
Outcome:	The proposed framework outperforms ReAct, Function Calling, and Self-Reflection in overall pass5 scores.

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Xuyang Wu, Yuan Wang, Hsin-Tai Wu, Zhiqiang Tao, Yi Fang

VIBE: Can a VLM Read the Room? (2025.findings-emnlp)

Challenge:	Large vision-language models have demonstrated strong capabilities in open-world visual understanding, but it is not clear how they address demographic biases in real life.
Approach:	They propose a method to assess visual fairness in LVLMs by question-answering/classification tasks.
Outcome:	The proposed approach improves transparency and offers a scalable solution for fairness mitigation.

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Tania Chakraborty, Eylon Caplan, Dan Goldwasser

LoRATK: LoRA Once, Backdoor Everywhere in the Share-and-Play Ecosystem (2025.findings-emnlp)

Challenge:	Vision Language Models (LLMs) cannot account for the role that non-verbal cues play in understanding social situations.
Approach:	They propose a task to test the capabilities of Vision Language Models (VLMs) to account for the visual social-pragmatic inference gap.
Outcome:	The proposed task tests the capabilities of a VLM for a social reasoning task.

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Hongyi Liu, Shaochen Zhong, Xintong Sun, Minghao Tian, Mohsen Hariri, Zirui Liu, Ruixiang Tang, Zhimeng Jiang, Jiayi Yuan, Yu-Neng Chuang, Li Li, Soo-Hyun Choi, Rui Chen, Vipin Chaudhary, Xia Hu

Pearl: A Multimodal Culturally-Aware Arabic Instruction Dataset (2025.findings-emnlp)

Challenge:	distributing LLMs without a proven track record like ‘meta-llama‘ or ‘qwen‘ rarely gains community traction.
Approach:	They propose a simple, efficient, yet specific recipe for a backdoor LoRA to be injected into task-enhancing LoRAs and examine the mechanisms of such infections.
Outcome:	The proposed model allows attackers to scale the distribution of compromised LoRAs with minimal effort by leveraging the rich pool of shared LoRA assets.

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Fakhraddin Alwajih, Samar M. Magdy, Abdellah El Mekki, Omer Nacar, Youssef Nafea, Safaa Taher Abdelfadil, Abdulfattah Mohammed Yahya, Hamzah Luqman, Nada Almarwani, Samah Aloufi, Baraah Qawasmeh, Houdaifa Atou, Serry Sibaee, Hamzah A. Alsayadi, Walid Al-Dhabyani, Maged S. Al-shaibani, Aya El aatar, Nour Qandos, Rahaf Alhamouri, Samar Ahmad, Mohammed Anwar AL-Ghrawi, Aminetou Yacoub, Ruwa AbuHweidi, Vatimetou Mohamed Lemin, Reem Abdel-Salam, Ahlam Bashiti, Adel Ammar, Aisha Alansari, Ahmed Ashraf, Nora Alturayeif, Alcides Alcoba Inciarte, AbdelRahim A. Elmadany, Mohamedou Cheikh Tourad, Ismail Berrada, Mustafa Jarrar, Shady Shehata, Muhammad Abdul-Mageed

Protein Large Language Models: A Comprehensive Survey (2025.findings-emnlp)

Challenge:	Mainstream large vision-language models (LVLMs) inherently encode cultural biases, highlighting the need for diverse multimodal datasets.
Approach:	They propose to construct a large-scale Arabic multimodal dataset and benchmark explicitly designed for cultural understanding.
Outcome:	The proposed dataset covers ten culturally significant domains covering all Arab countries and includes two evaluation benchmarks (PEARL and PEARL-LITE) and a specialized subset (PearL-X).

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Yijia Xiao, Wanjia Zhao, Junkai Zhang, Yiqiao Jin, Han Zhang, Zhicheng Ren, Renliang Sun, Haixin Wang, Guancheng Wan, Pan Lu, Xiao Luo, Yu Zhang, James Zou, Yizhou Sun, Wei Wang

MAKIEval: A Multilingual Automatic WiKidata-based Framework for Cultural Awareness Evaluation for LLMs (2025.findings-emnlp)

Challenge:	Existing studies focus on specific aspects or applications, but this study provides a comprehensive overview of Protein-specific large language models.
Approach:	This paper proposes a structured taxonomy of state-of-the-art ProteinLLMs . they analyze how they leverage large-scale protein sequence data for improved accuracy .
Outcome:	The proposed model covers their architectures, training datasets, evaluation metrics, and diverse applications.

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Raoyuan Zhao, Beiduo Chen, Barbara Plank, Michael A. Hedderich

Looking Beyond the Pixels: Evaluating Visual Metaphor Understanding in VLMs (2025.findings-emnlp)

Challenge:	Large language models (LLMs) are used globally across many languages, but their English-centric pretraining raises concerns about cross-lingual disparities for cultural awareness .
Approach:	They introduce an automatic multilingual framework for evaluating cultural awareness in large language models across languages, regions, and topics.
Outcome:	The framework evaluates open-ended text generation, capturing how models express culturally grounded knowledge in natural language.

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Manishit Kundu, Sumit Shekhar, Pushpak Bhattacharyya

AGENTVIGIL: Automatic Black-Box Red-teaming for Indirect Prompt Injection against LLM Agents (2025.findings-emnlp)

Challenge:	Visual metaphors are a complex vision–language phenomenon that requires both perceptual and conceptual reasoning to understand.
Approach:	They introduce a visual metaphor dataset featuring 2177 synthetic and 350 human-annotated images and benchmark several SOTA VLMs on two tasks: Visual Metaphor Captioning (VMC) and Visual Metamorphosis VQA (VM-VQA).
Outcome:	The proposed model outperforms standard few-shot baselines on visual metaphors and VM-VQA tasks.

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Zhun Wang, Vincent Siu, Zhe Ye, Tianneng Shi, Yuzhou Nie, Xuandong Zhao, Chenguang Wang, Wenbo Guo, Dawn Song

Improving LLM-as-a-Judge Inference with the Judgment Distribution (2025.findings-emnlp)

Challenge:	AGENTVIGIL is a black-box optimization framework to exploit indirect prompt injection vulnerabilities . indirect prompts compromise the core of LLM agents by manipulating contextual information rather than direct user prompts.
Approach:	They propose a black-box optimization framework to exploit indirect prompt injection vulnerabilities . they use a Monte Carlo tree-based algorithm to iteratively refine inputs .
Outcome:	The proposed framework achieves 71% and 70% success rates against two public benchmarks .

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Victor Wang, Michael JQ Zhang, Eunsol Choi

Challenge:	Using language models to scalably approximate human preferences on text quality (LLM-as-a-judge) is a standard practice applicable to many tasks.
Approach:	They propose to use LLM judges to approximate human preferences on text quality by using distributional output over judgment tokens.
Outcome:	The proposed method outperforms taking the mode (i.e. greedy decoding) in all evaluation settings, and incorporating risk aversion improves performance.

Learning Is Not A Race: Improving Retrieval in Language Models via Equal Learning (2025.findings-emnlp)

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Wanqian Yang, Aahlad Manas Puli, Rajesh Ranganath

The Prompt Makes the Person(a): A Systematic Evaluation of Sociodemographic Persona Prompting for Large Language Models (2025.findings-emnlp)

Challenge:	Overparametrized models trained on cross-entropy loss can overfit on noise . Fitting some tokens early reduces gradient signals in later iterations .
Approach:	They propose to overfit models trained on cross-entropy loss on noise . fitting some tokens early reduces gradient signals in later iterations .
Outcome:	The proposed approaches can be applied to large language models with longer contexts or larger embedding sizes.

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Marlene Lutz, Indira Sen, Georg Ahnert, Elisa Rogers, Markus Strohmaier

Spiral of Silence in Large Language Model Agents (2025.findings-emnlp)

Challenge:	persona prompting is increasingly used in large language models to simulate views of various sociodemographic groups.
Approach:	They use open-source LLMs to study how persona prompts influence LLM simulations . they use role adoption formats and demographic priming strategies to study marginalized groups .
Outcome:	The results show that the choice of demographic priming and role adoption strategy significantly impacts their portrayal.

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Mingze Zhong, Meng Fang, Zijing Shi, Yuxuan Huang, Shunfeng Zheng, Yali Du, Ling Chen, Jun Wang

Do We Know What LLMs Don’t Know? A Study of Consistency in Knowledge Probing (2025.findings-emnlp)

Challenge:	Existing theories of Spiral of Silence do not apply to large language models .
Approach:	They propose an evaluation framework for examining SoS in large language models . they consider four controlled conditions that vary the availability of "History" and "Persona" signals .
Outcome:	The proposed framework examines the SoS-like dynamics in large language models . it shows that history and persona together produce strong majority dominance .

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Raoyuan Zhao, Abdullatif Köksal, Ali Modarressi, Michael A. Hedderich, Hinrich Schuetze

Context Length Alone Hurts LLM Performance Despite Perfect Retrieval (2025.findings-emnlp)

Challenge:	Existing methods for probing knowledge gaps in large language models are inconsistent and inconsistent.
Approach:	They propose a process based on input variations and quantitative metrics to evaluate probing methods that are inconsistent on knowledge gaps.
Outcome:	The proposed process exposes two dimensions of inconsistency in knowledge gap probing.

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Yufeng Du, Minyang Tian, Srikanth Ronanki, Subendhu Rongali, Sravan Babu Bodapati, Aram Galstyan, Azton Wells, Roy Schwartz, Eliu A Huerta, Hao Peng

DebUnc: Improving Large Language Model Agent Communication With Uncertainty Metrics (2025.findings-emnlp)

Challenge:	Large language models (LLMs) often fail to scale their performance on long-context tasks performance in line with the context lengths they support.
Approach:	They propose a model-agnostic mitigation strategy that transforms a long-context task into a short-concept one by prompting the model to recite the retrieved evidence before attempting to solve the problem.
Outcome:	The proposed model improves on a long-context task up to 4% on RULER.

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Luke Yoffe, Alfonso Amayuelas, William Yang Wang

ProcVQA: Benchmarking the Effects of Structural Properties in Mined Process Visualizations on Vision–Language Model Performance (2025.findings-emnlp)

Challenge:	Multi-agent debates can improve the accuracy of Large Language Models by having multiple agents discuss solutions over several rounds of debate.
Approach:	a debate framework that uses uncertainty metrics to assess agent confidence is proposed . the framework uses textual prompts or a modified attention mechanism that adjusts token weights .
Outcome:	The proposed framework assesses agent confidence using uncertainty metrics . the framework is available at https://github.com/lukeyoffe/debunc.

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Kazi Tasnim Zinat, Saad Mohammad Abrar, Shoumik Saha, Sharmila Duppala, Saimadhav Naga Sakhamuri, Zhicheng Liu

Probing Political Ideology in Large Language Models: How Latent Political Representations Generalize Across Tasks (2025.findings-emnlp)

Challenge:	Vision-Language Models have shown impressive capabilities and notable failures in data visualization understanding tasks.
Approach:	They propose a benchmark to analyze how specific properties within a visualization type affect VLM performance.
Outcome:	The proposed benchmark examines how specific properties affect VLM performance . it shows that models exhibit steep drops on multi-hop reasoning and extraction errors increase with edge density .

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Tianyi Zhang

Understanding GUI Agent Localization Biases through Logit Sharpness (2025.findings-emnlp)

Challenge:	Large language models encode rich internal representations of political ideology, but it remains unclear how these representations contribute to model decision-making.
Approach:	They apply inference-time interventions to steer a decoder-only transformer along learned ideological directions . they find that learned ideological representations generalize well to bias detection, but not as well to voting simulations .
Outcome:	The proposed model steers a transformer along learned ideological directions . political bias detection, voting preference simulation and bias neutralization are tested .

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Xingjian Tao, Yiwei Wang, Yujun Cai, Zhicheng Yang, Jing Tang

Challenge:	Multimodal large language models often exhibit hallucinations that compromise reliability . despite promising performance, these models often display systematic localization errors .
Approach:	They propose a framework that categorizes model predictions into four distinct types . they propose metric that evaluates alignment between semantic continuity and logits distribution .
Outcome:	The proposed framework categorizes model predictions into four different types . it reveals nuanced failure modes beyond traditional accuracy metrics .

The Language of Interoception: Examining Embodiment and Emotion Through a Corpus of Body Part Mentions (2025.findings-emnlp)

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Sophie Wu, Jan Philip Wahle, Saif M. Mohammad

HomoGraphAdapter: A Homogeneous Graph Neural Network as an Effective Adapter for Vision-Language Models (2025.findings-emnlp)

Challenge:	5% to 10% of posts include body part mentions in English text . text containing BPMs tends to be more emotionally charged, even when the BPM is not used to describe a physical reaction to the emotion in the text.
Approach:	They create corpora of body part mentions in online English text with human annotations for the emotions of the person whose body part is mentioned.
Outcome:	The proposed study is the first to investigate the connection between emotion, embodiment, and everyday language in a large sample of natural language data.

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Chuan He, Zhuozhao Li, Song Guo, Xiaocheng Lu, Jinxiang Lai

No Black Boxes: Interpretable and Interactable Predictive Healthcare with Knowledge-Enhanced Agentic Causal Discovery (2025.findings-emnlp)

Challenge:	Existing adaptation methods overlook structural knowledge between text and image modalities or create overly complex graphs containing redundant information for alignment.
Approach:	They propose a method to adapt visual models to downstream tasks using text and image modalities.
Outcome:	The proposed method improves classification accuracy by 1.51% for 1-shot and 0.74% for 16-shot on 11 datasets.

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Xiaoxue Han, Pengfei Hu, Chang Lu, Jun-En Ding, Feng Liu, Yue Ning

PROOD: A Simple LLM Out-of-Distribution Guardrail Leveraging Response Semantics (2025.findings-emnlp)

Challenge:	Deep learning models lacking interpretability and interactivity, authors say . lack of interactive mechanisms prevents clinicians from incorporating their own knowledge into decision-making process.
Approach:	a new deep learning model is proposed to improve interpretability and interactivity . authors propose a knowledge-enhanced agent-driven causal discovery framework .
Outcome:	a new model improves interpretability and interactivity on EHR data . the proposed model improve interpretability through explicit reasoning and causal analysis .

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Joshua Tint

ICL-Bandit: Relevance Labeling in Advertisement Recommendation Systems via LLM (2025.findings-emnlp)

Challenge:	Existing OOD methods often struggle with deliberately obfuscated, context-dependent, or superficially benign prompts.
Approach:	They propose a framework that jointly analyzes LLM prompts and their outputs to improve semantic understanding.
Outcome:	The proposed framework outperforms existing OOD methods on three benchmarks and improves F1 scores by up to 6.3 points.

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Lu Wang, Chiming Duan, Pu Zhao, Fangkai Yang, Yong Shi, Xuefeng Luo, Bingjing Xu, Weiwei Deng, Qingwei Lin, Dongmei Zhang

Intent-aware Schema Generation and Refinement for Literature Review Tables (2025.findings-emnlp)

Challenge:	In-context learning (ICL) is a common practice to enhance LLM performance on domain-specific tasks.
Approach:	They propose a method that leverages large language models to enhance query-ad relevance labeling . they identify and provide superior demonstrations for ICL, thereby improving labeling performance .
Outcome:	The proposed method improves query-ad relevance labeling performance by providing demonstrations.

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Vishakh Padmakumar, Joseph Chee Chang, Kyle Lo, Doug Downey, Aakanksha Naik

NLP Needs Diversity outside of ‘Diversity’ (2025.findings-emnlp)

Challenge:	ambiguity in reference-based evaluations and lack of editing/refinement methods have slow progress on schema generation.
Approach:	They propose a method for augmenting unannotated table corpora with synthesized intents . they propose prompted workflows and fine-tuned models to improve schema generation .
Outcome:	The proposed approach significantly improves baseline performance in reconstructing reference schemas.

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Joshua Tint

Anatomy of a Feeling: Narrating Embodied Emotions via Large Vision-Language Models (2025.findings-emnlp)

Challenge:	a new position paper argues that diversity in NLP is concentrated on a small number of areas surrounding fairness .
Approach:	a new position paper argues that diversity in NLP is disproportionately concentrated on fairness areas.
Outcome:	a new position paper argues that diversity in NLP is disproportionately concentrated on fairness areas.

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Mohammad Saim, Phan Anh Duong, Cat Luong, Aniket Bhanderi, Tianyu Jiang

Towards Universal Debiasing for Language Models-based Tabular Data Generation (2025.findings-emnlp)

Challenge:	ELENA is a framework for embodied emotion analysis using large vision language models . ELEna uses attention maps and a persistent bias towards the facial region .
Approach:	They propose a framework that utilizes large vision language models to generate ELENA . they propose to use attention maps to describe emotional reactions from body parts .
Outcome:	The proposed framework outperforms baseline models without fine-tuning . it uses large vision language models to generate embodied emotion narratives .

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Tianchun Li, Tianci Liu, Xingchen Wang, Rongzhe Wei, Pan Li, Lu Su, Jing Gao

Beyond Linear Steering: Unified Multi-Attribute Control for Language Models (2025.findings-emnlp)

Challenge:	Existing large language models have exacerbated fairness issues in tabular data generation . inherent historical biases in tabulated data cause LLMs to exacerbate fairness problems .
Approach:	They propose a universal debiasing framework that minimizes group-level dependencies . it leverages the autoregressive structure and analytic sampling distributions of LLM-based tabular data generators .
Outcome:	The proposed framework minimizes group-level dependencies while reducing mutual information between advantaged and protected attributes.

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Narmeen Fatimah Oozeer, Luke Marks, Fazl Barez, Amir Abdullah

Unequal Scientific Recognition in the Age of LLMs (2025.findings-emnlp)

Challenge:	Empirical results show that K-Steering outperforms strong baselines in accurately steering multiple behaviors.
Approach:	They propose a method that trains a single classifier on hidden activations and computes intervention directions via gradients at inference time.
Outcome:	The proposed method outperforms strong baselines in steering multiple behaviors.

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Yixuan Liu, Abel Elekes, Jianglin Lu, Rodrigo Dorantes-Gilardi, Albert-Laszlo Barabasi

Zero-Shot Fine-Grained Image Classification Using Large Vision-Language Models (2025.findings-emnlp)

Challenge:	a new study evaluates the extent to which popular and frontier LLMs recognize scientists . recognition of scientists remains uneven across gender and geography .
Approach:	They evaluate the extent to which popular and frontier LLMs recognize scientists . they compare their outputs against OpenAlex and Wikipedia .
Outcome:	The proposed models show that they exhibit selective and inconsistent recognition patterns . women researchers and researchers from Africa, Asia, and Latin America are significantly underrecognized .

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Md. Atabuzzaman, Andrew Zhang, Chris Thomas

Using tournaments to calculate AUROC for zero-shot classification with LLMs (2025.findings-emnlp)

Challenge:	Large Vision-Language Models have demonstrated impressive performance on vision-language reasoning tasks, but their potential for zero-shot fine-grained image classification remains underexplored.
Approach:	They propose a method that transforms zero-shot fine-grained image classification into a visual question-answering framework.
Outcome:	The proposed method outperforms the current state-of-the-art approach and outperformed existing methods.

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WonJin Yoon, Ian Bulovic, Timothy A. Miller

Exploration-Driven Reinforcement Learning for Expert Routing Improvement in Mixture-of-Experts Language Models (2025.findings-emnlp)

Challenge:	Large language models perform well on zero-shot classification tasks, but are difficult to compare to supervised classifiers due to the lack of a modifiable decision boundary.
Approach:	They propose a method that transforms binary classification tasks into pairwise comparisons between instances within a dataset.
Outcome:	The proposed method produces relative rankings of instances using the Elo rating system .

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Gyunyeop Kim, Sangwoo Kang

D2CS - Documents Graph Clustering using LLM supervision (2025.findings-emnlp)

Challenge:	MoE-based LLMs are not explicitly supervised to select suitable experts.
Approach:	They propose Exploration-Driven Reinforcement Learning (ERL) which explicitly optimizes the router by exploration of alternative routing paths.
Outcome:	The proposed method improves summarization (SAMSum, XSUM, question answering, and language modeling), and raises routing quality, delivering 8.9 higher MRR than baselines over 100 perturbed routing paths.

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Yoel Ashkenazi, Etzion Harari, Regev Yehezkel Imra, Naphtali Abudarham, Dekel Cohen, Yoram Louzoun

GeoChain: Multimodal Chain-of-Thought for Geographic Reasoning (2025.findings-emnlp)

Challenge:	Document clustering does not inherently ensure thematic consistency.
Approach:	They propose a framework that constructs a similarity graph over document embeddings and applies iterative graph-based clustering algorithms to partition the corpus into initial clusters.
Outcome:	The proposed framework constructs a similarity graph over document embeddings and applies iterative graph-based clustering algorithms to partition the corpus into initial clusters.

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Sahiti Yerramilli, Nilay Pande, Rynaa Grover, Jayant Sravan Tamarapalli

SteerVLM: Robust Model Control through Lightweight Activation Steering for Vision Language Models (2025.findings-emnlp)

Challenge:	Leveraging 1.46 million Mapillary street-level images, GeoChain pairs each image with a 21-step chain-of-thought (CoT) question sequence (over 30 million Q&A pairs).
Approach:	They propose a large-scale benchmark for evaluating step-by-step geographic reasoning in multimodal large language models (MLLMs) they pair each image with a 21-step chain-of-thought (CoT) question sequence (over 30 million Q&A pairs)
Outcome:	The proposed benchmark pairs 1.46 million images with a 21-step chain-of-thought (CoT) question sequence (over 30 million Q&A pairs)

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Anushka Sivakumar, Andrew Zhang, Zaber Ibn Abdul Hakim, Chris Thomas

FractalLLM: Lossless Self-Speculative Decoding with Layer Embedded Self-Compression (2025.findings-emnlp)

Challenge:	SteerVLM is a lightweight steering module designed to guide Vision-Language Models (VLMs) towards outputs that better adhere to desired instructions.
Approach:	They propose a lightweight steering module that learns from latent embeddings of paired prompts encoding target and converse behaviors to dynamically adjust activations connecting the language modality with image context.
Outcome:	The proposed steering module outperforms existing intervention techniques on steering and hallucination mitigation benchmarks for VLMs.

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Juhyeong Kim, Sangyeon Yu, Gyunyeop Kim, Sangwoo Kang

Saten: Sparse Augmented Tensor Networks for Post-Training Compression of Large Language Models (2025.findings-emnlp)

Challenge:	Autoregressive decoding requires a full forward pass for each generated token, increasing inference latency.
Approach:	They propose a lossless self-speculative decoding method that embeds a compressed model within selected decoder layers of the original model.
Outcome:	The proposed method achieves substantial speed-ups (up to 2.47) over standard autoregressive decoding.

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Ryan Solgi, Kai Zhen, Rupak Vignesh Swaminathan, Nathan Susanj, Athanasios Mouchtaris, Siegfried Kunzmann, Zheng Zhang

Third-Person Appraisal Agent: Simulating Human Emotional Reasoning in Text with Large Language Models (2025.findings-emnlp)

Challenge:	Low-rank tensor compression techniques are used for over-parameterized neural networks, but their applications to compress pre-trained LLMs for downstream tasks remain challenging due to the high-rank nature of pre-training data.
Approach:	They propose sparse augmented tensor networks to enhance low-rank tenorized LLMs . they also propose a framework that enables full model compression .
Outcome:	The proposed framework improves accuracy and efficiency in tensorized language models.

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Simin Hong, Jun Sun, Hongyang Chen

Source-primed Multi-turn Conversation Helps Large Language Models Translate Documents (2025.findings-emnlp)

Challenge:	Emotional reasoning is essential for improving human-AI interactions, especially in mental health support and empathetic systems.
Approach:	They propose a third-person appraisal agent that simulates human-like emotional reasoning through three phases: Primary Appraisal, Secondary Appraisals, and Reappraisal.
Outcome:	The proposed model outperforms baseline LLMs in various emotional reasoning tasks, demonstrating superior generalization and interpretability.

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Hanxu Hu, Jannis Vamvas, Rico Sennrich

Mitigating Spurious Correlations via Counterfactual Contrastive Learning (2025.findings-emnlp)

Challenge:	Document-level machine translations have paved the way for truly simple document-level translation, but challenges such as omission errors remain.
Approach:	They propose a method for document-level machine translation that leverages previous contexts in a multi-turn conversational manner by decomposing documents into segments and iteratively translating them while maintaining previous turns.
Outcome:	The proposed method outperforms translations of entire documents in a single turn and translations independently according to multiple automatic metrics in representative LLMs.

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Fengxiang Cheng, Chuan Zhou, Xiang Li, Alina Leidinger, Haoxuan Li, Mingming Gong, Fenrong Liu, Robert Van Rooij

The RAG Paradox: A Black-Box Attack Exploiting Unintentional Vulnerabilities in Retrieval-Augmented Generation Systems (2025.findings-emnlp)

Challenge:	Existing methods to distinguish causally related words from spurious correlations are limited by the number of causally correlated words in a sentence.
Approach:	They propose to use probabilistic probability of necessity and probability of sufficiency to identify causal relationships rather than spurious correlations between words and class labels.
Outcome:	The proposed method is based on a contrastive learning approach name CPNS and is validated on public datasets.

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Chanwoo Choi, Jinsoo Kim, Sukmin Cho, Soyeong Jeong, Buru Chang

Guiding Large Language Models for Biomedical Entity Linking via Restrictive and Contrastive Decoding (2025.findings-emnlp)

Challenge:	Existing approaches to degrade retrieval-augmented generation systems rely on unrealistic assumptions in which external attackers have access to internal components such as the retriever.
Approach:	They propose a black-box attack based on the RAG paradox, a structural vulnerability that emerges from the system’s effort to enhance trust by revealing both the retrieved documents and their sources to users.
Outcome:	The proposed method significantly degrades system performance without internal access while generating natural-looking poisoned documents.

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Zhenxi Lin, Ziheng Zhang, Jian Wu, Yefeng Zheng, Xian Wu

Cut the Deadwood Out: Backdoor Purification via Guided Module Substitution (2025.findings-emnlp)

Challenge:	Existing attempts to apply large language models to BioEL have revealed difficulties .
Approach:	They propose a framework that enables large language models to adapt well to BioEL . they employ restrictive decoding to ensure the generation of valid entities .
Outcome:	Extensive experiments show that the framework outperforms existing LLMs.

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Yao Tong, Weijun Li, Xuanli He, Haolan Zhan, Qiongkai Xu

RepoDebug: Repository-Level Multi-Task and Multi-Language Debugging Evaluation of Large Language Models (2025.findings-emnlp)

Challenge:	Model NLP models are often trained on datasets from untrusted platforms, posing significant risks of data poisoning attacks.
Approach:	They propose a retraining-free method that selectively replaces modules in the victim model based on a trade-off signal between utility and backdoor.
Outcome:	The proposed method outperforms even the strongest defense baseline against challenging attacks like LWS.

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Jingjing Liu, Zeming Liu, Zihao Cheng, Mengliang He, Xiaoming Shi, Yuhang Guo, Xiangrong Zhu, Yuanfang Guo, Yunhong Wang, Haifeng Wang

FaStFact: Faster, Stronger Long-Form Factuality Evaluations in LLMs (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have exhibited significant proficiency in code debugging, especially in automatic program repair.
Approach:	They propose a repository-level code debugging dataset with 22 subtypes of errors that supports 8 commonly used programming languages and 3 debug tasks.
Outcome:	The proposed dataset supports 8 commonly used programming languages and 3 debugging tasks.

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Yingjia Wan, Haochen Tan, Xiao Zhu, Xinyu Zhou, Zhiwei Li, Qingsong Lv, Changxuan Sun, Jiaqi Zeng, Yi Xu, Jianqiao Lu, Yinhong Liu, Zhijiang Guo

PropXplain: Can LLMs Enable Explainable Propaganda Detection? (2025.findings-emnlp)

Challenge:	Prior evaluation pipelines fail to evaluate factuality of long-form LLMs due to inefficiency and costly human assessment.
Approach:	They propose a fast and strong evaluation pipeline that can evaluate factuality of long-form LLMs . they propose 'faStFact' to reduce cost of web searching and inference calling .
Outcome:	The proposed evaluation pipeline achieves highest alignment with human evaluation and efficiency among existing baselines.

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Maram Hasanain, Md Arid Hasan, Mohamed Bayan Kmainasi, Elisa Sartori, Ali Ezzat Shahroor, Giovanni Da San Martino, Firoj Alam

EoT: Evolution of Thoughts for Complex Reasoning Tasks (2025.findings-emnlp)

Challenge:	Currently, propagandistic content detection studies focus on detection, with little attention given to explanations justifying the predicted label.
Approach:	They propose a multilingual explanation-enhanced dataset and an explanation-based LLM to address this issue.
Outcome:	The proposed model performs comparably while also generating explanations.

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Qin Hua, Jiaqi Sun, Shiyou Qian, Dingyu Yang, Jian Cao, Guangtao Xue

Reveal and Release: Iterative LLM Unlearning with Self-generated Data (2025.findings-emnlp)

Challenge:	Existing studies focus on ensuring behavior fidelity, factuality or reliability in generated reasoning processes, but they neglect the simultaneous optimization of all three aspects for each thought.
Approach:	They propose a thought assessment method that is sensitive to knowledge and LLM behaviors . they use three scorers to evaluate each thought by considering domain context, semantic alignment, and behavior impact.
Outcome:	The proposed framework outperforms advanced approaches in knowledge-based complex tasks.

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Linxi Xie, Xin Teng, Shichang Ke, Hongyi Wen, Shenji Wan

Challenge:	Existing approaches to unlearning large language models assume full access to the forget dataset, overlooking two key challenges: (1) Forget data is often privacy-sensitive, rare, or legally regulated, making it expensive or impractical to obtain (2) The distribution of available forget data may not align with how that information is represented within the model.
Approach:	They propose a “Reveal-and-Release” method to unlearn with self-generated data, prompting the model to reveal what it knows using optimized instructions.
Outcome:	The proposed method removes the influence of undesirable data from the model.

An Evaluation Resource for Grounding Translation Errors (2025.findings-emnlp)

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Sujin Chen, Kang Wang, Zixuan Zhou, Xiangyu Duan, Wanqun Zhang, Hao Yang, Jinsong Su, Min Zhang

Enhancing Time Awareness in Generative Recommendation (2025.findings-emnlp)

Challenge:	Current fine-grained error analyses do not ground the errors to the reasons why the annotated text spans are erroneous.
Approach:	They use a bi-directional grounding scheme to ground erroneous text in two directions . if the error spans of both directions are consistent, the explanation is valid .
Outcome:	The proposed grounding process improves translation error detection significantly.

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Sunkyung Lee, Seongmin Park, Jonghyo Kim, Mincheol Yoon, Jongwuk Lee

Challenge:	Existing models focus on sequential order of items and neglect to handle temporal dynamics . existing models neglect to capture hidden user preferences via various temporal signals .
Approach:	They propose a model that generates recommendations into a text-to-text generation task . they introduce Time-aware Prompting and Trend-awful Inference .
Outcome:	The proposed model outperforms state-of-the-art models with gains of 15.4% and 14.3% . it is based on time-aware Prompting and Trend-awful Inference .

Adaptive LLM Routing under Budget Constraints (2025.findings-emnlp)

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Pranoy Panda, Raghav Magazine, Chaitanya Devaguptapu, Sho Takemori, Vishal Sharma

Promptception: How Sensitive Are Large Multimodal Models to Prompts? (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have revolutionized natural language processing, but their varying capabilities and costs pose challenges in practical applications.
Approach:	They propose to use LLM routing as a contextual bandit problem to enable adaptive decision-making . they use offline human preference data to refine the embeddings through online bandit feedback .
Outcome:	The proposed model is based on a contextual bandit problem enabling adaptive decision-making . it learns from offline human preference data and refines it through online bandit feedback.

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Mohamed Insaf Ismithdeen, Muhammad Uzair Khattak, Salman Khan

Can Federated Learning Safeguard Private Data in LLM Training? Vulnerabilities, Attacks, and Defense Evaluation (2025.findings-emnlp)

Challenge:	despite the success of Large Multimodal Models, prompt design for MCQA remains poorly understood.
Approach:	They propose a framework for evaluating prompt sensitivity in LMMs . they propose 61 prompt types, each targeting specific aspects of prompt formulation .
Outcome:	The proposed framework evaluates 10 LMMs across 3 MCQA benchmarks.

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Wenkai Guo, Xuefeng Liu, Haolin Wang, Jianwei Niu, Shaojie Tang, Jing Yuan

Runaway is Ashamed, But Helpful: On the Early-Exit Behavior of Large Language Model-based Agents in Embodied Environments (2025.findings-emnlp)

Challenge:	federated learning (FL) fine-tunes large language models with local data, but organizations are reluctant to share local data.
Approach:	They propose a framework for fine-tuning large language models with local data . they propose centralized fine- tuning with local datasets is a good idea .
Outcome:	The proposed framework allows clients to retain local data while sharing only model parameters for training.

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Qingyu Lu, Liang Ding, Siyi Cao, Xuebo Liu, Kanjian Zhang, Jinxia Zhang, Dacheng Tao

AutoMIR: Effective Zero-Shot Medical Information Retrieval without Relevance Labels (2025.findings-emnlp)

Challenge:	Experiments with 4 different LLMs across 5 embodied environments show significant efficiency improvements, with only minor drops in agent performance.
Approach:	They propose an intrinsic method that injects exit instructions during generation and an extransic system that verifies task completion to determine when to halt an agent’s trial.
Outcome:	The proposed method injects exit instructions during generation and an exit method verifies task completion to determine when to halt an agent’s trial.

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Lei Li, Xiangxu Zhang, Xiao Zhou, Zheng Liu

RG-VQA: Leveraging Retriever-Generator Pipelines for Knowledge Intensive Visual Question Answering (2025.findings-emnlp)

Challenge:	Effective zero-shot dense retrieval in the medical domain remains difficult due to the scarcity of relevance-labeled data.
Approach:	They propose a framework that leverages large language models to generate hypothetical documents . they also propose 'CMIRB' to provide a rigorous evaluation suite .
Outcome:	The proposed framework outperforms HyDE in retrieval accuracy and generalization . it leverages large language models to generate hypothetical documents conditioned on a query .

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Settaluri Lakshmi Sravanthi, Pulkit Agarwal, Debjyoti Mondal, Rituraj Singh, Subhadarshi Panda, Ankit Mishra, Kiran Pradeep, Srihari K B, Godawari Sudhakar Rao, Pushpak Bhattacharyya

Enhancing RAG Efficiency with Adaptive Context Compression (2025.findings-emnlp)

Challenge:	Existing methods to improve the reasoning capabilities of VQA systems are limited due to complexity of graph neural networks and end-to-end training.
Approach:	They propose a method to integrate Dense Passage Retrievers with Vision Language Models to boost the reasoning capabilities of VQA systems.
Outcome:	The proposed method outperforms human accuracy and GPT-4 in the ScienceQA dataset.

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Shuyu Guo, Shuo Zhang, Zhaochun Ren

Revealing the impact of synthetic native samples and multi-tasking strategies in Hindi-English code-mixed humour and sarcasm detection (2025.findings-emnlp)

Challenge:	Existing methods apply fixed compression rates, over-compressing simple queries or under-compressed complex ones.
Approach:	a new framework uses a hierarchical compressor and a context selector to optimize inference efficiency . a framework that dynamically adjusts compression rates based on input complexity optimizes inference without loss of accuracy.
Outcome:	Adaptive Context Compression for RAG outperforms fixed-rate methods on Wikipedia and five QA datasets .

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Debajyoti Mazumder, Aakash Kumar, Jasabanta Patro

CogAtom: From Cognitive Atoms to Olympiad-level Mathematical Reasoning in Large Language Models (2025.findings-emnlp)

Challenge:	Specifically, we tried native sample mixing, multi-task learning, and prompting and instruction finetuning very large multilingual language models (VMLMs).
Approach:	They used native sample mixing, multi-task learning and prompting and instruction finetuning to improve code-mixed humour and sarcasm detection.
Outcome:	The proposed methods improve humour and sarcasm detection by adding native samples to training sets and multitask learning and prompting and instruction finetuning VMLMs.

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Zhuofan Chen, Jiyuan He, Yichi Zhang, Xing Hu, Haoxing Wen, Jun Bai, Wenge Rong

Efficient Latent Semantic Clustering for Scaling Test-Time Computation of LLMs (2025.findings-emnlp)

Challenge:	Existing methods for generating high-quality, multi-step reasoning are limited . we present a new framework for synthesising rigorous, cognitively diverse problems .
Approach:	They propose a cognitive atom-based framework for synthesizing mathematically rigorous problems.
Outcome:	The proposed framework outperforms existing methods in accuracy, reasoning depth and diversity while exceeding the difficulty of AIME.

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Sungjae Lee, Hoyoung Kim, Jeongyeon Hwang, Eunhyeok Park, Jungseul Ok

Text Clustering

Challenge:	Existing methods for scaling test-time computation rely on external models that introduce substantial computational overhead and fail to capture context-aware semantics.
Approach:	They propose a method that leverages the generator LLM’s internal hidden states for clustering, eliminating the need for external models.
Outcome:	The proposed method improves the computational efficiency of test-time scaling while maintaining or exceeding the performance of existing methods.

BannerBench: Benchmarking Vision Language Models for Multi-Ad Selection with Human Preferences (2025.findings-emnlp)

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Hiroto Otake, Peinan Zhang, Yusuke Sakai, Masato Mita, Hiroki Ouchi, Taro Watanabe

DeKeyNLU: Enhancing Natural Language to SQL Generation through Task Decomposition and Keyword Extraction (2025.findings-emnlp)

Challenge:	Web banner advertisements are often selected manually because of human preferences . a new benchmark evaluates the degree of alignment with human preferences in two tasks .
Approach:	a benchmark was developed to evaluate the human preference-driven banner selection process using vision-language models.
Outcome:	The proposed benchmark assesses the degree of alignment with human preferences in two tasks using vision-language models.

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Jian Chen, Zhenyan Chen, Xuming Hu, Peilin Zhou, Yining Hua, Han Fang, Cissy Hing Yee Choy, Xinmei Ke, Jingfeng Luo, Zixuan Yuan

Facilitating Cross-lingual Transfer of Empathy through Language-independent Latent Diffusion: A Case Study in Chinese (2025.findings-emnlp)

Challenge:	NL2SQL provides a model-centric paradigm that simplifies database access for non-technical users . challenges such as inaccurate task decomposition and keyword extraction remain major bottlenecks .
Approach:	They propose a RAG-based NL2SQL pipeline that employs three modules for query understanding, entity retrieval, and generation to improve SQL generation accuracy.
Outcome:	The proposed pipeline improves the accuracy of query generation on BIRD and Spider datasets.

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Junlin Li, Peng Bo, Yu-Yin Hsu

Evaluating Compound AI Systems through Behaviors, Not Benchmarks (2025.findings-emnlp)

Challenge:	Existing human empathy data are limited to English . a new study examines the pragmatic transferability of empathy across languages .
Approach:	a team of researchers integrate language-independent diffusion processes to facilitate the cross-lingual transfer of empathy.
Outcome:	The proposed method demonstrates that empathy can be transferred across languages without compromising linguistic naturalness.

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Pranav Bhagat, K N Ajay Shastry, Pranoy Panda, Chaitanya Devaguptapu

Challenge:	Compound AI (CAI) systems combine LLMs with retrievers and tools to enable information-seeking applications in the real-world.
Approach:	They propose a behavior-driven evaluation framework that generates test specifications aligned with real usage contexts.
Outcome:	The proposed framework generates test specifications aligned with real usage contexts and transforms them into concrete test cases.

SciCompanion: Graph-Grounded Reasoning for Structured Evaluation of Scientific Arguments (2025.findings-emnlp)

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Joshua Alan Flashner, Adithya Kulkarni, Dawei Zhou

From Generation to Detection: A Multimodal Multi-Task Dataset for Benchmarking Health Misinformation (2025.findings-emnlp)

Challenge:	Existing retrieval-augmented generation (RAG) methods fail to provide deep, relational understanding of scientific literature.
Approach:	They propose a graph-grounded reasoning framework for structured scientific evaluation that uses multi-hop reasoning to iteratively construct contextual graphs and generate structured critiques.
Outcome:	The proposed framework reduces evaluation error by over 30% compared to baselines and allows smaller models to outperform larger models.

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Zhihao Zhang, Yiran Zhang, Xiyue Zhou, Liting Huang, Imran Razzak, Preslav Nakov, Usman Naseem

Estimating Machine Translation Difficulty (2025.findings-emnlp)

Challenge:	Infodemics and health misinformation have significant negative impact on individuals and society . generative AI has significantly accelerated the spread and expanded the reach of health misinfo .
Approach:	MM-Health is a large scale multimodal misinformation dataset in the health domain . it includes human-generated multimodal information and AI-generated multiplemodal information .
Outcome:	MM-Health is a large scale misinformation dataset in the health domain . it includes human-generated multimodal information and AI-generated content .

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Lorenzo Proietti, Stefano Perrella, Vilém Zouhar, Roberto Navigli, Tom Kocmi

TIU-Bench: A Benchmark for Evaluating Large Multimodal Models on Text-rich Image Understanding (2025.findings-emnlp)

Challenge:	Despite the high-quality outputs, it is difficult to distinguish between state-of-the-art models and identify areas for future improvement.
Approach:	They propose a new metric to evaluate difficulty estimators and use it to assess both baselines and novel approaches.
Outcome:	The proposed models outperform both heuristic-based methods and LLM-as-a-judge approaches, with sentinel-src achieving the best performance.

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Kun Zhang, Liqiang Niu, Zhen Cao, Fandong Meng, Jie Zhou

Breaking Token Into Concepts: Exploring Extreme Compression in Token Representation Via Compositional Shared Semantics (2025.findings-emnlp)

Challenge:	Existing text-rich image understanding benchmarks lack scale and fragmented scenarios . a new full-image structured output format is proposed to enable fine-grained evaluation of perception and reasoning capabilities.
Approach:	They propose a large-scale, multilingual benchmark that includes over 100,000 annotations and 22,000 question-answer pairs.
Outcome:	The proposed framework provides a comprehensive platform for developing and evaluating next-generation multimodal AI systems.

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Kavin R V, Pawan Goyal

ExeSQL: Self-Taught Text-to-SQL Models with Execution-Driven Bootstrapping for SQL Dialects (2025.findings-emnlp)

Challenge:	Standard language models employ unique, monolithic embeddings for each token, limiting their ability to capture multifaceted meanings.
Approach:	They propose a compositional structure that accumulates diverse semantic facets for tokens . they apply this representational scheme to standard transformer architectures and a biomedical domain benchmark .
Outcome:	The proposed representational scheme achieves extreme compression in embedding parameters while maintaining >95% task performance relative to the base model.

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Jipeng Zhang, Haolin Yang, Kehao Miao, Ruiyuan Zhang, Renjie Pi, Jiahui Gao, Xiaofang Zhou

Under the Shadow of Babel: How Language Shapes Reasoning in LLMs (2025.findings-emnlp)

Challenge:	Existing text-to-SQL models are limited to SQLite due to dataset limitations . data generated through static prompting is noisy and unreliable, authors say .
Approach:	They propose a text-to-SQL framework with execution-driven, agentic bootstrapping . ExeSQl bridges the dialect gap in text- to-Sql, achieving average improvements .
Outcome:	ExeSQL bridges the dialect gap in text-to-SQl, with average improvements of 15.2%, 10.38%, and 4.49% over GPT-4o on PostgreSQLE, MySQL, and Oracle.

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Chenxi Wang, Yixuan Zhang, Lang Gao, Zixiang Xu, Zirui Song, Yanbo Wang, Xiuying Chen

Linguistics & Cognitive NLP

Challenge:	linguistic relativity suggests that the structure of language shapes cognitive patterns . large language models internalize the habitual logical structures embedded in different languages, authors say .
Approach:	a study introduces a bilingual dataset for causal reasoning in Chinese and English.
Outcome:	a new study shows that large language models internalize reasoning biases shaped by language . the model internalizes language-specific preferences and rigidly applies them to atypical inputs, the study shows .

Think Right, Not More: Test-Time Scaling for Numerical Claim Verification (2025.findings-emnlp)

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Primakov Chungkham, Venktesh V, Vinay Setty, Avishek Anand

Nexus: Adaptive Upcycling to Efficiently Pretrain Mixture of Experts (2025.findings-emnlp)

Challenge:	Fact-checking real-world claims requires multistep reasoning and numerical reasoning . large language models are unable to understand nuance of numerical aspects .
Approach:	They propose scaling test-time compute (TTS) for large language models to solve this problem . they train a verifier model to navigate the space of possible reasoning paths .
Outcome:	The proposed approach achieves 1.8x higher efficiency than standard TTS while delivering 18.8% performance improvement over single-shot verification methods.

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Nikolas Gritsch, Qizhen Zhang, Acyr Locatelli, Sara Hooker, Ahmet Üstün

Exploring Context Strategies in LLMs for Discourse-Aware Machine Translation (2025.findings-emnlp)

Challenge:	Nevertheless, training from scratch on trillions of tokens remains expensive that most users can only finetune these models.
Approach:	They propose to reuse parameters of dense models for the MoE layers with a router that can integrate new experts into existing trained models without hurting performance on previous domains.
Outcome:	The proposed router can integrate new experts into existing trained models without hurting the performance on previous domains.

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Ritvik Choudhary, Rem Hida, Masaki Hamada, Hayato Futami, Toshiyuki Sekiya

Insights into using temporal coordinated behaviour to explore connections between social media posts and influence (2025.findings-emnlp)

Challenge:	Large language models excel at machine translation, but the impact of how LLMs utilize different forms of contextual information on discourse-level phenomena remains underexplored.
Approach:	They examine how different forms of context influence standard MT metrics and specific discourse phenomena such as formality, pronoun selection, and lexical cohesion.
Outcome:	Evaluating multiple LLMs across multiple domains and language pairs, the findings consistently show that context boosts translation and discourse-specific performance.

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Elisa Sartori, Serena Tardelli, Maurizio Tesconi, Mauro Conti, Alessandro Galeazzi, Stefano Cresci, Giovanni Da San Martino

Challenge:	Political campaigns often use coordinated behaviour to identify communities of users who exhibit similar patterns.
Approach:	They analysed messages users were exposed to during the UK 2019 election and compared those received by users who shifted communities with others covering the same topics.
Outcome:	The results show that political campaigns often use coordinated behaviour to identify communities of users who exhibit similar patterns.

SpecCoT: Accelerating Chain-of-Thought Reasoning through Speculative Exploration (2025.findings-emnlp)

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Junhan Shi, Yijia Zhu, Zhenning Shi, Dan Zhao, Qing Li, Yong Jiang

Sang Min Jung, Kaixiang Zhang, Cristian Danescu-Niculescu-Mizil

Challenge:	Large Reasoning Models suffer from high inference latency due to lengthy reasoning chains.
Approach:	They propose a collaborative framework that combines large and small models for effective reasoning.
Outcome:	The proposed framework reduces inference latency by 1.7-4.1 while maintaining comparable accuracy to standard large model inference.

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AgentDrug: Utilizing Large Language Models in an Agentic Workflow for Zero-Shot Molecular Optimization (2025.findings-emnlp)

Challenge:	Qualities of a conversation are dependent on how interactions combine to form a “shape” of the conversation.
Approach:	They propose a similarity measure to capture differences in conversation dynamics and assess its sensitivity to the topic of the conversation.
Outcome:	The proposed measure captures differences in conversation dynamics and assesses its sensitivity to the topic of the conversation.

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Le Huy Khiem, Ting Hua, Nitesh V Chawla

Improving Preference Alignment of LLM with Inference-Free Self-Refinement (2025.findings-emnlp)

Challenge:	Molecular optimization is a fundamental task in drug discovery.
Approach:	They propose an agentic workflow that leverages LLMs in a structured refinement process to achieve significantly higher accuracy.
Outcome:	The proposed workflow improves on single- and multi-property optimization tasks under loose and strict thresholds.

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Fukun Ma, Kaibin Tian, Jieting Xue, Xiaoyi Wang, Ye Ma, Quan Chen, Peng Jiang, Lijie Wen

Challenge:	Large language models (LLMs) develop in-context learning capability through pretraining and instruction tuning.
Approach:	Large language models (LLMs) develop in-context learning capability through pretraining and instruction tuning.
Outcome:	Experiments show that incorporating IFSR into preference alignment yields performance improvement over 10%.

Guaranteed Guess: A Language Modeling Approach for CISC-to-RISC Transpilation with Testing Guarantees (2025.findings-emnlp)

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Ahmed Heakl, Sarim Hashmi, Chaimaa Abi, Celine Lee, Abdulrahman Mahmoud

StructuThink: Reasoning with Task Transition Knowledge for Autonomous LLM-Based Agents (2025.findings-emnlp)

Challenge:	ISA-centric transpilation pipelines are used to translate low-level programs between ISAs . GG provides high code coverage across unit tests and better energy efficiency .
Approach:	They propose a ISA-centric transpilation pipeline that embeds large language models into software testing frameworks to ensure accuracy.
Outcome:	The proposed method achieves high code coverage across unit tests and functional/semantic correctness of 99% on HumanEval and 49% on BringupBench programs.

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Haiyu Zhao, Zhenyu Guo, Chunhong Zhang, Ziyu Zhou, Zheng Hu

Leveraging Unpaired Feedback for Long-Term LLM-based Recommendation Tuning (2025.findings-emnlp)

Challenge:	StructuThink framework enhances LLMs' ability to ground decisions in domain-specific scenarios.
Approach:	They propose a knowledge-structured reasoning framework that enhances LLM-based agents with explicit decision constraints.
Outcome:	The proposed framework achieves higher task success rates and more efficient action sequences than baseline methods.

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Jizhi Zhang, Chongming Gao, Wentao Shi, Xin Chen, Jingang Wang, Xunliang Cai, Fuli Feng

Challenge:	a recent study highlights unpaired feedback as a key challenge for long-term LLM-based recommenders . unpaired user feedback is crucial for improving LLMs in dynamic user environments, authors say .
Approach:	They propose a framework that incorporates unpaired feedback into LLMs to improve long-term recommendation performance.
Outcome:	The proposed framework improves long-term recommendation performance by incorporating unpaired feedback without requiring paired supervision.

Investigating Multi-layer Representations for Dense Passage Retrieval (2025.findings-emnlp)

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Zhongbin Xie, Thomas Lukasiewicz

KELE: Residual Knowledge Erasure for Enhanced Multi-hop Reasoning in Knowledge Editing (2025.findings-emnlp)

Challenge:	Dense retrieval models adopt vectors from the last hidden layer of the document encoder to represent a document, which is in contrast to the fact that representations in different layers of a pre-trained language model contain different kinds of linguistic knowledge and behave differently during fine-tuning.
Approach:	They propose to utilize representations from multiple encoder layers to make up the representation of a document, which they denote Multi-layer Representations (MLR).
Outcome:	The proposed model outperforms dual encoder, ME-BERT and ColBERT in the single-vector retrieval setting and with other advanced training techniques.

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Mengqi Zhang, Bowen Fang, Qiang Liu, Xiaotian Ye, Shu Wu, Pengjie Ren, Zhumin Chen, Liang Wang

Dissecting Persona-Driven Reasoning in Language Models via Activation Patching (2025.findings-emnlp)

Challenge:	Existing knowledge editing techniques show limitations when applied to multi-hop reasoning . residual single-hop knowledge causes edited models to revert to original answers .
Approach:	They propose a knowledge editing method that incorporates a Knowledge Erasure mechanism for Large language model Editing (KELE) they propose an erasure function for residual knowledge and an injection function for new knowledge .
Outcome:	The proposed method significantly improves multi-hop reasoning capability of edited models.

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Ansh Poonia, Maeghal Jain

PUER: Boosting Few-shot Positive-Unlabeled Entity Resolution with Reinforcement Learning (2025.findings-emnlp)

Challenge:	Large language models (LLMs) exhibit remarkable versatility in adopting diverse personas.
Approach:	They examine how assigning a persona influences a model’s reasoning on an objective task by activation patching .
Outcome:	The early Multi-Layer Perceptron (MLP) layers attend to syntactic structure of input and process its semantic content.

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Yaoshu Wang, Mengyi Yan, Wei Wang

Toward the Automatic Detection of Word Meaning Negotiation Indicators in Conversation (2025.findings-emnlp)

Challenge:	Existing approaches to entity resolution focus on supervised learning, but manual annotation is labor-intensive.
Approach:	They propose an end-to-end ER solution that leverages Large Language Models in PU learning setting to address low-resource entity resolution.
Outcome:	The proposed solution improves the performance of PUER on a positive-unlabeled learning environment.

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Aina Garí Soler, Matthieu Labeau, Chloé Clavel

Forget the Unneeded: Backdooring Large Language Models via Contrastive-enhanced Machine Unlearning (2025.findings-emnlp)

Challenge:	Word Meaning Negotiations (WMN) are sequences in conversation where speakers collectively discuss and shape word meaning.
Approach:	They propose to detect WMN indicators in conversations where a speaker signals the need to clarify or challenge word meaning.
Outcome:	The proposed models have better precision than previous regular expression based approaches and show some generalization abilities, but have moderate recall.

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Shiji Yang, Shu Zhao, Congyao Mei, Zhen Yang, Jie Chen, Fulan Qian, Zhen Duan, Yanping Zhang

Equipping Retrieval-Augmented Large Language Models with Document Structure Awareness (2025.findings-emnlp)

Challenge:	Existing methods for prompt tuning for Large Language Models find backdoor attacks to be significant in data-rich scenarios.
Approach:	They propose a backdoor attacks through contrastive-enhanced machine unlearning in data-limited scenarios . they use a machine un learning method to capture precise backdoor patterns .
Outcome:	The proposed method captures precise backdoor patterns without association between triggers and backdoors, reducing side effects.

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Lingnan Xu, Chong Feng, Kaiyuan Zhang, Liu Zhengyong, Wenqiang Xu, Fanqing Meng

QEVA: A Reference-Free Evaluation Metric for Narrative Video Summarization with Multimodal Question Answering (2025.findings-emnlp)

Challenge:	Existing approaches to retrieval-augmented generation ignore valuable structure that is crucial for document organization.
Approach:	They propose a framework that explicitly incorporates structural information throughout the RAG process.
Outcome:	The proposed framework incorporates structural information throughout the RAG process.

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Woojun Jung, Junyeong Kim

Thinking Before You Speak: A Proactive Test-time Scaling Approach (2025.findings-emnlp)

Challenge:	Existing video-to-text summarization evaluation methods depend heavily on human-written reference summaries.
Approach:	They propose a reference-free metric evaluating candidate summaries directly against source videos through multimodal question answering.
Outcome:	The proposed metric assesses candidate summaries directly against source videos through multimodal question answering.

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Cong Liu, Wenchang Chai, Hejun Wu, Yan Pan, Pengxu Wei, Liang Lin

Do Before You Judge: Self-Reference as a Pathway to Better LLM Evaluation (2025.findings-emnlp)

Challenge:	Large Language Models often exhibit deficiencies with complex reasoning tasks, such as maths, due to the discrepancy between human reasoning patterns and those presented in training data.
Approach:	They propose to insert insights between consecutive reasoning steps to bridge this gap by generating insights between the next reasoning steps.
Outcome:	Experiments on mathematical datasets confirm the effectiveness of the proposed reasoning framework on complex problems.

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Wei-Hsiang Lin, Sheng-Lun Wei, Hen-Hsen Huang, Hsin-Hsi Chen

Challenge:	LLM-as-Judge frameworks are increasingly popular for AI evaluation, yet research findings on the relationship between models’ generation and judgment abilities remain inconsistent.
Approach:	They propose a self-reference-guided evaluation strategy that leverages a model’s own answers as references to strengthen the correlation between generation and judgment abilities.
Outcome:	The proposed approach strengthens the correlation between model generation and judgment abilities and provides a reliable proxy for model selection in evaluation tasks.

Beyond Content: How Grammatical Gender Shapes Visual Representation in Text-to-Image Models (2025.findings-emnlp)

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Muhammed Saeed, Shaina Raza, Ashmal Vayani, Muhammad Abdul-Mageed, Ali Emami, Shady Shehata

ToolHaystack: Stress-Testing Tool-Augmented Language Models in Realistic Long-Term Interactions (2025.findings-emnlp)

Challenge:	grammatical gender significantly influences image generation in text-to-image models . masculine grammatikal markers increase male representation to 73% on average . feminine grammatological markers increase female representation to 38% .
Approach:	They propose a cross-linguistic benchmark examining words where grammatical gender contradicts stereotypical gender associations.
Outcome:	The proposed benchmark examines words where grammatical gender contradicts stereotypical gender associations.

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Beong-woo Kwak, Minju Kim, Dongha Lim, Hyungjoo Chae, Dongjin Kang, Sunghwan Kim, Dongil Yang, Jinyoung Yeo

GraphCheck: Multipath Fact-Checking with Entity-Relationship Graphs (2025.findings-emnlp)

Challenge:	Existing evaluations assume tool use in short contexts, offering limited insight into model behavior during realistic long-term interactions.
Approach:	a benchmark is a tool to test long-term tool use in large language models . the tool includes multiple tasks execution contexts and realistic noise .
Outcome:	a new benchmark tests the tool use capabilities in long-term interactions.

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Hyewon Jeon, Jay-Yoon Lee

FLAMES: Improving LLM Math Reasoning via a Fine-Grained Analysis of the Data Synthesis Pipeline (2025.findings-emnlp)

Challenge:	GraphCheck is a framework for fact-checking complex claims that require multi-hop reasoning . Graphcheck excels in complex scenarios, but may be unnecessarily elaborate for simpler claims .
Approach:	They propose a framework that transforms claims into entity-relationship graphs for fact-checking . DP-GraphCheck employs a lightweight strategy selector to choose between direct prompting and GraphCheck adaptively.
Outcome:	The proposed framework outperforms existing methods in verification accuracy while achieving strong computational efficiency.

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Parker Seegmiller, Kartik Mehta, Soumya Saha, Chenyang Tao, Shereen Oraby, Arpit Gupta, Tagyoung Chung, Mohit Bansal, Nanyun Peng

POW: Political Overton Windows of Large Language Models (2025.findings-emnlp)

Challenge:	Recent work improving LLM math reasoning with synthetic data uses unique setups, making comparison of data synthesis strategies impractical.
Approach:	They propose a framework for LLM assessment of math reasoning with synthetic data . they use 10 existing data synthesis strategies and multiple other factors to study performance .
Outcome:	The proposed data synthesis strategies outperform public datasets on OlympiadBench, CollegeMath, GSMPlus and MATH.

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Leif Azzopardi, Yashar Moshfeghi

Columbo: Expanding Abbreviated Column Names for Tabular Data Using Large Language Models (2025.findings-emnlp)

Challenge:	Political bias in Large Language Models (LLMs) presents a growing concern for the responsible deployment of AI systems.
Approach:	They propose to use the Overton Window as a framework to map the ideological boundaries that a given LLM will espouse, remain neutral on, or refuse to endorse.
Outcome:	The proposed methodology reveals political bias in large language models by examining the political stances of models from eight providers.

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Ting Cai, Stephen Sheen, AnHai Doan

RTTC: Reward-Guided Collaborative Test-Time Compute (2025.findings-emnlp)

Challenge:	Existing solutions to expand table names are limited by the abbreviated column names of tables.
Approach:	They propose to use abbreviated tables to expand column names . they propose to introduce four new datasets with real-world abbrevations .
Outcome:	The proposed solution outperforms NameGuess in terms of accuracy and consistency over five datasets.

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Juan Pablo Munoz, Jinjie Yuan

AMANDA: Agentic Medical Knowledge Augmentation for Data-Efficient Medical Visual Question Answering (2025.findings-emnlp)

Challenge:	Reward-Guided Test-Time Compute (RTTC) is a powerful paradigm for large language models . indiscriminate application of TTC strategy incurs substantial computational overhead .
Approach:	They propose a framework that adaptively selects the most effective TTC strategy for each query via a pretrained reward model.
Outcome:	The proposed framework maximizes accuracy across diverse domains and tasks.

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Ziqing Wang, Chengsheng Mao, Xiaole Wen, Yuan Luo, Kaize Ding

Mixed Signals: Decoding VLMs’ Reasoning and Underlying Bias in Vision-Language Conflict (2025.findings-emnlp)

Challenge:	Existing Med-MLLMs fail when deployed in low-resource settings where abundant labeled data is unavailable.
Approach:	They propose a training-free agentic framework that performs medical knowledge augmentation via LLM agents.
Outcome:	The proposed framework performs medical knowledge augmentation via LLM agents.

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Pouya Pezeshkpour, Moin Aminnaseri, Estevam Hruschka

Mitigating Hallucination in Large Vision-Language Models through Aligning Attention Distribution to Information Flow (2025.findings-emnlp)

Challenge:	Vision-language models have demonstrated impressive performance by effectively integrating visual and textual information to solve complex tasks.
Approach:	They build upon existing benchmarks to create five datasets containing mismatched image-text pairs and examine how they reason over visual and textual data .
Outcome:	The proposed model reasoned over visual and textual data in real-world applications but not in the visual and visual descriptions.

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Jianfei Zhao, Feng Zhang, Xin Sun, Chong Feng

OptiSeq: Ordering Examples On-The-Fly for In-Context Learning (2025.findings-emnlp)

Challenge:	Decode-Only models propagate information from left to right, but the model's attention still focuses on the visual representations, resulting in hallucinations.
Approach:	They propose to leverage the core information embedded in semantic representations to enhance the model's visual understanding by leveraging the attention distributions.
Outcome:	The proposed method reduces hallucinations by 80% by aligning the attention distribution with the actual information flow.

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Rahul Atul Bhope, Praveen Venkateswaran, K. R. Jayaram, Vatche Isahagian, Vinod Muthusamy, Nalini Venkatasubramanian

Dependency Parsing-Based Syntactic Enhancement of Relation Extraction in Scientific Texts (2025.findings-emnlp)

Challenge:	In-context-learning (ICL) is fragile and requires a lot of examples to perform.
Approach:	They propose a purely inference-time, dataset-free optimization method that efficiently determines the best example order.
Outcome:	The proposed method improves in-context-learning accuracy by 5.5 - 10.5 percentage points across multiple tasks.

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Devvrat Joshi, Islem Rekik

DIPLomA: Efficient Adaptation of Instructed LLMs to Low-Resource Languages via Post-Training Delta Merging (2025.findings-emnlp)

Challenge:	a pipeline approach to extract entities and relations from scientific text is challenging due to long sentences with densely packed entities.
Approach:	They propose a syntactic filtering method that prunes unlikely entity pairs before relation prediction.
Outcome:	The proposed method improves Rel+ F1 scores on SciERC, SciER, and ACE05 datasets.

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Ixak Sarasua Antero, Ander Corral, Xabier Saralegi

Reliability Crisis of Reference-free Metrics for Grammatical Error Correction (2025.findings-emnlp)

Challenge:	Using delta-based weight merging, open-weight instruction-tuned large language models can be efficiently adapted to low-resource languages without costly large-scale post-training.
Approach:	They propose a delta-based instruction-preserving language adaptation strategy that decouples adaptation from post-training alignment by pretraining a foundational LLM on monolingual target-language data and then injecting instruction-following capabilities via delta-derived weight merging.
Outcome:	The proposed method achieves significant gains in instruction-following, linguistic proficiency, and safety on Basque, Welsh, and Swahili while preserving the original model's multilingual performance.

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Takumi Goto, Yusuke Sakai, Taro Watanabe

Who Speaks Matters: Analysing the Influence of the Speaker’s Linguistic Identity on Hate Classification (2025.findings-emnlp)

Challenge:	Reference-free evaluation metrics for grammatical error correction have high correlation with human judgments, but they are not designed to evaluate adversarial systems that aim to obtain unjustifiably high scores.
Approach:	They propose adversarial attack strategies for four reference-free metrics . they propose SOME, Scribendi, IMPARA, and LLM-based metrics based on these metrics a .
Outcome:	The proposed attacks outperform the current state-of-the-art for four reference-free metrics .

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Ananya Malik, Kartik Sharma, Shaily Bhatt, Lynnette Hui Xian Ng

Are LLMs Empathetic to All? Investigating the Influence of Multi-Demographic Personas on a Model’s Empathy (2025.findings-emnlp)

Challenge:	Large Language Models are known to be brittle and biased against marginalised communities and dialects.
Approach:	They investigate the robustness of hate speech classification using LLMs when explicit and implicit markers of the speaker’s ethnicity are injected into the input.
Outcome:	The proposed model is robust when explicit and implicit markers of speaker's ethnicity are injected into the input.

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Ananya Malik, Nazanin Sabri, Melissa M. Karnaze, Mai ElSherief

Challenge:	Large Language Models’ ability to converse naturally is empowered by their ability to empathetically understand and respond to their users.
Approach:	They propose a framework to investigate how LLMs’ cognitive and affective empathy vary across user personas defined by intersecting demographic attributes.
Outcome:	The proposed framework examines 315 unique personas from age, culture, and gender across four LLMs.

Active Learning for Multidialectal Arabic POS Tagging (2025.findings-emnlp)

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Diyam Akra, Mohammed Khalilia, Mustafa Jarrar

Embedding-Free RAG (2025.findings-emnlp)

Challenge:	Multidialectal Arabic POS tagging is challenging due to the morphological richness and high variability among dialects.
Approach:	They propose an active learning approach for multidialectal Arabic POS tagging . they annotate approximately 15,000 tokens, reducing the annotation requirement by about 2,000 tokens .
Outcome:	The proposed approach achieves 97.6% accuracy on the Emirati corpus.

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Jessica Maghakian, Raunak Sinha, Max Schettewi, Gunkirat Kaur

Rating Roulette: Self-Inconsistency in LLM-As-A-Judge Frameworks (2025.findings-emnlp)

Challenge:	Retrieval-Augmented Generation (RAG) is the current state-of-the-art method for mitigating the shortcomings of large language models.
Approach:	They propose a model-agnostic approach to retrieval-augmented generation that leverages generalized reasoning abilities of large language models.
Outcome:	Embedding-free RAG outperforms existing state-of-the-art methods in a wide range of domains.

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Rajarshi Haldar, Julia Hockenmaier

Quantifying Uncertainty in Natural Language Explanations of Large Language Models for Question Answering (2025.findings-emnlp)

Challenge:	Using large language models (LLMs) for evaluating natural language generation has gained traction . lm judges have low intra-rater reliability in their assigned scores, making it difficult to measure how good their judgments actually are.
Approach:	They show that large language models align more closely with human preferences than n-grams . they quantify this variance and compare them to other NLG tasks and benchmarks based on the results .
Outcome:	The proposed models align more closely with human preferences than n-gram or embedding-based metrics.

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Yangyi Li, Mengdi Huai

Real-World Summarization: When Evaluation Reaches Its Limits (2025.findings-emnlp)

Challenge:	Large language models (LLMs) have shown strong capabilities, enabling concise, context-aware answers in question answering tasks.
Approach:	They propose a framework that provides valid uncertainty guarantees for LLMs . they also propose 'model-agnostic' uncertainty estimation method that maintains valid guarantees even under noise.
Outcome:	The proposed method provides valid uncertainty guarantees even under noise.

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Patrícia Schmidtová, Ondrej Dusek, Saad Mahamood

Summarization

Challenge:	a recent study examines the evaluation of hotel highlights in the context of hotel data.
Approach:	They examine evaluation of faithfulness to input data in the context of hotel highlights . they compare traditional metrics, trainable methods, and LLM-as-a-judge approaches .
Outcome:	The results show that simple metrics outperform human judgments on LLM-generated summaries . the results also highlight challenges in crowdsourced evaluations.

Open-DeBias: Toward Mitigating Open-Set Bias in Language Models (2025.findings-emnlp)

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Arti Rani, Shweta Singh, Nihar Ranjan Sahoo, Gaurav Kumar Nayak

SACL: Understanding and Combating Textual Bias in Code Retrieval with Semantic-Augmented Reranking and Localization (2025.findings-emnlp)

Challenge:	Existing approaches to addressing harmful biases in LLMs are limited to predefined categories . a novel, data-efficient, and parameter-efficient debiasing method is proposed to mitigate existing social and stereotypical biase .
Approach:	They propose an open-set bias detection and mitigation method to address harmful biases in text-based QA.
Outcome:	The proposed method improves QA accuracy on Korean BBQ dataset by nearly 48% on ambiguous subsets and 6% on disambiguated ones.

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Dhruv Gupta, Gayathri Ganesh Lakshmy, Yiqing Xie

Jailbreak Distillation: Renewable Safety Benchmarking (2025.findings-emnlp)

Challenge:	a recent study shows that code retrievers exhibit a strong bias towards well-documented code .
Approach:	They propose a framework that augments textual information with semantic information to mask specific features while preserving code functionality.
Outcome:	The proposed framework enhances textual information and reduces bias by augmenting code or structural knowledge with semantic information.

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Jingyu Zhang, Ahmed Elgohary, Xiawei Wang, A S M Iftekhar, Ahmed Magooda, Benjamin Van Durme, Daniel Khashabi, Kyle Jackson

Uncertainty-Aware Answer Selection for Improved Reasoning in Multi-LLM Systems (2025.findings-emnlp)

Challenge:	a new framework for safety benchmarking is being developed for large language models . the framework "distills" jailbreak attacks into high-quality and easily-updatable safety benchmarks .
Approach:	They propose a framework that "distills" jailbreak attacks into high-quality safety benchmarks.
Outcome:	The proposed framework "distills" jailbreak attacks into high-quality safety benchmarks . it requires minimal human effort to rerun the pipeline and produce updated benchmarks.

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Aakriti Agrawal, Rohith Aralikatti, Anirudh Satheesh, Souradip Chakraborty, Amrit Singh Bedi, Furong Huang

GreekBarBench: A Challenging Benchmark for Free-Text Legal Reasoning and Citations (2025.findings-emnlp)

Challenge:	Existing approaches to selecting reliable responses from multiple LLMs often depend on external verifiers, human evaluators, or self-consistency techniques.
Approach:	They propose a calibrated log-likelihood-based selection framework to improve multi-LLM performance.
Outcome:	The proposed method outperforms majority voting and exceeds self-consistency performance when using a large number of model calls.

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Odysseas S. Chlapanis, Dimitris Galanis, Nikolaos Aletras, Ion Androutsopoulos

Pi-SQL: Enhancing Text-to-SQL with Fine-Grained Guidance from Pivot Programming Languages (2025.findings-emnlp)

Challenge:	GreekBarBench evaluates LLMs on legal questions across five different legal areas from the Greek Bar exams.
Approach:	They propose a three-dimensional scoring system and an LLM-as-a-judge approach to tackle the challenges of free-text evaluation.
Outcome:	The proposed system uses an LLM-as-a-judge approach to evaluate LLMs on legal questions across five legal areas from the Greek Bar exams.

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Yongdong Chi, Hanqing Wang, Yun Chen, Yan Yang, Jian Yang, Zonghan Yang, Xiao Yan, Guanhua Chen

RAC: Efficient LLM Factuality Correction with Retrieval Augmentation (2025.findings-emnlp)

Challenge:	Existing prompt-based methods craft meticulous text guidelines and examples to facilitate SQL generation, but their accuracy is hindered by the large semantic gap between the texts and the low-resource SQL programs.
Approach:	They propose to use Python as a pivot to bridge between natural language query and SQL program.
Outcome:	The proposed method improves the execution accuracy of the best-performing baseline by up to 3.20.

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Changmao Li, Jeffrey Flanigan

Does It Run and Is That Enough? Revisiting Text-to-Chart Generation with a Multi-Agent Approach (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) exhibit impressive results across a wide range of tasks, yet they can often produce factually incorrect outputs.
Approach:	They propose a low-latency post-correction method that decomposes the LLM’s output into atomic facts and applies a fine-grained verification and correction process with retrieved content to verify and correct the Llm-generated output.
Outcome:	The proposed method has greatly reduced latency and token consumption up to 7x compared to previous state-of-the-art methods with similar or better performance.

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James Ford, Anthony Rios

GeLoRA: Geometric Adaptive Ranks For Efficient LoRA Fine-tuning (2025.findings-emnlp)

Challenge:	15% of generated scripts fail to execute, even after supervised fine-tuning and reinforcement learning.
Approach:	They propose a lightweight multi-agent pipeline that separates drafting, execution, repair, and judgment . the system reduces execution errors to 4.5% within three repair iterations .
Outcome:	The proposed pipeline reduces execution errors to 4.5% within three repair iterations while requiring significantly less compute.

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Abdessalam Ed-dib, Zhanibek Datbayev, Amine M. Aboussalah

Uncovering Scaling Laws for Large Language Models via Inverse Problems (2025.findings-emnlp)

Challenge:	Existing adaptive LoRA methods lack a theoretical foundation to guide this trade-off optimally.
Approach:	They propose a principled approach that estimates the intrinsic dimensionality of hidden data representations to adaptively select LoRA ranks.
Outcome:	Experiments show that GeLoRA outperforms adaptive LoRA methods by up to +1.0% .

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Arun Verma, Zhaoxuan Wu, Zijian Zhou, Xiaoqiang Lin, Zhiliang Chen, Rachael Hwee Ling Sim, Rui Qiao, Jingtan Wang, Nhung Bui, Xinyuan Niu, Wenyang Hu, Gregory Kang Ruey Lau, Zi-Yu Khoo, Zitong Zhao, Xinyi Xu, Apivich Hemachandra, See-Kiong Ng, Bryan Kian Hsiang Low

UIPE: Enhancing LLM Unlearning by Removing Knowledge Related to Forgetting Targets (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have achieved remarkable success across diverse domains.
Approach:	inverse problems can efficiently uncover scaling laws that guide the building of LLMs, authors argue . authors propose brute-force approaches to improve LLM training costs due to high costs .
Outcome:	This paper advocates that inverse problems can efficiently uncover scaling laws that guide the building of LLMs to achieve the desirable performance with significantly better cost-effectiveness.

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Wenyu Wang, Mengqi Zhang, Xiaotian Ye, Zhaochun Ren, Pengjie Ren, Zhumin Chen

FicSim: A Dataset for Multi-Faceted Semantic Similarity in Long-Form Fiction (2025.findings-emnlp)

Challenge:	Existing methods to unlearning large language models focus on forgetting target data while overlooking the impact of logically related knowledge on the effectiveness of unlearning.
Approach:	They propose a method that removes knowledge highly correlated with the forgetting targets and a technique that remove logically related knowledge from the model.
Outcome:	The proposed method significantly improves the performance of the proposed method on the TOFU and WMDP benchmarks.

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Natasha Johnson, Amanda Bertsch, Maria-Emil Deal, Emma Strubell

Masked Diffusion Captioning for Visual Feature Learning (2025.findings-emnlp)

Challenge:	evaluating the usefulness of language models for literary-domain tasks remains challenging due to the cost of fine-grained annotation for long-form texts and data contamination concerns inherent in using public-domain literature.
Approach:	They use a dataset of long-form, recently written fiction to evaluate embedding models . they prioritize author agency and rely on continual, informed author consent .
Outcome:	The proposed dataset of long-form, recently written fiction is compared with existing models on this task.

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Chao Feng, Zihao Wei, Andrew Owens

Diverse Multi-tool Aggregation with Large Language Models for Enhanced Math Reasoning (2025.findings-emnlp)

Challenge:	Unlike autoregressive captioning, the strength of the visual learning signal in MDC does not depend on each token’s position in the sequence, reducing the need for auxiliary objectives.
Approach:	a decoder conditioned on visual features is trained to reconstruct the original text.
Outcome:	masked diffusion captioning (MDC) is a form of image-conditioned captioning that can be applied to visual tasks.

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Bohan Yao, Vikas Yadav

Enhancing Goal-oriented Proactive Dialogue Systems via Dynamic Multi-dimensional Consistency Optimization (2025.findings-emnlp)

Challenge:	Multi-TAG uses multiple tools to solve complex math problems over multiple reasoning steps.
Approach:	They propose a tool-based LLM framework that leverages multiple tools to solve math problems.
Outcome:	The proposed framework outperforms baselines that use individual tools with the same number of runs and significantly outperformed standard baselines.

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Didi Zhang, Yaxin Fan, Peifeng Li, Qiaoming Zhu

Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey (2025.findings-emnlp)

Challenge:	Existing work on goal-oriented proactive dialogue systems failed to address the multi-dimensional consistency issue between generated responses and key contextual elements.
Approach:	They propose a Dynamic Multi-dimensional Consistency Reinforcement Learning framework which measures the impact of each consistency dimension on overall dialogue quality and provides feedback to improve response quality.
Outcome:	The proposed framework significantly improves the consistency of generated responses on two datasets.

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Zirui Song, Bin Yan, Yuhan Liu, Miao Fang, Mingzhe Li, Rui Yan, Xiuying Chen

Who’s the Author? How Explanations Impact User Reliance in AI-Assisted Authorship Attribution (2025.findings-emnlp)

Challenge:	specialized LLMs are often limited in domain-specific applications that require specialized knowledge.
Approach:	They provide a comprehensive overview of four key methods to enhance large language models by integrating domain-specific knowledge.
Outcome:	The proposed methods are categorized into four key approaches: dynamic knowledge injection, static knowledge embedding, modular adapters, and prompt optimization.

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Calvin Bao, Connor Baumler, Hal Daumé Iii, Marine Carpuat

UniSpeaker: A Unified Approach for Multimodality-driven Speaker Generation (2025.findings-emnlp)

Challenge:	despite growing interest in explainable NLP, it remains unclear how explanation strategies shape user behavior in tasks like authorship identification.
Approach:	They propose two explanation types to support their analysis of user behavior . they use example-based style rewrites and feature-based rationales to generate explanations .
Outcome:	The proposed explanations support appropriate reliance, whereas explanations increase AI overreliance, the study finds .

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Zhengyan Sheng, Zhihao Du, Heng Lu, ShiLiang Zhang, Zhen-Hua Ling

On the Fine-Grained Planning Abilities of VLM Web Agents (2025.findings-emnlp)

Challenge:	a new framework for speaker generation is proposed to enable multimodal speaker generation . multimodal cues such as visual appearance, textual descriptions, and other biometric signals are still in its early stages.
Approach:	a new framework is proposed to enable multimodal speaker generation . the framework uses self-distillation to apply speaker disentanglement to speech generation a model is developed .
Outcome:	The proposed framework is the first to support unified voice generation from arbitrary modality combinations.

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Surgan Jandial, Yinong Oliver Wang, Andrea Bajcsy, Fernando De la Torre

Challenge:	Vision-Language Models (VLMs) have shown promise as web agents, yet their planning has been overlooked.
Approach:	They propose to examine VLMs’ ability to understand temporal relationships within web contexts and assess plans of actions across diverse scenarios.
Outcome:	The proposed models exhibit limited performance in the above skills and are not reliable to function as web agents.

InterFeedback: Unveiling Interactive Intelligence of Large Multimodal Models with Human Feedback (2025.findings-emnlp)

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Henry Hengyuan Zhao, Wenqi Pei, Yifei Tao, Haiyang Mei, Mike Zheng Shou

ReFLAIR: Enhancing Multimodal Reasoning via Structured Reflection and Reward-Guided Learning (2025.findings-emnlp)

Challenge:	Existing benchmarks do not test Large Multimodal Models (LMMs) on their interactive intelligence with human users.
Approach:	They propose an interactive framework that can be applied to any LMM and assess their interactive intelligence with human users.
Outcome:	The proposed framework can be applied to any LMM and dataset to assess interactive intelligence with human users.

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Jiazhou Ji, Xinru Lu

ControlText: Unlocking Controllable Fonts in Multilingual Text Rendering without Font Annotations (2025.findings-emnlp)

Challenge:	Existing training methods for large models do not address the trade-off between reflection and accuracy.
Approach:	a unified framework teaches large models to perform structured reflection via an explicit $think re-think answer $ format and hybrid reward learning.
Outcome:	The proposed framework improves model performance on mathematical benchmarks and reduces inference cost by nearly 23%.

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Bowen Jiang, Yuan Yuan, Xinyi Bai, Zhuoqun Hao, Alyson Yin, Yaojie Hu, Wenyu Liao, Lyle Ungar, Camillo Jose Taylor

STA-CoT: Structured Target-Centric Agentic Chain-of-Thought for Consistent Multi-Image Geological Reasoning (2025.findings-emnlp)

Challenge:	a new method for visual text rendering requires glyph annotations to be obtained .
Approach:	They propose a model that integrates diffusion with a text segmentation model to achieve multilingual text rendering using just raw images without font label annotations.
Outcome:	The proposed model can achieve font-controllable multilingual text rendering without label annotations.

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Beibei Yu, Tao Shen, Ling Chen

Can Language Models Follow Multiple Turns of Entangled Instructions? (2025.findings-emnlp)

Challenge:	Reliable multi-image geological reasoning is essential for automating expert tasks in remote-sensing mineral exploration.
Approach:	They propose a framework that orchestrates planning, execution, and verification agents to decompose, ground, and iteratively refine reasoning steps over geological and hyperspectral image sets.
Outcome:	The proposed framework decomposes, ground, and iteratively refines reasoning steps over geological and hyperspectral image sets.

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Chi Han, Xin Liu, Haodong Wang, Shiyang Li, Jingfeng Yang, Haoming Jiang, Zhengyang Wang, Qingyu Yin, Liang Qiu, Changlong Yu, Yifan Gao, Zheng Li, Bing Yin, Jingbo Shang, Heng Ji

How to Generalize the Detection of AI-Generated Text: Confounding Neurons (2025.findings-emnlp)

Challenge:	Despite of significant achievements in improving instruction-following capabilities of large language models, the ability to process multiple potentially entangled or conflicting instructions remains a considerable challenge.
Approach:	They construct multi-turn instruction with 1.1K high-quality multi-turned conversations using the human-in-the-loop approach and examine their capabilities.
Outcome:	The proposed model shows that it is difficult to integrate multiple turns and balance competing objectives when instructions intersect or conflict.

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Claudio Borile, Carlo Abrate

SparsePO: Controlling Preference Alignment of LLMs via Sparse Token Masks (2025.findings-emnlp)

Challenge:	Linguistic and domain confounders introduce spurious correlations, leading to poor out-of-distribution (OOD) performance.
Approach:	They propose a novel post-hoc, neuron-level intervention framework to disentangle AI-generated text detection factors from data-specific biases.
Outcome:	The proposed framework reduces topic-specific biases by encoding individual neurons within transformers-based detectors rather than task-specific signals.

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Fenia Christopoulou, Ronald Cardenas, Gerasimos Lampouras, Haitham Bou Ammar, Jun Wang

We Argue to Agree: Towards Personality-Driven Argumentation-Based Negotiation Dialogue Systems for Tourism (2025.findings-emnlp)

Challenge:	Current direct preference optimization algorithms focus on a strict set of tokens contributing signals of KL divergence and rewards to the loss function.
Approach:	They propose a method that automatically learns to weight the KL divergence and reward corresponding to each token during PO training.
Outcome:	The proposed method achieves +10% and +3% win-rate points in two PO scenarios.

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Priyanshu Priya, Saurav Dudhate, Desai Vishesh Yasheshbhai, Asif Ekbal

Towards the Roots of the Negation Problem: A Multilingual NLI Dataset and Model Scaling Analysis (2025.findings-emnlp)

Challenge:	Argumentation mechanisms are integrated into negotiation dialogue systems to improve conflict resolution and adaptability.
Approach:	They propose a dataset of Argumentation Profile, Preference Profile, and Buying Style Profiles to generate personality-driven dialogues in negotiation dialogue systems.
Outcome:	The proposed task improves argumentation mechanisms and adaptability by aligning interactions with individuals’ preferences and styles.

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Tereza Vrabcová, Marek Kadlčík, Petr Sojka, Michal Štefánik, Michal Spiegel

Improving the Language Understanding Capabilities of Large Language Models Using Reinforcement Learning (2025.findings-emnlp)

Challenge:	Negations are key to determining sentence meaning, making them essential for logical reasoning.
Approach:	They construct and publish two new textual entailment datasets in four languages with paired examples differing in negation.
Outcome:	The results show that increasing the model size may improve the models’ ability to handle negations.

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Sai Ashish Somayajula, Bokai Hu, Qi Cao, Xin Pan, Pengtao Xie

HATECAT-TR: A Hate Speech Span Detection and Categorization Dataset for Turkish (2025.findings-emnlp)

Challenge:	Instruction-fine-tuned large language models (LLMs) under 14B parameters underperform on NLU tasks . we explore a framework to improve the NLU capabilities of LLMs .
Approach:	They propose to use Proximal Policy Optimization to improve NLU capabilities . they frame NLU as a reinforcement learning environment and optimize for reward signals .
Outcome:	The proposed framework outperforms supervised fine-tuning on GLUE and superGLUE tasks.

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Hasan Kerem Şeker, Gökçe Uludoğan, Pelin Önal, Arzucan Özgür

DM-Codec: Distilling Multimodal Representations for Speech Tokenization (2025.findings-emnlp)

Challenge:	a new dataset of Turkish tweets contains 4465 hateful spans . each hateful post is directed at one of eight minority groups .
Approach:	They propose a span-annotated dataset of Turkish tweets containing 4465 hateful spans . each hateful spat is categorized into one of five discourse types .
Outcome:	The proposed dataset contains 4465 hateful spans across 2981 tweets . each span is categorized into one of five discourse types .

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Md Mubtasim Ahasan, Md Fahim, Tasnim Mohiuddin, Akmmahbubur Rahman, Aman Chadha, Tariq Iqbal, M Ashraful Amin, Md Mofijul Islam, Amin Ahsan Ali

LCAN: A Label-Aware Contrastive Attention Network for Multi-Intent Recognition and Slot Filling in Task-Oriented Dialogue Systems (2025.findings-emnlp)

Challenge:	Existing speech tokenization models lack contextual representations for speech synthesis . absence of contextual representation results in elevated WER and WIL scores .
Approach:	They propose a language model-guided distillation method that incorporates contextual information into a comprehensive speech tokenizer.
Outcome:	The proposed method outperforms state-of-the-art tokenization models in reducing WER and WIL scores.

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Shuli Zhang, Zhiqiang You, Xiao Xiang Qi, Peng Liu, Gaode Wu, Kan Xia, Shenguang Huang

Semantic Parsing

Challenge:	Multi-intent utterances processing remains a persistent challenge due to intricate intent-slot dependencies and semantic ambiguities.
Approach:	They propose a label-aware contrastive attention network (LCAN) that integrates label-based attention and contrastive learning strategies to improve semantic understanding and generalization in multi-intent scenarios.
Outcome:	The proposed model improves intent recognition and slot filling performance in multi-intent dialogue systems.

Low-Resource Languages LLM Disinformation is Within Reach: The Case of Walliserdeutsch (2025.findings-emnlp)

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Andrei Kucharavy, Sherine Seppey, Cyril Vallez, Dimitri Percia David, Ljiljana Dolamic

Exploring and Controlling Diversity in LLM-Agent Conversation (2025.findings-emnlp)

Challenge:	a low-resource language lacks fluidity, but its capabilities can be leveraged.
Approach:	They investigate whether a moderately sophisticated attacker can perform an impersonation attack in the Walliserdeutsch dialect .
Outcome:	The proposed attack is performed in the Walliserdeutsch dialect, a low-resource language . the findings highlight the urgency of LLM detectability research in low-source languages.

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KuanChao Chu, Yi-Pei Chen, Hideki Nakayama

Agentic-ToM: Cognition-Inspired Agentic Processing For Enhancing Theory of Mind Reasoning (2025.findings-emnlp)

Challenge:	Adaptive Prompt Pruning (APP) allows users to control diversity via a single parameter.
Approach:	They propose a method that allows users to control diversity via a single parameter . they propose to modularize the utterance generation prompt and reduce contextual information .
Outcome:	The proposed method reduces diversity in dialogues over long-term simulations by reducing contextual information.

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Sneheel Sarangi, Chetan Talele, Hanan Salam

Linguistics & Cognitive NLP

Challenge:	Current models struggle with reasoning about others’ perspectives, limiting their ability to attribute mental states to oneself and others.
Approach:	They propose to embed psychologically-grounded functions into LLMs to enable them to attribute mental states to oneself and others, known as Theory of Mind.
Outcome:	The proposed approach outperforms baselines on three ToM datasets without task-specific modifications.

Can We Edit LLMs for Long-Tail Biomedical Knowledge? (2025.findings-emnlp)

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Xinhao Yi, Jake Lever, Kevin Bryson, Zaiqiao Meng

GeoPQA: Bridging the Visual Perception Gap in MLLMs for Geometric Reasoning (2025.findings-emnlp)

Challenge:	Existing knowledge editing methods can enhance LLMs' performance on long-tail biomedical knowledge, but their performance on high-frequency popular knowledge remains inferior to that on high frequency popular knowledge.
Approach:	They conduct the first comprehensive study to investigate the effectiveness of knowledge editing methods for editing long-tail biomedical knowledge.
Outcome:	The proposed methods improve LLMs' performance on long-tail biomedical knowledge, but their performance on high-frequency popular knowledge remains inferior even after editing.

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Guizhen Chen, Weiwen Xu, Hao Zhang, Hou Pong Chan, Deli Zhao, Anh Tuan Luu, Yu Rong

CM-Align: Consistency-based Multilingual Alignment for Large Language Models (2025.findings-emnlp)

Challenge:	Recent advances in reinforcement learning (RL) have enhanced the reasoning abilities of large language models, but the impact on multimodal LLMs is limited.
Approach:	They propose a two-stage RL framework that enhances visual perception and fosters reasoning capabilities.
Outcome:	The proposed framework improves geometric reasoning by 9.7% and problem-solving by 9.1% compared to direct reasoning training approach.

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Xue Zhang, Yunlong Liang, Fandong Meng, Songming Zhang, Yufeng Chen, Jinan Xu, Jie Zhou

Cache Saver: A Modular Framework for Efficient, Affordable, and Reproducible LLM Inference (2025.findings-emnlp)

Challenge:	Current large language models (LLMs) show a significant performance gap in alignment between English and other languages.
Approach:	They propose a consistency-based method to construct high-quality multilingual preference data for improving multilingual alignment.
Outcome:	The proposed method is based on three LLMs and three common tasks and shows that it performs better than current methods.

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Nearchos Potamitis, Lars Henning Klein, Bardia Mohammadi, Chongyang Xu, Attreyee Mukherjee, Niket Tandon, Laurent Bindschaedler, Akhil Arora

Evaluating Cultural Knowledge and Reasoning in LLMs Through Persian Allusions (2025.findings-emnlp)

Challenge:	Inference is the major cost throughout the lifecycle of a large language model (LLM).
Approach:	They propose a plug-and-play, asynchronous framework that facilitates high-level inference optimizations.
Outcome:	The proposed framework reduces cost and CO2 by 35% across methods, tasks, and LLMs.

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Melika Nobakhtian, Yadollah Yaghoobzadeh, Mohammad Taher Pilehvar

Evolving Stances on Reproducibility: A Longitudinal Study of NLP and ML Researchers’ Views and Experience of Reproducibility (2025.findings-emnlp)

Challenge:	Allusion recognition is a critical test of LLMs' ability to deploy stored information in open-ended, figurative settings.
Approach:	They propose a framework for evaluating Persian literary allusions through annotations and LLM-generated texts incorporating allusion in novel contexts.
Outcome:	The proposed framework evaluates Persian literary allusions through annotations and LLM-generated texts incorporating allusion in novel contexts.

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Craig Thomson, Ehud Reiter, João Sedoc, Anya Belz

KAHAN: Knowledge-Augmented Hierarchical Analysis and Narration for Financial Data Narration (2025.findings-emnlp)

Challenge:	Identical experiments producing different results can be due to variation between samples of evaluation items or evaluators, but it can also be due . poor experimental practice can be mitigated by bringing multiple comparable studies together in systematic reviews that draw conclusions beyond the level of the individual studies.
Approach:	They propose to assess NLP/ML practitioners' views and experience of reproducibility over the past two years.
Outcome:	The results of two identical surveys show that views and experience of reproducibility have changed over the past two years.

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Yajing Yang, Tony Deng, Min-Yen Kan