SCALABILITY

Your Inference Request Will Become a Black Box: Confidential Inference for Cloud-based Large Language Models (2026.acl-long)

Challenge:	DIALIGHT is a toolkit for developing and evaluating multilingual Task-Oriented Dialogue systems.
Approach:	They propose a toolkit for developing and evaluating multilingual Task-Oriented Dialogue systems which facilitates systematic evaluations and comparisons between ToD systems using pretrained language models and those utilising the zero-shot and in-context learning capabilities of Large Language Models.
Outcome:	The toolkit enables systematic evaluations between ToD systems using pretrained language models and those utilising the zero-shot and in-context learning capabilities of Large Language Models (LLMs).

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Chung-ju Huang, Huiqiang Zhao, Yuanpeng He, Lijian Li, Wenpin Jiao, Zhi Jin, Peixuan Chen, Leye Wang

Fairseq S2T: Fast Speech-to-Text Modeling with Fairseq (2020.aacl-demo)

Challenge:	Existing approaches fail to ensure privacy, maintain model performance, and preserve computational efficiency simultaneously.
Approach:	They propose a confidential inference framework that partitions the LLM pipeline between a client-verified Confidential Virtual Machine (CVM) and the public cloud to protect client data without compromising the cloud’s model intellectual property or inference quality.
Outcome:	The proposed framework can defend against state-of-the-art token inference attacks while preserving model privacy, performance, and efficiency.

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Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino

Improving Knowledge Graph Embedding Using Simple Constraints (P18-1)

Challenge:	End-to-end sequence-to sequence (S2S) modeling has witnessed rapid growth in speech-totext (ST) tasks.
Approach:	They introduce fairseq S2T, a fairsq extension for speech-to-text modeling tasks such as end-to end speech recognition and speech-text translation.
Outcome:	The proposed extension provides end-to-end workflows from data pre-processing, model training to offline (online) inference.

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Boyang Ding, Quan Wang, Bin Wang, Li Guo

LUCE: A Dynamic Framework and Interactive Dashboard for Opinionated Text Analysis (2025.coling-demos)

Challenge:	Recent efforts focused on designing more complicated models or incorporating extra information beyond triples.
Approach:	They propose to use non-negativity constraints on entity representations and approximate entailment constraints on relation representations to improve KG embedding.
Outcome:	The proposed model outperforms baseline models on WordNet, Freebase, and DBpedia.

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Omnia Zayed, Gaurav Negi, Sampritha Hassan Manjunath, Devishree Pillai, Paul Buitelaar

Challenge:	LUCE is an advanced dynamic framework for analysing opinionated text . it features computational modules for different elements of opinions, e.g., sentiment/emotion, suggestion, figurative language, hate/toxic speech, and topics.
Approach:	They introduce a dynamic framework with an interactive dashboard for analysing opinionated text . it features computational modules of text classification and extraction for different elements of opinions .
Outcome:	The framework is validated in a relevant environment and its capabilities and performance demonstrated . it features trained models, python-based APIs, and a user-friendly dashboard .

CLEAR: Cross-Lingual Enhancement in Retrieval via Reverse-training (2026.acl-long)

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Seungyoon Lee, Minhyuk Kim, Seongtae Hong, Youngjoon Jang, Dongsuk Oh, Heuiseok Lim

JORA: JAX Tensor-Parallel LoRA Library for Retrieval Augmented Fine-Tuning (2024.acl-demos)

Challenge:	Existing multilingual embedding models often struggle to capture cross-lingual alignment during training.
Approach:	They propose a novel loss function that leverages an English passage as a bridge to strengthen alignments between target language and English.
Outcome:	The proposed model improves retrieval performance across cross-lingual scenarios while minimizing performance degradation in English.

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Anique Tahir, Lu Cheng, Huan Liu

Genetic Instruct: Scaling up Synthetic Generation of Coding Instructions for Large Language Models (2025.acl-industry)

Challenge:	Large Language Models (LLMs) face significant memory constraints when fine-tuning large prompt sequences.
Approach:	They propose a framework for PEFT-compatible fine-tuning of large language models, leveraging distributed training.
Outcome:	The proposed framework improves performance 12x compared to Hugging Face/DeepSpeed implementation with four GPUs while consuming less than half the VRAM per GPU.

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Somshubra Majumdar, Vahid Noroozi, Mehrzad Samadi, Sean Narenthiran, Aleksander Ficek, Wasi Uddin Ahmad, Jocelyn Huang, Jagadeesh Balam, Boris Ginsburg

HiPool: Modeling Long Documents Using Graph Neural Networks (2023.acl-short)

Challenge:	Large Language Models (LLMs) require high quality instruction data for effective alignment, especially in code generation tasks where expert curated datasets are expensive to produce.
Approach:	They propose a scalable algorithm for synthesizing large-scale, high quality coding instructions using evolutionary principles.
Outcome:	The proposed approach generates 7.5 million coding instructions with a small seed population and is highly parallelizable and effective even with weaker generator models.

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Irene Li, Aosong Feng, Dragomir Radev, Rex Ying

Structured Data in NLP

Challenge:	Recent work on pretraining languages have achieved satisfying results in many NLP tasks, but they are still restricted by a pre-defined maximum length.
Approach:	They propose a graph-based method to model sentence-level information using a fixed length and graphs to model intra- and cross-sentence correlations.
Outcome:	The proposed model outperforms baseline models by 2.6% in F1 score, and 4.8% on the longest sequence dataset.

Dialogue Meaning Representation for Task-Oriented Dialogue Systems (2022.findings-emnlp)

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Xiangkun Hu, Junqi Dai, Hang Yan, Yi Zhang, Qipeng Guo, Xipeng Qiu, Zheng Zhang

UnifiedMLLM: Enabling Unified Representation for Multi-modal Multi-tasks With Large Language Model (2025.findings-naacl)

Challenge:	Existing work on dialogue meaning representations is limited in scalability for complex expressions.
Approach:	They propose a pliable and easily extendable representation for task-oriented dialogue . they propose an inheritance hierarchy mechanism focusing on domain extensibility .
Outcome:	The proposed representation can be easily extended to a task-oriented dialogue dataset.

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Zhaowei Li, Wei Wang, YiQing Cai, Qi Xu, Pengyu Wang, Dong Zhang, Hang Song, Botian Jiang, Zhida Huang, Tao Wang

Disordered-DABS: A Benchmark for Dynamic Aspect-Based Summarization in Disordered Texts (2024.findings-emnlp)

Challenge:	Representative models like LLaVA and MiniGPT-4 have great capabilities in various tasks.
Approach:	They propose a unified model to represent various multi-modal tasks using a single representation.
Outcome:	The proposed model outperforms existing models in a variety of tasks while maintaining generality and scalability.

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Xiaobo Guo, Soroush Vosoughi

Summarization

Challenge:	Current research focuses on predefined aspects within structured texts, neglecting complexities of dynamic and disordered environments.
Approach:	They propose a benchmark for dynamic aspect-based summarization tailored to unstructured text.
Outcome:	The proposed benchmark addresses the complexities of dynamic and disordered environments in unstructured text.

stopes - Modular Machine Translation Pipelines (2022.emnlp-demos)

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Pierre Andrews, Guillaume Wenzek, Kevin Heffernan, Onur Çelebi, Anna Sun, Ammar Kamran, Yingzhe Guo, Alexandre Mourachko, Holger Schwenk, Angela Fan

Personalized neural language models for real-world query auto completion (N18-3)

Challenge:	Neural machine translation is a natural language deep learning application that needs data to be trained.
Approach:	They describe a framework that empowers scalability and versatility for research use cases.
Outcome:	The proposed framework empowers scalability and versatility for research use cases.

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Nicolas Fiorini, Zhiyong Lu

Fast and Scalable Dialogue State Tracking with Explicit Modular Decomposition (2021.naacl-main)

Challenge:	Existing popularity-based methods for query auto completion (QAC) are ineffective in predicting unseen queries.
Approach:	They propose to use real-world data to build an end-to-end system that can predict unseen queries by integrating user information.
Outcome:	The proposed methods improve on two separate datasets while increasing diversity while scalability.

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Dingmin Wang, Chenghua Lin, Qi Liu, Kam-Fai Wong

Towards Hierarchical Multi-Step Reward Models for Enhanced Reasoning in Large Language Models (2026.findings-acl)

Challenge:	Existing approaches for dialogue state tracking are mainly based on classification-based and extraction-based methods.
Approach:	They propose a model which incorporates both classification-based and extraction-based methods and integrates four modules to jointly extract dialogue states.
Outcome:	The proposed model outperforms the state-of-the-art models in multi-domain dialogues with many turns of utterances.

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Teng Wang, Jiang Zhangyi, Zhenqi He, Hailei Gong, Shenyang Tong, Wenhan Yang, Zeyu Li, Yanan Zheng, Zifan He, Zewen Ye, Shengjie Ma, Jianping Zhang

Leveraging Codebook Knowledge with NLI and ChatGPT for Zero-Shot Political Relation Classification (2024.acl-long)

Challenge:	Existing Process Reward Models (PRMs) are vulnerable to reward hacking and require expensive, large-scale annotation of reasoning steps.
Approach:	They propose a reward model approach which evaluates both individual and consecutive reasoning steps from fine-grained and coarse-grounded level.
Outcome:	Empirical results show that the proposed model performs better than existing PRMs and is more robust than existing models.

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Yibo Hu, Erick Skorupa Parolin, Latifur Khan, Patrick Brandt, Javier Osorio, Vito D’Orazio

DecIF: Improving Instruction-Following through Decomposition (2026.acl-long)

Challenge:	Existing annotation codebook is labor-intensive for coding events from large datasets.
Approach:	They propose to use existing annotation codebook to classify political relations without extensive annotations.
Outcome:	The proposed methods outperform dictionary-based methods and the existing ontology annotation codebook and improve interpretability and efficiency.

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Tingfeng Hui, Pengyu Zhu, Bowen Ping, Ling Tang, Guanting Dong, Yaqi Zhang, Sen Su

Challenge:	Existing approaches to obtain high-quality instruction-following data rely heavily on existing documents and existing methods.
Approach:	They propose a data synthesis framework, DecIF, which automatically generates accurate and diverse instruction-following data from scratch for supervised fine-tuning and reinforcement learning.
Outcome:	Extensive experiments show that the proposed framework can synthesize accurate instruction-following data for both SFT and RL paradigms compared to baselines.

Quality Controlled Paraphrase Generation (2022.acl-long)

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Elron Bandel, Ranit Aharonov, Michal Shmueli-Scheuer, Ilya Shnayderman, Noam Slonim, Liat Ein-Dor

Welcome to the Real World: Efficient, Incremental and Scalable Key Point Analysis (2023.emnlp-industry)

Challenge:	Recent studies have shown that high quality paraphrases are difficult to generate because of their low flexibility and scalability.
Approach:	They propose a quality-guided controlled paraphrase generation model that allows directly controlling the quality dimensions of the generated paraphrase.
Outcome:	The proposed method generates paraphrases which maintain original meaning while achieving higher diversity than the uncontrolled baseline.

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Lilach Eden, Yoav Kantor, Matan Orbach, Yoav Katz, Noam Slonim, Roy Bar-Haim

Structural Reward Model: Enhancing Interpretability, Efficiency, and Scalability in Reward Modeling (2025.emnlp-industry)

Challenge:	Key Point Analysis (KPA) extracts the main points from opinions and quantifies their prevalence.
Approach:	They propose a key point analysis framework that extracts the main points from opinions and quantifies their prevalence.
Outcome:	The proposed system is able to match sentences to key points over five datasets and demonstrate its performance.

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Xiaoyu Liu, Di Liang, Hongyu Shan, Peiyang Liu, Yonghao Liu, Muling Wu, Yuntao Li, Xianjie Wu, Li Miao, Jiangrong Shen, Minlong Peng

CaBSALLM: Efficient Context-Aware Batch Annotation of Conversational Streams with Large Language Models (2026.acl-short)

Challenge:	Generative RMs (GRMs) lack contextual and background information during inference, leading to incomplete evaluations.
Approach:	They propose a modular and interpretable framework that integrates side-branch models as auxiliary feature generators.
Outcome:	The proposed framework outperforms scalar and saline reward models in robustness and alignment with human preferences.

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Mohammadsadegh Abolhasani, Reza Mousavi, Paul Jen-Hwa Hu

SpeechWeave: Diverse Multilingual Synthetic Text & Audio Data Generation Pipeline for Training Text to Speech Models (2025.acl-industry)

Challenge:	Large-scale annotations of subjective, discourse-dependent social interactions remain a critical bottleneck in computational social science.
Approach:	They propose a pipeline that incorporates lightweight conversational context and a dynamic batching method to improve throughput and scalability.
Outcome:	The proposed pipeline improves throughput and scalability while preserving interpretive depth essential to complex social annotations.

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Karan Dua, Puneet Mittal, Ranjeet Gupta, Hitesh Laxmichand Patel

Speech & Audio in NLP

Challenge:	Text-to-Speech (TTS) training requires extensive and diverse text and speech data.
Approach:	They propose a synthetic speech data generation pipeline that generates multilingual, domain-specific datasets for TTS training.
Outcome:	The proposed pipeline generates data that is 10–48% more diverse than baseline across various linguistic and phonetic metrics, along with speaker-standardized speech audio while generating approximately 97% correctly normalized text.

LightEA: A Scalable, Robust, and Interpretable Entity Alignment Framework via Three-view Label Propagation (2022.emnlp-main)

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Xin Mao, Wenting Wang, Yuanbin Wu, Man Lan

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

Challenge:	Existing EA methods inherit the inborn defects from their neural network lineage: poor interpretability and weak scalability.
Approach:	They propose a neural-free EA framework that can find equivalent entity pairs between KGs.
Outcome:	The proposed framework has impressive scalability, robustness, and interpretability.

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Shichen Li, Jiawei Zhang, Zhongqing Wang, Peifeng Li

Sentiment Analysis

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.

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

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

DisComp: A Two-Stage Prompt Optimization Framework Combining Task-Agnostic and Task-Aware Compression (2025.findings-naacl)

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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Liu Quancai, Haihui Fan, Jinchao Zhang, Lixiangfang Lixiangfang, Lichuanrong Lichuanrong, Bo Li

MUSE: A Multimodal Conversational Recommendation Dataset with Scenario-Grounded User Profiles (2025.findings-acl)

Challenge:	Extended prompts can lead to substantial computational overhead and increased hardware demands, limiting the scalability and efficiency of large language models.
Approach:	They propose a two-stage prompt compression framework that combines task-agnostic and task-based strategies to efficiently compress prompt length without compromising performance.
Outcome:	The proposed framework outperforms task-agnostic and task-specific compression methods on three benchmark datasets and is up to 6.56 faster at inference compared to the best token-level compression method.

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Zihan Wang, Xiaocui Yang, YongKang Liu, Shi Feng, Daling Wang, Yifei Zhang

DeepPavlov Dream: Platform for Building Generative AI Assistants (2023.acl-demo)

Challenge:	Existing research focuses solely on text, leaving a gap with practical applications.
Approach:	They propose to synthesize a multimodal conversational recommendation dataset using multimodal large language models to automatically synthesized data from 7,000 conversations in the Clothing domain.
Outcome:	The proposed dataset contains 83,148 utterances from 7,000 conversations centered around the Clothing domain.

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Diliara Zharikova, Daniel Kornev, Fedor Ignatov, Maxim Talimanchuk, Dmitry Evseev, Ksenya Petukhova, Veronika Smilga, Dmitry Karpov, Yana Shishkina, Dmitry Kosenko, Mikhail Burtsev

DatawiseAgent: A Notebook-Centric LLM Agent Framework for Adaptive and Robust Data Science Automation (2025.emnlp-main)

Challenge:	open-source DeepPavlov Dream Platform is designed for development of complex dialog systems . platform supports modular approach to implementation of conversational agents .
Approach:	open-source DeepPavlov Dream Platform is designed for development of complex dialog systems . platform includes a conversational orchestrator called DeepPvlov Agent to coordinate asynchronous dialog pipeline .
Outcome:	The open-source DeepPavlov Dream Platform is designed for development of complex dialog systems like Generative AI Assistants.

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Ziming You, Yumiao Zhang, Dexuan Xu, Yiwei Lou, Yandong Yan, Wei Wang, Huamin Zhang, Yu Huang

OxyGent: Making Multi-Agent Systems Modular, Observable, and Evolvable via Oxy Abstraction (2026.acl-demo)

Challenge:	Existing large language model (LLM) agents for data science automation are limited by narrow task scopes, limited generalization across tasks and models, and over-reliance on state-of-the-art (SOTA) LLMs.
Approach:	They propose a notebook-centric LLM agent framework for adaptive and robust data science automation.
Outcome:	The proposed framework surpasses baselines such as AutoGen and TaskWeaver in performance tests across diverse data science scenarios and models.

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Junxing Hu, Tianlong Li, Lei Yu, Ai Han

Challenge:	Existing MAS frameworks lack standardized abstractions, leading to low efficiency and repetitive implementation of core functions.
Approach:	They propose an open-source framework that encapsulates agents, tools, and reasoning flows as pluggable atomic components.
Outcome:	The OxyGent framework provides a robust and scalable foundation for multi-agent systems in industrial environments.

Multi-Agent Task-Oriented Dialog Policy Learning with Role-Aware Reward Decomposition (2020.acl-main)

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Ryuichi Takanobu, Runze Liang, Minlie Huang

SAGE: A Search-AuGmented Evaluation of Large Language Models on Free-Form QA (2026.acl-long)

Challenge:	Many studies have applied reinforcement learning to train a dialog policy . but modeling a real-world user simulator is challenging and requires domain expertise .
Approach:	They propose to build dialog policies with two agents as dialog agents to avoid building a user simulator beforehand.
Outcome:	The proposed method can build a system policy and a user policy simultaneously . it can achieve high task success rate through conversational interaction .

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Sher Badshah, Ali Emami, Hassan Sajjad

The Role of Exploration Modules in Small Language Models for Knowledge Graph Question Answering (2025.acl-srw)

Challenge:	Large Language Models (LLMs) are prone to hallucination and rely on static, pre-annotated references for evaluation.
Approach:	They propose a framework to assess large language models without fixed ground-truth answers by iteratively generating web queries and synthesizing external evidence.
Outcome:	The proposed framework achieves substantial to perfect agreement with human evaluations on multiple free-form QA benchmarks.

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Yi-Jie Cheng, Oscar Chew, Yun-Nung Chen

Lightweight Domain-Specific Language Model for Real-Time Structuring of Medical Prescriptions (2026.eacl-industry)

Challenge:	Existing methods to integrate knowledge graphs into large language models often rely on proprietary or extremely large models .
Approach:	They propose to integrate knowledge graphs into reasoning processes of large language models . they propose to use simple and efficient exploration modules to handle knowledge graph traversal .
Outcome:	The proposed modules improve the performance of small language models on knowledge graph question answering tasks.

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Jonathan Pattin Cottet, Véronique Eglin, Alex Aussem

SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-Augmented Generation (2024.findings-emnlp)

Challenge:	Existing language models ignore layout information, rely on expensive image-based architectures, or cannot operate under privacy and hardware constraints.
Approach:	They propose a lightweight, privacy-preserving transformer specifically designed for Entity Extraction (EE) and Entity Linking (EL) in french medical prescriptions.
Outcome:	The proposed model matches or surpasses larger document-understanding models on strict extraction metrics while maintaining essential spatial cues.

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Minda Hu, Licheng Zong, Hongru Wang, Jingyan Zhou, Jingjing Li, Yichen Gao, Kam-Fai Wong, Yu Li, Irwin King

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

Challenge:	Existing retrieval-augmented approaches to large language models face performance limitations due to the lack of publicly available training data.
Approach:	They propose a plug-and-play LLM-based retrieval method called Self-Rewarding Tree Search based on Monte Carlo Tree Search and a self-rewarding paradigm to address these limitations.
Outcome:	The proposed method improves the performance of the BM25 retriever and surpasses the baseline of self-reflection in both efficiency and scalability.

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

SFTMix: Elevating Language Model Instruction Tuning with Mixup Recipe (2026.acl-long)

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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Yuxin Xiao, Shujian Zhang, Marzyeh Ghassemi, Wenxuan Zhou

Contextualized Sparse Representations for Real-Time Open-Domain Question Answering (2020.acl-main)

Challenge:	Efforts to improve instruction tuning often focus on higher-quality supervised fine-tuning datasets, typically requiring data filtering with proprietary LLMs or human annotation.
Approach:	They propose a Mixup-based recipe that elevates LLM instruction tuning without relying on well-curated datasets.
Outcome:	The proposed model improves instruction-following and healthcare-specific tasks with consistent improvements across LLM families and SFT datasets.

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Jinhyuk Lee, Minjoon Seo, Hannaneh Hajishirzi, Jaewoo Kang

Chain-of-Skills: A Configurable Model for Open-Domain Question Answering (2023.acl-long)

Challenge:	Existing phrase retrieval models suffer from low accuracy due to limited scalability and speed . 'Open-domain question answering' is a task of answering generic factoid questions by looking up a large knowledge source, typically unstructured text corpora such as Wikipedia.
Approach:	They aim to augment existing phrase retrieval models with contextualized sparse representations to improve the quality of each phrase embedding.
Outcome:	The proposed model improves CuratedTREC and SQuAD-Open by 4% and 45x faster inference speeds over the existing model.

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Kaixin Ma, Hao Cheng, Yu Zhang, Xiaodong Liu, Eric Nyberg, Jianfeng Gao

CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation (2023.emnlp-main)

Challenge:	Using customized retrieval models, model transferability and scalability are limited.
Approach:	They propose a modular retrieval model where individual modules correspond to key skills that can be reused across datasets.
Outcome:	The proposed model outperforms self-supervised retrievers in zero-shot evaluations and achieves state-of-the-art fine-tuned retrieval performance on NQ, HotpotQA and OTT-QA.

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Minzhi Li, Taiwei Shi, Caleb Ziems, Min-Yen Kan, Nancy Chen, Zhengyuan Liu, Diyi Yang

COSIGN: Contextual Facts Guided Generation for Knowledge Graph Completion (2024.naacl-long)

Challenge:	Annotated data plays a critical role in training models and evaluating their performance.
Approach:	They propose a paradigm for Human-LLM co-annotation of unstructured texts at scale that utilizes uncertainty to estimate LLMs’ annotation capability.
Outcome:	The proposed model outperforms existing models on many text-annotation tasks with up to 21% performance improvement over random baseline.

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Jinpeng Li, Hang Yu, Xiangfeng Luo, Qian Liu

Structured Data in NLP

Challenge:	Existing methods for knowledge graph completion (KGC) are limited in generality and scalability due to poor contextual facts.
Approach:	They propose a contextual facts collector and contextual facts organizer to enhance the inference ability of GM-based methods for various KGC tasks.
Outcome:	The proposed model outperforms state-of-the-art methods in terms of performance.

Condensing Multilingual Knowledge with Lightweight Language-Specific Modules (2023.emnlp-main)

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Haoran Xu, Weiting Tan, Shuyue Li, Yunmo Chen, Benjamin Van Durme, Philipp Koehn, Kenton Murray

Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering (2020.emnlp-main)

Challenge:	Existing methods to boost performance in multilingual models but scalability is difficult to manage.
Approach:	They propose a method that incorporates language-specific (LS) modules to boost model performance.
Outcome:	The proposed method outperforms state-of-the-art methods while outperforming existing methods.

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Yanlin Feng, Xinyue Chen, Bill Yuchen Lin, Peifeng Wang, Jun Yan, Xiang Ren

Question Answering

Challenge:	Existing work on augmenting question answering models with external knowledge (e.g., knowledge graphs) lacks transparency into the model’s prediction rationale.
Approach:	They propose a knowledge-aware approach that equips pre-trained language models with a multi-hop relational reasoning module that performs multi-relational reasoning over subgraphs extracted from external knowledge graphs.
Outcome:	The proposed model performs multi-hop, multi-relational reasoning over subgraphs extracted from external knowledge graphs.

Evolutionary Search for Automated Design of Uncertainty Quantification Methods (2026.acl-srw)

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Mikhail Seleznyov, Daniil Korbut, Viktor Moskvoretskii, Oleg Somov, Alexander Panchenko, Elena Tutubalina

Explainability & Interpretability in NLP

Challenge:	Qualitative analysis reveals that different LLMs employ qualitatively distinct evolutionary strategies for automating, interpretable hallucination detector design.
Approach:	They apply LLM-powered evolutionary search to discover unsupervised UQ methods represented as Python programs and apply them to atomic claim verification.
Outcome:	The proposed methods outperform strong manually-designed baselines while generalizing robustly out-of-distribution.

AWARE: Agentic Knowledge Warehousing for Contextual Intelligence (2026.findings-acl)

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Hongjin Qian, Siqi Bao, Zhao Cao, Zheng Liu

Breaking the Generator Barrier: Disentangled Representation for Generalizable AI-Text Detection (2026.acl-long)

Challenge:	Large language models excel in information seeking tasks, but their knowledge is limited in coverage and timeliness.
Approach:	They propose an agentic knowledge warehousing framework that transforms unstructured data into minimal, task-conditioned knowledge representations consumable by LLMs.
Outcome:	Experiments on GAIA, WebWalker, and BrowseComp-Plus show improvements over baselines.

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Xiao Pu, Zepeng Cheng, Lin Yuan, Yu Wu, Xiuli Bi

Re-Align: Aligning Vision Language Models via Retrieval-Augmented Direct Preference Optimization (2025.emnlp-main)

Challenge:	a rapid proliferation of large language models (LLMs) generate text that increasingly resembles human writing . this makes it difficult to capture subtle cues that distinguish AI-generated content from human-written content .
Approach:	They propose a framework that disentangles AI-detection semantics from generator-aware artifacts by latent encoding and perturbation-based regularization.
Outcome:	The proposed framework disentangles AI-detection semantics from generator-aware artifacts on 20 representative LLMs across 7 categories.

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Shuo Xing, Peiran Li, Yuping Wang, Ruizheng Bai, Yueqi Wang, Chan-Wei Hu, Chengxuan Qian, Huaxiu Yao, Zhengzhong Tu

FIDELITY: Fine-grained Interpretable Distillation for Effective Language Insights and Topic Yielding (2025.findings-naacl)

Challenge:	emergence of large Vision Language Models (VLMs) has broadened the capabilities of single-modal Large Language Model (LLM) but VLMs are prone to significant hallucinations, especially in the form of cross-modal inconsistencies.
Approach:	They propose a new alignment framework that leverages image retrieval to integrate both textual and visual preference signals.
Outcome:	The proposed framework mitigates hallucinations more effectively than previous methods . it maintains robustness and scalability across a wide range of VLM sizes and architectures .

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Divyansh Singh, Brodie Mather, Demi Zhang, Patrick Lehman, Justin Ho, Bonnie J Dorr

Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures (P18-1)

Challenge:	Existing methods for topic modeling generate contextually specific and semantically intuitive topics, especially in dynamic environments and low-resource languages.
Approach:	They propose a hybrid method that combines topic modeling and text summarization to produce fine-grained, semantically rich, and contextually relevant output.
Outcome:	FIDELITY outperforms traditional models in topic diversity, similarity, and ability to process new, unseen documents.

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Wenqiang Lei, Xisen Jin, Min-Yen Kan, Zhaochun Ren, Xiangnan He, Dawei Yin

Paraphrasing

Challenge:	Existing solutions to task-oriented dialogue systems follow pipeline designs which introduces complexity and fragility.
Approach:	They propose a novel sequence-to-sequence (seq2sequ) model which tracks dialogue believes and a two stage copynet instantiation which emonstrates good scalability.
Outcome:	The proposed framework outperforms state-of-the-art pipeline-based methods on large datasets and retains satisfactory entity match rate on out-of vocabulary (OOV) cases where pipeline-designed competitors totally fail.

DynClean: Training Dynamics-based Label Cleaning for Distantly-Supervised Named Entity Recognition (2025.findings-naacl)

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Qi Zhang, Huitong Pan, Zhijia Chen, Longin Jan Latecki, Cornelia Caragea, Eduard Dragut

Information Extraction & Text Mining

Challenge:	Existing methods to identify entities using distant annotations are expensive and time-consuming.
Approach:	They propose a training dynamics-based label cleaning approach to characterize distant annotations and an automatic threshold estimation strategy to locate errors in distant labels.
Outcome:	The proposed method outperforms several advanced DS-NER approaches across four datasets.

Enhancing Zero-shot Chain of Thought Prompting via Uncertainty-Guided Strategy Selection (2025.coling-main)

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Shanu Kumar, Saish Mendke, Karody Lubna Abdul Rahman, Santosh Kurasa, Parag Agrawal, Sandipan Dandapat

Automatic Multiple-Choice Question Generation and Evaluation Systems Based on LLM: A Study Case With University Resolutions (2025.coling-main)

Challenge:	Existing methods for chain-of-thought (CoT) prompting are limited by handcrafted demonstrations and trigger phrases are prone to inaccuracies.
Approach:	They propose a method that generates rationales using a trigger phrase to select effective demonstrations without accessing model parameters.
Outcome:	The proposed method outperforms existing methods across four reasoning benchmarks and is robust and scalable.

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Sérgio Silva Mucciaccia, Thiago Meireles Paixão, Filipe Wall Mutz, Claudine Santos Badue, Alberto Ferreira de Souza, Thiago Oliveira-Santos

Long Context Modeling with Ranked Memory-Augmented Retrieval (2026.acl-long)

Challenge:	Multiple choice questions (MCQs) are often used in employee selection and training, but their creation is resource-intensive and requires significant effort and investment.
Approach:	They propose to use large language models and prompt engineering techniques to automate the generation and validation of MCQs.
Outcome:	The proposed system reduces the burden on human resources and enables scalable, cost-effective MCQ generation.

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Ghadir Alselwi, Hao Xue, Shoaib Jameel, Basem Suleiman, Flora D. Salim, Imran Razzak

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

Challenge:	Large Language Models (LLMs) face a fundamental limitation in processing long-context scenarios due to quadratic complexity of attention mechanisms and increasing memory demands during generation.
Approach:	They propose a framework that dynamically ranks memory entries based on relevance . ERMAR employs a relevance scoring mechanism and a pointwise re-ranking model for key-value embeddings .
Outcome:	The proposed framework achieves state-of-the-art performance on benchmarks . it uses historical usage patterns and adaptive retrieval to improve performance .

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

Generative Dense Retrieval: Memory Can Be a Burden (2024.eacl-long)

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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Peiwen Yuan, Xinglin Wang, Shaoxiong Feng, Boyuan Pan, Yiwei Li, Heda Wang, Xupeng Miao, Kan Li

Text Clustering

Challenge:	Empirical results show that Generative Dense Retrieval (GDR) achieves an average of 3.0 R@100 improvement on NQ dataset under multiple settings and has better scalability.
Approach:	They propose a Generative Dense Retrieval paradigm that auto-decodes document identifiers given a query and uses memory to avoid memory confusion.
Outcome:	Empirical results show that the proposed paradigm improves on the small-scale corpora and improves scalability.

Efficient Latent Variable Modeling for Knowledge-Grounded Dialogue Generation (2023.findings-emnlp)

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Gunsoo Han, Daejin Jo, Daniel Nam, Eunseop Yoon, Taehwan Kwon, Seungeun Rho, Kyoung-Woon On, Chang Yoo, Sungwoong Kim

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

Challenge:	Existing knowledge-grounded dialogue generation algorithms require annotated knowledge to generate a response grounded on the retrieved knowledge.
Approach:	They propose an efficient algorithm for latent variable modeling that leverages large amount of dialogue data.
Outcome:	The proposed algorithm outperforms the supervised learning algorithm on knowledge-grounded dialogue datasets while maintaining efficiency and scalability.

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

GENDEX: Generative Data Augmentation Strategy Leveraging External Data for Abstractive Dialogue Summarization (2024.findings-acl)

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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Sangwon Park, Hongseok Choi, Dongha Choi, Hyunju Lee

Rethinking Reading Order: Toward Generalizable Document Understanding with LLM-based Relation Modeling (2026.eacl-long)

Challenge:	Existing methods to summarize text data are limited by the lack of data.
Approach:	They propose a method that uses external data to generate synthetic dialogues from short texts containing people and their interpersonal interactions.
Outcome:	The proposed method shows robust performance, generalizability, and scalability regardless of complexity of dialogues.

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Weishi Wang, Hengchang Hu, Daniel Dahlmeier

Challenge:	Document understanding requires modeling structural and semantic relationships between layout elements within the document without human supervision.
Approach:	They propose a cost-effective paradigm that leverages large language models to infer global RO and inter-element layout relations without human supervision.
Outcome:	Experiments on Semantic Entity Recognition, Entity Linking, and Document Question Answering show that the proposed model improves on baseline models while preserving the robustness of existing models.

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

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

Can LLMs Facilitate Interpretation of Pre-trained Language Models? (2023.emnlp-main)

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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Basel Mousi, Nadir Durrani, Fahim Dalvi

ConveRT: Efficient and Accurate Conversational Representations from Transformers (2020.findings-emnlp)

Challenge:	Existing methods to uncover knowledge encoded within pre-trained language models are limited in terms of scalability and scope of interpretation.
Approach:	They propose to use a large language model, ChatGPT, as an annotation tool . they demonstrate that ChatGPt produces accurate and semantically richer annotations .
Outcome:	The proposed method produces accurate and semantically richer annotations compared to human annotations.

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Matthew Henderson, Iñigo Casanueva, Nikola Mrkšić, Pei-Hao Su, Tsung-Hsien Wen, Ivan Vulić

LIRE: listwise reward enhancement for preference alignment (2024.findings-acl)

Challenge:	ConveRT is a pretraining framework for conversational AI that is computationally heavy, slow, and expensive to train.
Approach:	They propose a pretraining framework for conversational tasks that is efficient, lightweight, and inexpensive.
Outcome:	The proposed model achieves state-of-the-art performance across widely established responses . it trains substantially faster than existing state- of-the art models .

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Mingye Zhu, Yi Liu, Lei Zhang, Junbo Guo, Zhendong Mao

Challenge:	prevailing approaches to preference alignment focus on pairwise comparisons, with limited exploration into multi-response scenarios.
Approach:	They propose a listwise reward enhancement approach that integrates offline rewards of multiple responses into a streamlined listwise framework.
Outcome:	The proposed approach outperforms existing methods on dialogue and summarization tasks with good transferability to out-of-distribution data.

Segment-Level Diffusion: A Framework for Controllable Long-Form Generation with Diffusion Language Models (2025.acl-long)

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Xiaochen Zhu, Georgi Karadzhov, Chenxi Whitehouse, Andreas Vlachos

Tricking Retrievers with Influential Tokens: An Efficient Black-Box Corpus Poisoning Attack (2025.naacl-long)

Challenge:	Diffusion models have shown promise in text generation, but often struggle with generating long, coherent, and contextually accurate text.
Approach:	They propose a framework that enhances diffusion-based text generation through text segmentation, robust representation training with adversarial and contrastive learning, and improved latent-space guidance.
Outcome:	The proposed framework improves diffusion-based text generation and improves scalability and fluency.

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Cheng Wang, Yiwei Wang, Yujun Cai, Bryan Hooi

Rethinking Meeting Effectiveness: A Benchmark and Framework for Temporal Fine-grained Automatic Meeting Effectiveness Evaluation (2026.acl-long)

Challenge:	Existing methods for crafting adversarial passages are slow and computationally expensive, requiring either access to retriever’s gradients or large computational resources.
Approach:	They propose a method that leverages two key characteristics of retrievers: insensitivity to token order and bias towards influential tokens to generate effective adversarial passages.
Outcome:	The proposed method achieves superior efficiency and scalability compared to existing methods while maintaining comparable or better attack success rates across multiple datasets.

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Yihang Li, Chenhui Chu

Challenge:	Current methods for evaluating meeting effectiveness rely on post-hoc surveys that yield a single coarse-grained score for an entire meeting.
Approach:	They propose a new paradigm for evaluating meeting effectiveness centered on novel criteria and temporal fine-grained approach . they introduce a meta-evaluation dataset containing 2,459 human-annotated segments from 130 AMI Corpus meetings.
Outcome:	The proposed framework can be used to evaluate meeting effectiveness across different meeting types and from business scenarios to unstructured discussions.

Revisiting the Test-Time Scaling of o1-like Models: Do they Truly Possess Test-Time Scaling Capabilities? (2025.acl-long)

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Zhiyuan Zeng, Qinyuan Cheng, Zhangyue Yin, Yunhua Zhou, Xipeng Qiu

Challenge:	Longer CoTs of o1-like models do not consistently enhance accuracy, causing performance degradation.
Approach:	They propose a method that combines parallel scaling strategies with CoT length characteristics to improve models’ test-time scalability.
Outcome:	The proposed method improves models’ test-time scalability compared to majority voting approaches.

pFedGPT: Hierarchically Optimizing LoRA Aggregation Weights for Personalized Federated GPT Models (2025.emnlp-main)

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Zhanming Shen, Tianqi Xu, Hao Wang, Jian Li, Miao Pan

GuideDog: A Real-World Egocentric Multimodal Dataset for Blind and Low-Vision Accessibility-Aware Guidance (2026.acl-long)

Challenge:	Existing methods for fine-tuning Large Language Models (LLMs) struggle with data heterogeneity and adapt shared global knowledge to individual client needs.
Approach:	They propose a framework that leverages Hierarchical Bayesian Optimization (HBO) for fine-grained, personalized LoRA aggregation.
Outcome:	The proposed framework achieves state-of-the-art (SOTA) performance on personalized FL benchmarks while introducing only minimal (approx. 4%) additional optimization overhead.

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Junhyeok Kim, Jaewoo Park, Junhee Park, Sangeyl Lee, Jiwan Chung, Jisung Kim, Ji Hoon Joung, Youngjae Yu

Gen-SQL: Efficient Text-to-SQL By Bridging Natural Language Question And Database Schema With Pseudo-Schema (2025.coling-main)

Challenge:	Recent advances in multimodal large language models (MLLMs) offer new opportunities for higher-level scene understanding, but they require labor-intensive, expert annotation.
Approach:	They propose a dataset that combines 2K human-verified images with 22K image-description pairs to provide a more accurate representation of pedestrian scenes.
Outcome:	The proposed dataset improves scalability while maintaining quality.

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Jie Shi, Bo Xu, Jiaqing Liang, Yanghua Xiao, Jia Chen, Chenhao Xie, Peng Wang, Wei Wang

MegaAgent: A Large-Scale Autonomous LLM-based Multi-Agent System Without Predefined SOPs (2025.findings-acl)

Challenge:	Recent studies have shifted paradigms and leveraged Large Language Models (LLMs) to tackle the challenging task of Text-to-SQL.
Approach:	They propose a framework that leverages large language models to generate SQL queries . they exploit prior knowledge from the LLM to enhance embedding-based retriever .
Outcome:	The proposed method improves embedding-based retriever and reduces cost.

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Qian Wang, Tianyu Wang, Zhenheng Tang, Qinbin Li, Nuo Chen, Jingsheng Liang, Bingsheng He

Learning to Reason from Feedback at Test-Time (2025.acl-long)

Challenge:	Existing multi-agent systems lack agent coordination and rely on predefined procedures . existing systems lack adaptive task coordination when task is big and complex .
Approach:	They propose a large-scale autonomous LLM-based multi-agent system that generates agents based on task complexity and enables dynamic task decomposition, parallel execution, efficient communication and comprehensive system monitoring.
Outcome:	The proposed system outperforms existing systems in task completion efficiency and scalability.

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Yanyang Li, Michael R. Lyu, Liwei Wang

ConVEx: Data-Efficient and Few-Shot Slot Labeling (2021.naacl-main)

Challenge:	Existing approaches to utilizing feedback are expensive and lack the time to perform iterative interactions with the environment.
Approach:	They propose a novel paradigm that formulates feedback utilization as an optimization problem at test time and a learnable test-time optimizer to effectively exploit feedback.
Outcome:	The proposed paradigm improves scalability and performance on two large language models across four reasoning datasets.

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Matthew Henderson, Ivan Vulić

Dual-Cluster Memory Agent: Resolving Multi-Paradigm Ambiguity in Optimization Problem Solving (2026.acl-long)

Challenge:	ConVEx is an efficient pretraining and fine-tuning neural approach for slot-labeling dialog tasks.
Approach:	They propose an efficient pretraining and fine-tuning neural approach for slot-labeling dialog tasks that uses a pairwise cloze task and reddit data.
Outcome:	The proposed approach is well aligned with its intended use on slot-labeling tasks and can be used across a range of domains and data sets.

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Xinyu Zhang, Yuchen Wan, Boxuan Zhang, Zesheng Yang, Lingling Zhang, Bifan Wei, Jun Liu

Text Clustering

Challenge:	Large Language Models (LLMs) struggle with structural ambiguity in optimization problems, where a single problem admits multiple related but conflicting modeling paradigms, hindering effective solution generation.
Approach:	They propose to use memory to leverage historical solutions in a training-free manner to enhance performance by leveraging generalizable guidance knowledge.
Outcome:	The proposed agent achieves an average performance improvement of 11%-21% over previous agents.

From Phrases to Subgraphs: Fine-Grained Semantic Parsing for Knowledge Graph Question Answering (2025.findings-acl)

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Yurun Song, Xiangqing Shen, Rui Xia

Mixture-of-Domain-Adapters: Decoupling and Injecting Domain Knowledge to Pre-trained Language Models’ Memories (2023.acl-long)

Challenge:	Existing approaches to knowledge graph question answering (KGQA) face semantic misalignment and reasoning noise.
Approach:	They propose a fine-grained semantic parsing framework for KGQA that maps natural language queries to executable logical forms.
Outcome:	The proposed framework achieves 18.5% performance improvement over the SOTA on a multi-hop CWQ dataset.

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Shizhe Diao, Tianyang Xu, Ruijia Xu, Jiawei Wang, Tong Zhang

AutoTaskEval: Towards Domain-Specific and Fine-Grained Evaluation for LLMs (2026.acl-long)

Challenge:	Pre-trained language models demonstrate excellent abilities to understand texts in the generic domain while struggling in a specific domain.
Approach:	They propose to decouple the feed-forward networks of the Transformer architecture into two parts to maintain old-domain knowledge and a mixture-of-adapters gate to inject domain-specific knowledge in parallel.
Outcome:	The proposed method achieves superior performance on in-domain, out-of-domain and knowledge-intensive tasks.

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Qingqing Lyu, Linjuan Wu, Yongliang Shen, Hengwei Liu, Hao Li, Shengpei Jiang, Yin Zhang, Weiming Lu

Challenge:	Existing automated approaches operate within fixed task schemas and often fail to autonomously discover new evaluation dimensions.
Approach:	They propose an automated framework that constructs domain-specific benchmarks directly from unstructured corpora using Bloom’s Taxonomy.
Outcome:	The proposed framework uncovers a broader and more fine-grained task space than expert-curated benchmarks while producing high-quality instances that preserve established model-level evaluation trends.

TReX: Tokenizer Regression for Optimal Data Mixture (2026.eacl-long)

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Inho Won, Hangyeol Yoo, Minkyung Cho, Jungyeul Park, Hoyun Song, KyungTae Lim

Text-guided 3D Human Generation from 2D Collections (2023.findings-emnlp)

Challenge:	Existing approaches to train and inference tokenizers rely on heuristics or large-scale searches to determine optimal data mixtures.
Approach:	They propose a regression-based framework that efficiently predicts the optimal data mixture for tokenizer training.
Outcome:	The proposed model outperforms mixtures based on LLaMA3 and uniform distributions by up to 12% in both in- and out-of-distribution compression efficiency.

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Tsu-Jui Fu, Wenhan Xiong, Yixin Nie, Jingyu Liu, Barlas Oguz, William Wang

Neural network embeddings recover value dimensions from psychometric survey items on par with human data (2026.findings-eacl)

Challenge:	3D human modeling is used for engaging interaction in gaming, film, and animation. however, the customization of characters is crucial for creativity and scalability.
Approach:	They propose a 3D human generation using fashion descriptions to enhance 3D geometry transformation and fine-grained consistency.
Outcome:	The proposed model can generate a 3D human, guided by a fashion description, with high efficiency.

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Max Pellert, Clemens M Lechner, Indira Sen, Markus Strohmaier

E-Verify: A Paradigm Shift to Scalable Embedding-based Factuality Verification (2025.findings-emnlp)

Challenge:	Embedings from large language models can recover structure of human values . quantitative analysis reveals that SQuID addresses the challenge of obtaining negative correlations between dimensions without domain-specific fine-tuning or training data reannotation.
Approach:	They propose to use questionnaire item embeddings to recover human values from PVQ-RR . their results have implications for psychometrics and social science research .
Outcome:	The proposed method explains 55% variance in dimension-dimension similarities compared to human data.

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Zeyang Liu, Jingfeng Xue, Xiuqi Yang, Wenbiao Du, Jiarun Fu, Junbao Chen, Wenjie Guo, Yong Wang

Instruction-Tuning Data Synthesis from Scratch via Web Reconstruction (2025.findings-acl)

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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Yuxin Jiang, Yufei Wang, Chuhan Wu, Xinyi Dai, Yan Xu, Weinan Gan, Yasheng Wang, Xin Jiang, Lifeng Shang, Ruiming Tang, Wei Wang

OS-Kairos: Adaptive Interaction for MLLM-Powered GUI Agents (2025.findings-acl)

Challenge:	Existing methods for generating and curating high-quality instruction-tuning data rely heavily on the quality of seed data or strong assumptions about the structure and content of web documents.
Approach:	They propose a fully automated framework for synthesizing high-quality instruction-tuning (IT) data directly from raw web documents with minimal assumptions.
Outcome:	The proposed framework outperforms state-of-the-art baselines by 16.65% across four instruction-following benchmarks.

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Pengzhou Cheng, Zheng Wu, Zongru Wu, Tianjie Ju, Aston Zhang, Zhuosheng Zhang, Gongshen Liu

Do Not Step Into the Same River Twice: Learning to Reason from Trial and Error (2026.acl-long)

Challenge:	Existing efforts to build GUI agents focused on the autonomous mode have failed to address the problem of over-execution.
Approach:	They propose an adaptive GUI agent that predicts confidence levels at each interaction step and elicits adaptive interaction.
Outcome:	The proposed GUI agent outperforms existing models on a complex dataset and on established benchmarks.

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Chenming Tang, Hsiu-Yuan Huang, Weijie Liu, Clive Bai, Saiyong Yang, Yunfang Wu

Conv-Basis: A New Paradigm for Efficient Attention Inference and Gradient Computation in Transformers (2025.findings-emnlp)

Challenge:	Existing approaches to RLVR train LMs based on their own on-policy responses and are constrained by the initial capability of LM.
Approach:	They propose an approach that hints LMs with their self-made mistakes without external guidance.
Outcome:	The proposed approach outperforms the normal group relative policy optimization and requires no external guidance.

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Yingyu Liang, Heshan Liu, Zhenmei Shi, Zhao Song, Zhuoyan Xu, Jiale Zhao, Zhen Zhuang

VizoMem: A Visual-Textual Memory Framework for Efficient Long-Horizon Reasoning (2026.findings-acl)

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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Weijie Liang, Yuanfeng Song, Xing Chen, Caleb Chen Cao, Sirui Han, Yike Guo

Visual Data in NLP

Challenge:	Existing systems that use long-context modeling incur computational and memory overhead.
Approach:	They propose a visual memory framework that pre-rendered text into structured images and stored as visual notes for agentic systems.
Outcome:	The proposed system reduces token consumption while preserving effective long-term memory recall.

From Word to World: Can Large Language Models be Implicit Text-based World Models? (2026.acl-long)

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Yixia Li, Hongru Wang, Jiahao Qiu, Zhenfei Yin, Dongdong Zhang, Cheng Qian, Zeping Li, Xiaoteng Ma, Guanhua Chen, Heng Ji

Personalized Pieces: Efficient Personalized Large Language Models through Collaborative Efforts (2024.emnlp-main)

Challenge:	Agentic learning increasingly hinges on interaction, yet real-world experience is expensive, limited, and often irreversible at inference time.
Approach:	They propose a framework that reframes language modeling as next-state prediction under interaction.
Outcome:	The proposed framework evaluates world models in text-based environments . it shows that sufficiently trained models capture coherent environment dynamics .

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Zhaoxuan Tan, Zheyuan Liu, Meng Jiang

A General Knowledge Injection Framework for ICD Coding (2025.findings-acl)

Challenge:	Experimental results show Per-Pcs outperforms non-personalized and PEFT retrieval baselines, offering performance comparable to OPPU with significantly lower resource use across six tasks.
Approach:	They propose a framework that allows users to safely share and assemble personalized large language models using their history data.
Outcome:	Experimental results show that Per-Pcs outperforms non-personalized and PEFT retrieval baselines with significantly lower resource use across six tasks.

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Xu Zhang, Kun Zhang, Wenxin Ma, Rongsheng Wang, Chenxu Wu, Yingtai Li, S Kevin Zhou

Challenge:	Existing methods to improve ICD coding focus on a single type of knowledge and design specialized modules that are complex and incompatible with each other.
Approach:	They propose a general knowledge injection framework that integrates three key types of knowledge without specialized design of additional modules.
Outcome:	The proposed framework outperforms baseline models and is comparable to models relying on extra human annotations.

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Tomoyuki Jinno, Kazuki Hayashi, Yusuke Sakai, Hidetaka Kamigaito, Taro Watanabe

Enrich-on-Graph: Query-Graph Alignment for Complex Reasoning with LLM Enriching (2025.emnlp-main)

Challenge:	Existing knowledge probes for pre-trained language models exhibit quadratic time complexity, limiting the size of knowledge graphs used for probing.
Approach:	They propose an embedding-based relational probe that evaluates pre-trained language models' factual knowledge retrieval capabilities.
Outcome:	The proposed probe achieves effective time complexity of linear order O(n), supports rank-based evaluation metrics including Hit@k, handles multi-token entity names and enables probing whilst disambiguating homographic tail-entity names.

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Songze Li, Zhiqiang Liu, Zhengke Gui, Huajun Chen, Wen Zhang

Structured Data in NLP

Challenge:	Existing methods employ resource-intensive, non-scalable workflows reasoning on vanilla KGs, but overlook this gap.
Approach:	They propose a flexible framework that leverages LLMs’ prior knowledge to enrich KGs and bridge the semantic gap between queries and graphs.
Outcome:	The proposed framework bridges the semantic gap between structured knowledge graphs and unstructured queries while ensuring low computational costs, scalability, and adaptability across different methods.

Towards A Better Initial Policy Model For Scalable Long-CoT Reinforcement Learning (2025.findings-acl)

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Bofei Gao, Yejie Wang, Yibo Miao, Ruoyu Wu, Feifan Song, Longhui Yu, Tianyu Liu, Baobao Chang

CERET: Cost-Effective Extrinsic Refinement for Text Generation (2024.naacl-long)

Challenge:	Long-CoT reasoning and reinforcement learning are demonstrating remarkable performance and scalability, however, there is a lack of systematic guidelines for obtaining a better initial policy model.
Approach:	They propose a systematic guideline and a novel Re-RFT method to obtain more efficient reasoning patterns from different initial models.
Outcome:	The proposed method surpasses DeepSeek-R1-Distill-Qwen-14B model by 4.6%, demonstrating its effectiveness and superiority.

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Jason Cai, Hang Su, Monica Sunkara, Igor Shalyminov, Saab Mansour

TopoSHIELD: Reshaping the Flow of Malice via Spatio-Temporal Risk-Aware Topological Evolution in Multi-Agent Systems (2026.findings-acl)

Challenge:	Large Language Models (LLMs) generate incomplete, biased or misleading outputs in their initial attempts.
Approach:	They propose a method for refining text generation that takes into account semantic stability, entailment and inter-sample uncertainty measures.
Outcome:	The proposed method outperforms self-consistency and self-rerank baselines under various task setups by 1.6% and 3.5% respectively.

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Ruiyang Huang, Chenxi Wang, Tinghe Zhang, Fengrui Liu, Jiayan Sun, Haocheng Wang, Yifan Wu

Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion (2020.findings-emnlp)

Challenge:	Multi-agent systems (MAS) inherit general task-solving and instruction-following capabilities, but their interconnectivity introduces significant security risks.
Approach:	They propose a framework that reshapes the flow of malice via risk-aware topological evolution.
Outcome:	Empirically, TopoSHIELD reduces toxicity by 58% on GPT-4o while preserving high utility (>90% success rate).

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Rajarshi Das, Ameya Godbole, Nicholas Monath, Manzil Zaheer, Andrew McCallum

Semi-Supervised Reward Modeling via Iterative Self-Training (2024.findings-emnlp)

Challenge:	Existing methods for learning non-parametric representations of entities and relations are based on tensor factorization or sophisticated neural approaches.
Approach:	They propose a case-based reasoning system that retrieves ‘cases’ that are similar to the given problem and then stores them in its parameters.
Outcome:	The proposed model outperforms state-of-the-art methods on several benchmark datasets and is non-parametric and grows dynamically as new entities and relations arrive in the KB.

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Yifei He, Haoxiang Wang, Ziyan Jiang, Alexandros Papangelis, Han Zhao

MIND: Multimodal Shopping Intention Distillation from Large Vision-language Models for E-commerce Purchase Understanding (2024.emnlp-main)

Challenge:	Reward models capture values and preferences of humans and are used in Reinforcement Learning with Human Feedback (RLHF) Traditionally, training large language models relies on extensive human-annotated preference data, which poses significant challenges in terms of scalability and cost.
Approach:	They propose a method that enhances RM training using unlabeled data.
Outcome:	The proposed approach improves reward models without incurring additional labeling costs on unlabeled datasets.

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Baixuan Xu, Weiqi Wang, Haochen Shi, Wenxuan Ding, Huihao Jing, Tianqing Fang, Jiaxin Bai, Xin Liu, Changlong Yu, Zheng Li, Chen Luo, Qingyu Yin, Bing Yin, Long Chen, Yangqiu Song

Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models (2025.emnlp-main)

Challenge:	Existing methods for acquiring large-scale intentions generate product-centric intentions without product images and incur high costs for scalability.
Approach:	They propose a multimodal framework that allows Large Vision-Language Models to infer purchase intentions from multimodal product metadata and prioritize human-centric ones.
Outcome:	The proposed framework shows that it is robust to different prompts and superior to previous methods.

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Mehdi Ali, Manuel Brack, Max Lübbering, Elias Wendt, Abbas Goher Khan, Richard Rutmann, Alex Jude, Maurice Kraus, Alexander Arno Weber, Felix Stollenwerk, David Kaczér, Florian Mai, Lucie Flek, Rafet Sifa, Nicolas Flores-Herr, Joachim Koehler, Patrick Schramowski, Michael Fromm, Kristian Kersting

FACTS: Table Summarization via Offline Template Generation with Agentic Workflows (2026.findings-acl)

Challenge:	Existing open-source multilingual datasets rely on heuristic filtering methods restricting both their cross-lingual transferability and scalability.
Approach:	They propose a systematic approach that curates diverse and high-quality multilingual data at scale while significantly reducing computational demands.
Outcome:	Evaluated empirically across 35 languages, the proposed approach outperforms current heuristic filtering methods like Fineweb2 and improves model training quality and retention rates.

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Ye Yuan, Mohammad Amin Shabani, Siqi Liu

Instance-Selection-Inspired Undersampling Strategies for Bias Reduction in Small and Large Language Models for Binary Text Classification (2025.acl-long)

Challenge:	Existing methods for query-focused table summarization struggle with complex reasoning and token-limit issues.
Approach:	They propose a Fast, Accurate, and Privacy-Compliant table summarization approach via Offline Template Generation.
Outcome:	The proposed method outperforms baseline methods on widely-used benchmarks.

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Guilherme Fonseca, Washington Cunha, Gabriel Prenassi, Marcos André Gonçalves, Leonardo Chaves Dutra Da Rocha

CtrlNews: LLM-based Multi-Agent Controllable News Writing via Knowledge Gravitational Field (2025.findings-emnlp)

Challenge:	Existing methods to mitigate class imbalanced datasets are limited by existing methods.
Approach:	They propose two undersampling methods inspired by state-of-the-art Instance Selection techniques to mitigate class imbalance bias in ATC.
Outcome:	The proposed methods reduce classifier bias (56%) across all datasets without effectiveness loss while improving efficiency (1.6x speedup), scalability and reducing carbon emissions (up to 50%).

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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.

Scaling External Knowledge Input Beyond Context Windows of LLMs via Multi-Agent Collaboration (2026.acl-long)

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Zijun Liu, Zhennan Wan, Peng Li, Ming Yan, Fei Huang, Yang Liu

WPO: Enhancing RLHF with Weighted Preference Optimization (2024.emnlp-main)

Challenge:	Existing context window extension methods obstruct scaling external knowledge input.
Approach:	They develop a multi-agent framework to overcome two core bottlenecks in existing agent orchestration designs.
Outcome:	The proposed framework overcomes two core bottlenecks and improves inference-time knowledge integration without longer-context training.

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Wenxuan Zhou, Ravi Agrawal, Shujian Zhang, Sathish Reddy Indurthi, Sanqiang Zhao, Kaiqiang Song, Silei Xu, Chenguang Zhu

Simple Recurrent Units for Highly Parallelizable Recurrence (D18-1)

Challenge:	Off-policy preference optimization suffers from a distributional gap between the policy used for data collection and the target policy, leading to suboptimal optimization.
Approach:	They propose a method to simulate on-policy learning with off-police preference data.
Outcome:	The proposed method outperforms Direct Preference Optimization (DPO) by up to 5.6% on Alpaca Eval 2 and MT-bench.

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Tao Lei, Yu Zhang, Sida I. Wang, Hui Dai, Yoav Artzi

Selecting and Merging: Towards Adaptable and Scalable Named Entity Recognition with Large Language Models (2025.acl-long)

Challenge:	recurrent neural networks scale poorly due to the intrinsic difficulty in parallelizing their state computations.
Approach:	They propose a simple recurrent unit that provides expressive recurrence and allows highly parallel implementation.
Outcome:	The proposed model achieves 5—9x speed-up over cuDNN-optimized LSTM on classification and question answering datasets and delivers stronger results than LS and convolutional models.

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Zhuojun Ding, Wei Wei, Chenghao Fan

Uni-Retrieval: A Multi-Style Retrieval Framework for STEM’s Education (2025.acl-long)

Challenge:	Existing approaches to align large language models with information extraction tasks are costly and not all training data benefits target domains.
Approach:	They propose a framework which dynamically Selects and Merges expert models at inference time and combines experts beneficial to target domains.
Outcome:	The proposed framework outperforms the unified model by 10% on multiple benchmarks.

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Yanhao Jia, Xinyi Wu, Li Hao, QinglinZhang QinglinZhang, Yuxiao Hu, Shuai Zhao, Wenqi Fan

UniRPG: Unified Discrete Reasoning over Table and Text as Program Generation (2022.emnlp-main)

Challenge:	Current retrieval models focus on natural text-image retrieval, which is insufficient for STEM education contexts due to ambiguities in the retrieval process.
Approach:	They propose a diverse expression retrieval task tailored to educational scenarios . they extract query style features as prototypes and build a continuously updated Prompt Bank .
Outcome:	The proposed model outperforms existing retrieval models in most retrieval tasks.

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Yongwei Zhou, Junwei Bao, Chaoqun Duan, Youzheng Wu, Xiaodong He, Tiejun Zhao

Modeling Context With Linear Attention for Scalable Document-Level Translation (2022.findings-emnlp)

Challenge:	Existing methods for question answering using knowledge resources are mixed-of-experts and semantic parsing-based.
Approach:	They propose a semantic-parsing-based approach to perform Unified discrete Reasoning over heterogeneous knowledge resources as Program Generation.
Outcome:	The proposed approach improves interpretability and scalability over table and text . it achieves promising performance on the TAT-QA dataset without annotation .

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Zhaofeng Wu, Hao Peng, Nikolaos Pappas, Noah A. Smith

Multiplex Word Embeddings for Selectional Preference Acquisition (D19-1)

Challenge:	Document-level machine translation models lack quadratic complexity in the sequence length due to their attention layers.
Approach:	They evaluate a recent linear attention model with a sentential gate to promote a recency inductive bias and compare it to open-source document translation.
Outcome:	The proposed model significantly improves translation quality on IWSLT 2015 and OpenSubtitles 2018 with similar or better BLEU scores.

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Hongming Zhang, Jiaxin Bai, Yan Song, Kun Xu, Changlong Yu, Yangqiu Song, Wilfred Ng, Dong Yu

GuessArena: Guess Who I Am? A Self-Adaptive Framework for Evaluating LLMs in Domain-Specific Knowledge and Reasoning (2025.acl-long)

Challenge:	Existing word embeddings are limited in their ability to represent fixed vectors . instead, they incorporate relational dependencies of different words into their embeddables - a limitation that is addressed by a multiplex model .
Approach:	They propose a word embedding model which incorporates relational dependencies of different words into their embeddables.
Outcome:	The proposed model can be easily extended according to various relations among words.

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Qingchen Yu, Zifan Zheng, Ding Chen, Simin Niu, Bo Tang, Feiyu Xiong, Zhiyu Li

TWIST: Text-encoder Weight-editing for Inserting Secret Trojans in Text-to-Image Models (2025.acl-long)

Challenge:	Existing evaluation methods for large language models rely on static benchmarks and standardized evaluation protocols.
Approach:	They propose an adaptive evaluation framework that integrates dynamic domain knowledge modeling with progressive reasoning assessment to improve evaluation fidelity.
Outcome:	Empirical results show that the framework distinguishes LLMs in terms of domain knowledge coverage and reasoning chain completeness.

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Xindi Li, Zhe Liu, Tong Zhang, Jiahao Chen, Qingming Li, Jinbao Li, Shouling Ji

Knowledge is Not Enough: Injecting RL Skills for Continual Adaptation (2026.acl-long)

Challenge:	Existing Trojan attacks require extensive training data and poor generalization, limiting effectiveness and scalability.
Approach:	They propose a method for embedding Trojans into plugins using a single edit layer . they find that the method reduces modified parameters by 8-fold and cuts injection time to 25 seconds .
Outcome:	The proposed method achieves an average attack success rate of 91%, a 78% improvement over the state-of-the-art (SOTA) method.

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Pingzhi Tang, Yiding Wang, Muhan Zhang

Efficient Temporal Extrapolation of Multimodal Large Language Models with Temporal Grounding Bridge (2024.emnlp-main)

Challenge:	Large Language Models face the "knowledge cutoff" problem because their parametric memory remains frozen after pretraining, preventing them from natively internalizing new information or tools on the fly.
Approach:	They propose a framework that supports modular skill transfer for efficient and effective knowledge adaptation by extracting a domain-agnostic **Skill Vector from a source domain.
Outcome:	Experiments on knowledge-incorporation QA (SQuAD, LooGLE) and agentic tool-use benchmarks show that the proposed framework outperforms state-of-the-art self-editing SFT by 9.9 points.

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Yuxuan Wang, Yueqian Wang, Pengfei Wu, Jianxin Liang, Dongyan Zhao, Yang Liu, Zilong Zheng

Learning Preference Model for LLMs via Automatic Preference Data Generation (2023.emnlp-main)

Challenge:	despite advances in multimodal large language models, the challenge of interpreting long-form videos remains a challenge . despite advancements in video-language benchmarks, the inefficiency in temporal grounding and limited pre-trained context window size remains .
Approach:	They propose a framework that bootstraps MLLMs with advanced temporal grounding capabilities and broadens their contextual scope.
Outcome:	The proposed framework significantly enhances the temporal capabilities of existing MLLMs.

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Shijia Huang, Jianqiao Zhao, Yanyang Li, Liwei Wang

Challenge:	Existing training methods for large language models rely on human-annotated data.
Approach:	They propose to learn the preference model for LLMs via automatic preference data generation (AutoPM) using HHH-guided preference data, they show reliability and potential .
Outcome:	The proposed approach enables LLMs to learn human preferences and align with human values.

KNN-Instruct: Automatic Instruction Construction with K Nearest Neighbor Deduction (2024.emnlp-main)

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Jianshang Kou, Benfeng Xu, Chiwei Zhu, Zhendong Mao

InfiniteICL: Breaking the Limit of Context Window Size via Long Short-term Memory Transformation (2025.findings-acl)

Challenge:	Existing methods for generating synthetic instructions for large language models suffer from stale distribution and scalability.
Approach:	They propose a method which incorporates KNN deduction to produce meaningful new instructions by summarizing and learning from existing ones.
Outcome:	The proposed method outperforms all 7B models on the LMSYS leaderboard.

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Bowen Cao, Deng Cai, Wai Lam

DictLLM: Harnessing Key-Value Data Structures with Large Language Models for Enhanced Medical Diagnostics (2024.findings-acl)

Challenge:	InfiniteICL is a framework that parallels context and parameters in large language models with short- and long-term memory in human cognitive systems.
Approach:	They propose a framework that parallels context and parameters in large language models with short- and long-term memory in human cognitive systems and enables infinite context integration.
Outcome:	The proposed framework reduces context length by 90% while achieving 103% average performance of full-context prompting across fact recall, grounded reasoning, and skill acquisition tasks.

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YiQiu Guo, Yuchen Yang, Ya Zhang, Yu Wang, Yanfeng Wang

From Tens of Hours to Tens of Thousands: Scaling Back-Translation for Speech Recognition (2025.emnlp-main)

Challenge:	Structured data processing is a complex and complex process.
Approach:	They propose a framework that captures heterogeneity of structured data using large language models . they propose group positional encoding, hierarchical attention bias and optimal transport alignment layer .
Outcome:	The proposed framework outperforms baseline methods and few-shot GPT-4 on a medical lab report dataset.

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Tianduo Wang, Lu Xu, Wei Lu, Shanbo Cheng

Genius: A Generalizable and Purely Unsupervised Self-Training Framework For Advanced Reasoning (2025.acl-long)

Challenge:	Recent advances in Automatic Speech Recognition (ASR) have been fueled by massive speech corpora, but extending coverage to diverse languages with limited resources remains a formidable challenge.
Approach:	They propose a pipeline that converts large-scale text corpora into synthetic speech using off-the-shelf text-to-speech (TTS) models.
Outcome:	The proposed pipeline generates 500,000 hours of synthetic speech in ten languages and achieves transcription error reductions of over 30%.

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Fangzhi Xu, Hang Yan, Chang Ma, Haiteng Zhao, Qiushi Sun, Kanzhi Cheng, Junxian He, Jun Liu, Zhiyong Wu

RELATE: Generating a linguistically inspired Knowledge Graph for fine-grained emotion classification (2022.lrec-1)

Challenge:	Existing methods for enhancing LLM reasoning rely on supervisory signals . current methods rely heavily on outcome supervision and auxiliary reward models .
Approach:	They propose a gen-eralizable and purely unsupervised self-training framework to enhance LLM reasoning without supervision.
Outcome:	The proposed framework improves LLM reasoning without supervision without external supervision.

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Annika Marie Schoene, Nina Dethlefs, Sophia Ananiadou

Sentiment Analysis

Challenge:	Existing knowledge resources for sentiment analysis (SA) tasks are either large, common-sense knowledge graphs (KGs) that cover a limited amount of polarities/emotions or they are smaller in size (e.g. lexicons) . however, these resources are limited by the low coverage of e.t. and scalability.
Approach:	They propose a new directed KG called ‘RELATE’ which incorporates the benefit of semantics without relying on costly human annotation.
Outcome:	The proposed KG overcomes low coverage of emotions and scalability issues . it is the first KG of its size to cover Ekman’s six basic emotions that are directed towards entities.

MM-Verify: Enhancing Multimodal Reasoning with Chain-of-Thought Verification (2025.acl-long)

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Linzhuang Sun, Hao Liang, Jingxuan Wei, Bihui Yu, Tianpeng Li, Fan Yang, Zenan Zhou, Wentao Zhang

In-Context Reinforcement Learning with Retrieval-Augmented Generation for Text-to-SQL (2025.coling-main)

Challenge:	MM-Verifier and MM Reasoner are a powerful multimodal reasoning model . large language models (LLMs) have demonstrated exceptional performance across tasks spanning myriad domains.
Approach:	They propose a method which combines tree search and verification to generate high-quality chain-of-thought data.
Outcome:	The proposed method outperforms all larger models on the MathCheck, MathVista, and MathVerse benchmarks.

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Rishit Toteja, Arindam Sarkar, Prakash Mandayam Comar

Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise Reward (2025.findings-emnlp)

Challenge:	Existing methods of synthetic query generation generate mostly simple queries which might not be sufficiently representative of complex, real world queries.
Approach:	They propose to use large language models to fine tune query generation to produce complex queries that practitioners may pose during inference.
Outcome:	The proposed framework achieves 15-20% higher recall in database/table retrieval task compared to the existing state-of-the-art models for schema identification and upto 2% higher execution accuracy for SQL generation.

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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

Retrieval Enhanced Feedback via In-context Neural Error-book (2025.emnlp-main)

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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Jongyeop Hyun, Bumsoo Kim

LLaVE: Large Language and Vision Embedding Models with Hardness-Weighted Contrastive Learning (2025.findings-emnlp)

Challenge:	Existing methods for learning from errors lack a structured framework for analyzing and mitigating errors, especially in Multimodal Large Language Models (MLLMs).
Approach:	They propose a teacher-student framework that systematically structures errors to deliver targeted feedback for multimodal reasoning.
Outcome:	The proposed framework improves inference efficiency, token usage, and scalability by building a query-based structure that prioritizes visual information, diagnoses failure points, and guides corrective actions.

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Zhibin Lan, Liqiang Niu, Fandong Meng, Jie Zhou, Jinsong Su

Table-as-Search: Agentic Information Seeking is Table Completion (2026.findings-acl)

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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Tian Lan, Felix Henry, Bin Zhu, Qianghuai Jia, Junyang Ren, Qihang PU, Haijun Li, Longyue Wang, Zhao Xu, Weihua Luo

Empowering Large Language Models for Textual Data Augmentation (2024.findings-acl)

Challenge:	Current Information Seeking (InfoSeeking) agents struggle to maintain focus and coherence during long-horizon exploration, as tracking search states within one plain-text context is inherently fragile.
Approach:	They propose a structured planning framework that reformulates the InfoSeeking task as a Table Completion task.
Outcome:	The proposed framework outperforms state-of-the-art frameworks across three kinds of benchmarks, including multi-agent framework and commercial systems.

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Yichuan Li, Kaize Ding, Jianling Wang, Kyumin Lee

Structured and Abstractive Reasoning on Multi-modal Relational Knowledge Images (2026.findings-acl)

Challenge:	True. True. False
Approach:	False slants are proposed to generate a large pool of augmentation instructions and select the most suitable task-informed instructions.
Outcome:	False omissions: the proposed approach consistently generates augmented data with better quality compared to non-LLM and LLM-based data augmentation methods.

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Yichi Zhang, Zhuo Chen, Lingbing Guo, Wen Zhang, Huajun Chen

Learning Continuous Temporal Dynamics on Symplectic Manifolds for Temporal Knowledge Graph Embedding (2026.findings-acl)

Challenge:	Existing studies on understanding and reasoning with abstractive information from the visual modality have not explored the use of STructured and Abstractive Reasoning (STAR) on such data.
Approach:	They propose an automatic STAR data engine to synthesize images with MMRK to build multi-modal instructions with reliable chain-of-thought thinking for various STAR tasks.
Outcome:	The proposed framework outperforms GPT-4o in STAR and improves performance across 8 open-source MLLMs.

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Jiang Li, Zehua Duo, Tian Lan, Feilong Bao, Guanglai Gao, Xiangdong Su

Scalable Efficient Training of Large Language Models with Low-dimensional Projected Attention (2024.emnlp-main)

Challenge:	Existing methods for temporal knowledge graph embedding lack explicit structural constraints for continuous-time dynamics.
Approach:	They propose a Temporal Knowledge Graph Embedding framework that embeds temporal dynamics into a symplectic phase space.
Outcome:	The proposed framework achieves competitive performance with lower embedding dimensions.

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Xingtai Lv, Ning Ding, Kaiyan Zhang, Ermo Hua, Ganqu Cui, Bowen Zhou

ReSo: A Reward-driven Self-organizing LLM-based Multi-Agent System for Reasoning Tasks (2025.emnlp-main)

Challenge:	Existing studies have found that low-rank pre-training often compromises effectiveness.
Approach:	They propose to apply low-dimensional module only to the attention layer to improve both effectiveness and efficiency.
Outcome:	The proposed model saves 12.4% time while improving test perplexity and on downstream tasks compared with vanilla Transformer.

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Heng Zhou, Hejia Geng, Xiangyuan Xue, Li Kang, Yiran Qin, Zhiyong Wang, Zhenfei Yin, Lei Bai

Bridging Local Details and Global Context in Text-Attributed Graphs (2024.emnlp-main)

Challenge:	Multi-agent systems (MAS) are limited by poor flexibility and scalability, with underdeveloped optimization strategies.
Approach:	They propose a task graph generation and a reward-driven two-stage agent selection process to integrate multi-agent systems to improve their reasoning capabilities.
Outcome:	The proposed model outperforms existing methods on Math-MAS and SciBench-MAS SciBech, while other methods completely fail.

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Yaoke Wang, Yun Zhu, Wenqiao Zhang, Yueting Zhuang, Liyunfei Liyunfei, Siliang Tang

Evaluating the Effectiveness and Scalability of LLM-Based Data Augmentation for Retrieval (2025.emnlp-main)

Challenge:	Existing studies focus on combining different information levels but overlook interconnections, i.e., contextual textual information among nodes.
Approach:	They propose a framework that bridges local and global perspectives by leveraging contextual textual information.
Outcome:	The proposed framework achieves state-of-the-art performance while reducing tokens significantly.

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Pranjal A Chitale, Bishal Santra, Yashoteja Prabhu, Amit Sharma

PATIMT-Bench: A Multi-Scenario Benchmark for Position-Aware Text Image Machine Translation in Large Vision-Language Models (2025.findings-emnlp)

Challenge:	Existing research does not explore key factors such as optimal augmentation scale and the necessity of using large augmentation models.
Approach:	They propose to use LLMs to augment compact dual-encoder models to improve retrieval performance.
Outcome:	The proposed approach improves retrieval performance but its benefits diminish beyond a certain scale even with diverse augmentation strategies.

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Wanru Zhuang, Wenbo Li, Zhibin Lan, Xu Han, Peng Li, Jinsong Su

Distract Large Language Models for Automatic Jailbreak Attack (2024.emnlp-main)

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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Zeguan Xiao, Yan Yang, Guanhua Chen, Yun Chen

Self-adaptive Dataset Construction for Real-World Multimodal Safety Scenarios (2025.findings-emnlp)

Challenge:	Commercial large language models (LLMs) have made great progress in various NLP tasks.
Approach:	They propose a black-box jailbreak framework for automated red teaming of Large language models using an iterative optimization algorithm to conceal malicious content and memory reframing.
Outcome:	The proposed framework outperforms existing jailbreak defense methods and highlights the need to develop more effective and practical defense strategies.

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Jingen Qu, Lijun Li, Bo Zhang, Yichen Yan, Jing Shao

PromptCoT: Synthesizing Olympiad-level Problems for Mathematical Reasoning in Large Language Models (2025.findings-acl)

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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Xueliang Zhao, Wei Wu, Jian Guan, Lingpeng Kong

SCALE: Synergized Collaboration of Asymmetric Language Translation Engines (2024.findings-acl)

Challenge:	PromptCoT is a novel approach for synthesizing Olympiad-level math problems . it integrates rationale generation and mathematical concepts to generate complex problems based on concepts and rationale behind problem construction.
Approach:	They propose a method for automatically generating high-quality Olympiad-level math problems . they use mathematical concepts and the rationale behind problem construction to synthesize complex problems based on mathematical concepts .
Outcome:	The proposed method outperforms existing problem generation methods on benchmarks including GSM8K, MATH-500, and AIME2024.

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Xin Cheng, Xun Wang, Tao Ge, Si-Qing Chen, Furu Wei, Dongyan Zhao, Rui Yan

Community-Cross-Instruct: Unsupervised Instruction Generation for Aligning Large Language Models to Online Communities (2024.emnlp-main)

Challenge:	In this paper, we introduce SCALE, a collaborative framework that connects a compact Specialized Translation Model (STM) and a general-purpose Large Language Model (LLM) as one unified translation engine.
Approach:	They propose a collaborative framework that connects a Specialized Translation Model (STM) and a general-purpose Large Language Model (LLM) as one unified translation engine.
Outcome:	The proposed framework outperforms both LLMs and supervised models in high-resource or challenging low-resourced settings.

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Zihao He, Minh Chu, Rebecca Dorn, Siyi Guo, Kristina Lerman

DeCoVec: Building Decoding Space based Task Vector for Large Language Models via In-Context Learning (2026.findings-acl)

Challenge:	Social scientists use surveys to learn opinions and beliefs of populations, but these methods are slow, costly, and prone to biases.
Approach:	They propose a framework for aligning large language models to online communities by finetuning instruction-output pairs by an advanced LLM to elicit their beliefs.
Outcome:	The proposed framework enables cost-effective and automated surveying of diverse online communities.

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Feiyang Li, Yile Wang

Domain-Specific Data Generation Framework for RAG Adaptation (2026.findings-acl)

Challenge:	Existing approaches to steering large language models require fine-tuning or manipulation of internal states, limiting their flexibility and scalability.
Approach:	They propose a framework that constructs task vectors directly in the decoding space by leveraging in-context learning.
Outcome:	The proposed framework outperforms standard few-shot baselines on TruthfulQA, Math-500, and AQUA-RAT with gains up to +5.50 accuracy.

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Chris Xing Tian, Weihao Xie, Zhen Chen, Hui Liu, Zhengyuan Yi, Haoliang Li, Shiqi Wang, Siwei Ma

Dynamic Evaluation with Cognitive Reasoning for Multi-turn Safety of Large Language Models (2025.acl-long)

Challenge:	Retrieval-Augmented Generation (RAG) combines the language understanding and reasoning capabilities of large language models (LLMs) with external retrieval to produce domain-grounded responses.
Approach:	They propose a scalable and modular data-centric framework for generating domain-grounded question–answer–context triples tailored to diverse RAG adaptation strategies.
Outcome:	The proposed framework generates domain-grounded question–answer–context triples for multiple RAG adaptation strategies.

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Lanxue Zhang, Yanan Cao, Yuqiang Xie, Fang Fang, Yangxi Li

CF-TCIR: A Compositor-Free Framework for Hierarchical Text-Conditioned Image Retrieval (2024.findings-acl)

Challenge:	Existing safety evaluation methods rely on static assessments that use fixed harmful prompts or predefined prefixes as jailbreak templates.
Approach:	They propose a dynamic evaluation framework for multi-turn safety assessment of LLMs based on cognitive theories to simulate real chatting process and scenario simulation and strategy decision to guide dynamic generation.
Outcome:	The proposed framework has been applied to evaluate the safety of widely used LLMs.

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Yuchen Yang, Yu Wang, Yanfeng Wang

MA-GTS: A Multi-Agent Framework for Solving Complex Graph Problems in Real-World Applications (2025.emnlp-main)

Challenge:	Existing approaches to text-conditioned image retrieval use attention-driven compositors . instead, we reformulate the retrieval process as a cross-modal interaction between a synthesized image feature and its corresponding text descriptor.
Approach:	They propose a compositor-free framework for text-conditioned image retrieval . they propose combining a reference image and modification text to form a query tuple .
Outcome:	The proposed framework offers advantages in terms of computational efficiency, scalability, and performance.

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Zike Yuan, Ming Liu, Hui Wang, Bing Qin

Linguistic Theories

Challenge:	Existing methods for solving complex problems are expensive and inefficient when handling large-scale, high-complexity problems.
Approach:	They propose a multi-agent framework that decomposes complex problems through agent collaboration by mapping implicitly expressed graph data into clear, structured graph representations and dynamically selecting the most suitable algorithm based on problem constraints and graph structure scale.
Outcome:	The proposed framework outperforms state-of-the-art methods on multiple benchmarks with robust performance on both closed- and open-source models.

CIKT: A Collaborative and Iterative Knowledge Tracing Framework with Large Language Models (2025.emnlp-main)

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Runze Li, Siyu Wu, Jun Wang, Wei Zhang

Explainability & Interpretability in NLP

Challenge:	Knowledge Tracing (KT) aims to model a student’s learning state over time and predict their future performance.
Approach:	They propose a framework that harnesses Large Language Models to enhance both prediction accuracy and explainability by a synergistic optimization loop.
Outcome:	The proposed framework improves both prediction accuracy and explainability by using a synergistic optimization loop.

Skeleton-Guided-Translation: A Benchmarking Framework for Code Repository Translation with Fine-Grained Quality Evaluation (2025.findings-emnlp)

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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

CROWD: Certified Robustness via Weight Distribution for Smoothed Classifiers against Backdoor Attack (2024.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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Siqi Sun, Procheta Sen, Wenjie Ruan

DReSD: Dense Retrieval for Speculative Decoding (2025.findings-acl)

Challenge:	Existing studies on the certification of robustness of NLP models against backdoor attacks have focused on empirical defences against adversarial attacks without formal guarantees.
Approach:	They propose a model-agnostic mechanism for large-scale models that applies to complex model structures without the need for assessing model architecture or internal knowledge.
Outcome:	The proposed model-agnostic mechanism is tested on a diverse range of datasets and tasks, showing it can be used to mitigat backdoor triggers.

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Milan Gritta, Huiyin Xue, Gerasimos Lampouras

MANTA: A Scalable Pipeline for Transmuting Massive Web Corpora into Instruction Datasets (2025.findings-emnlp)

Challenge:	Speculative decoding (SD) uses an efficient draft model to propose the next few tokens, which are verified by the LLM in a single forward call, reducing latency while preserving its outputs.
Approach:	They propose a draft model that proposes the next few tokens from a non-parametric datastore and uses a framework that uses approximate nearest neighbour search with contextualised token embeddings to retrieve the most semantically relevant sequences for SD.
Outcome:	The proposed framework achieves (on average) 87% higher acceptance rates, 65% longer accepted tokens and 19% faster generation speeds compared to sparse retrieval (REST).

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Heuiyeen Yeen, Seokhee Hong, Hyeongu Yun, Jinsik Lee

LLM-Symbolic Integration for Robust Temporal Tabular Reasoning (2025.findings-acl)

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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Atharv Kulkarni, Kushagra Dixit, Vivek Srikumar, Dan Roth, Vivek Gupta

PromptWizard: Optimizing Prompts via Task-Aware, Feedback-Driven Self-Evolution (2025.findings-acl)

Challenge:	Existing methods for temporal tabular question answering are inconsistent and fail to provide the variability needed to thoroughly evaluate models.
Approach:	TEMPTABQA-C uses a synthetic dataset and symbolic representation to generate and execute SQL queries.
Outcome:	TEMPTABQA-C improves on previous methods for temporal tabular question answering . incorporating adaptive fewshot prompting with tailored examples improves performance . lack of robustness, scalability, and interpretable solutions is key obstacle .

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Eshaan Agarwal, Raghav Magazine, Joykirat Singh, Vivek Dani, Tanuja Ganu, Akshay Nambi

Uncovering the Impact of Chain-of-Thought Reasoning for Direct Preference Optimization: Lessons from Text-to-SQL (2025.acl-long)

Challenge:	Large language models (LLMs) have transformed AI across diverse domains, with prompting being central to their success in guiding model outputs.
Approach:	They propose a framework for discrete prompt optimization that generates human-readable prompts using feedback-driven critique and synthesis process.
Outcome:	The proposed framework improves prompt quality across 45 tasks and reduces API calls, token usage and overall cost.

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Hanbing Liu, Haoyang Li, Xiaokang Zhang, Ruotong Chen, Haiyong Xu, Tian Tian, Qi Qi, Jing Zhang

Agent-based Substructure Counting under Local Differential Privacy (2026.acl-long)

Challenge:	Direct Preference Optimization (DPO) is effective in complex reasoning tasks like math word problems and code generation, but Text-to-SQL datasets often include only final answers (gold SQL queries) without detailed CoT solutions.
Approach:	They found that Direct Preference Optimization (DPO) is crucial for unlocking DPO's potential by augmenting Text-to-SQL datasets with synthetic CoT solutions.
Outcome:	The proposed method achieves consistent and significant performance improvements on Text-to-SQL datasets.

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Yuting Zhang, Kai Wang, Wei Ni, Ying Zhang, Wenjie Zhang

Divide, Optimize, Merge: Scalable Fine-Grained Generative Optimization for LLM Agents (2025.findings-emnlp)

Challenge:	Recent studies have demonstrated the ability of Large Language Models (LLMs) to process graph problems.
Approach:	They propose to decompose substructure counting into node-level tasks distributed among node agents and embed the knowledge of distributed algorithms and DP frameworks in the curator agent and privacy controller.
Outcome:	Extensive experiments on 6 real-world datasets validate the effectiveness of the proposed framework for substructure counting tasks under edge local differential privacy (LDP).

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Jiale Liu, Yifan Zeng, Shaokun Zhang, Chi Zhang, Malte Højmark-Bertelsen, Marie Normann Gadeberg, Huazheng Wang, Qingyun Wu

Beyond Frameworks: Unpacking Collaboration Strategies in Multi-Agent Systems (2025.acl-long)

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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Haochun Wang, Sendong Zhao, Jingbo Wang, Zewen Qiang, Bing Qin, Ting Liu

Challenge:	Existing frameworks prioritize structural architectures and role assignments but neglect granular mechanics of agent collaboration.
Approach:	They propose to use centralized governance, instructor-led participation, ordered interaction patterns to optimize task accuracy and computational efficiency.
Outcome:	The proposed model improves task accuracy and computational efficiency under two context-dependent scenarios.

Continuous-Time Attention: PDE-Guided Mechanisms for Long-Sequence Transformers (2025.emnlp-main)

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Yukun Zhang, Xueqing Zhou

War of Thoughts: Competition Stimulates Stronger Reasoning in Large Language Models (2025.findings-acl)

Challenge:	Existing approaches to optimize attention for long sequences have been limited by their computational cost.
Approach:	They propose a framework that infuses partial differential equations into the Transformer’s attention mechanism to better handle long sequences.
Outcome:	The proposed framework achieves consistent performance gains over standard and long-sequence Transformer variants across a range of tasks.

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Yibin Chen, Jinyi Liu, Yan Zheng, Yifu Yuan, Jianye Hao

Evaluating Large Language Models for Belief Inference: Mapping Belief Networks at Scale (2025.findings-emnlp)

Challenge:	Recent advances in Large Language Models (LLMs) have reshaped the landscape of reasoning tasks.
Approach:	They propose a method that enhances LLM reasoning without finetuning by using test-time scaling.
Outcome:	The proposed method outperforms baseline models in both budget and model size.

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Trisevgeni Papakonstantinou, Antonina Zhiteneva, Ana Yutong Ma, Derek Powell, Zachary Horne

Waterfall: Scalable Framework for Robust Text Watermarking and Provenance for LLMs (2024.emnlp-main)

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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Gregory Kang Ruey Lau, Xinyuan Niu, Hieu Dao, Jiangwei Chen, Chuan-Sheng Foo, Bryan Kian Hsiang Low

Temp-R1: A Unified Autonomous Agent for Complex Temporal KGQA via Reverse Curriculum Reinforcement Learning (2026.acl-long)

Challenge:	Existing text watermarking methods are not robust enough against paraphrasing attacks . existing methods lack robustness to paraphrases and are not scalable to millions of users .
Approach:	They propose a training-free framework for robust and scalable text watermarking . they propose to use large language models as paraphrasers and a combination of techniques .
Outcome:	The proposed framework improves scalability, verifiability and computational efficiency compared to existing methods.

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Zhaoyan Gong, Zhiqiang Liu, Songze Li, Xiaoke Guo, Yuanxiang Liu, Xinle Deng, Zhizhen Liu, Lei Liang, Huajun Chen, Wen Zhang

How do Language Models Reshape Entity Alignment? A Survey of LM-Driven EA Methods: Advances, Benchmarks, and Future (2025.emnlp-main)

Challenge:	Existing methods rely on fixed workflows and expensive closed-source APIs, limiting flexibility and scalability.
Approach:	They propose a temporal reasoning agent that trains on difficult questions first . they expand the action space with specialized internal actions alongside external action .
Outcome:	The proposed agent improves 19.8% over baselines on complex questions and multi-tasks.

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Zerui Chen, Huiming Fan, Qianyu Wang, Tao He, Ming Liu, Heng Chang, Weijiang Yu, Ze Li, Bing Qin

Thinking Before Running! Efficient Code Generation with Thorough Exploration and Optimal Refinement (2025.findings-acl)

Challenge:	Entity alignment (EA) is critical for knowledge graph (KG) integration.
Approach:	They propose a taxonomy that categorizes methods in three stages: data preparation, feature embedding, and alignment.
Outcome:	The proposed taxonomy categorizes methods in three key stages: data preparation, feature embedding, and alignment.

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Xiaoqing Zhang, Yuhan Liu, Flood Sung, Xiuying Chen, Shuo Shang, Rui Yan

Language Constrained Multimodal Hyper Adapter For Many-to-Many Multimodal Summarization (2025.acl-long)

Challenge:	Recent research indicates that large language models (LLMs) have demonstrated remark-able capabilities in various programming-related domains, such as code generation and code refinement.
Approach:	They propose a framework that combines exploration with refinement to reduce test-time computation overhead.
Outcome:	The proposed framework outperforms SOTA and AgentCoder on humanEval and MBPP benchmarks while reducing test-time computation overhead and scalability.

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Nayu Liu, Fanglong Yao, Haoran Luo, Yong Yang, Chen Tang, Bo Lv

Inference Helps PLMs’ Conceptual Understanding: Improving the Abstract Inference Ability with Hierarchical Conceptual Entailment Graphs (2024.emnlp-main)

Challenge:	Existing models that share parameters neglect the language-specific knowledge learning.
Approach:	They propose a language-constrained multimodal hyper adapter for multimodal summarization that integrates language-specific adapters into multilingual pre-trained backbones.
Outcome:	The proposed model can generate summaries based on multimodal documents such as text and visuals, allowing people to quickly locate key information from the vast multimedia con.

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Juncai Li, Ru Li, Xiaoli Li, Qinghua Chai, Jeff Pan

Robust and Scalable Model Editing for Large Language Models (2024.lrec-main)

Challenge:	Existing approaches to abstract inference ignore the polysemy and hierarchical nature of concepts . prevailing approaches disregard how arguments might entail differently across various concept levels, thereby missing potential enlargement connections.
Approach:	They propose a framework that organizes arguments hierarchically and delves into entailment relations at diverse concept levels.
Outcome:	The proposed framework improves the model's generalization and reasoning prowess in natural language inference.

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Yingfa Chen, Zhengyan Zhang, Xu Han, Chaojun Xiao, Zhiyuan Liu, Chen Chen, Kuai Li, Tao Yang, Maosong Sun

Beyond Single View: A Comprehensive Benchmark for Medical Multimodal Large Language Models on Multi-Image Understanding (2026.acl-long)

Challenge:	Existing methods that ignore contextual knowledge fail to reliably fall back to parametric knowledge when presented with irrelevant context.
Approach:	They propose to use contextual knowledge to update and correct LLMs' knowledge by in-context editing instead of retraining.
Outcome:	The proposed method outperforms current state-of-the-art methods by a large margin on a dataset that contains irrelevant questions.

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Dexuan Xu, Yuan Jiayin, Jianing Wang, Yanyuan Chen, Hanpin Wang, Yu Huang

Pathway to Relevance: How Cross-Encoders Implement a Semantic Variant of BM25 (2025.emnlp-main)

Challenge:	Existing benchmarks for multimodal large language models are limited to multiview diagnostics.
Approach:	They propose a benchmark specifically designed for medical multi-image understanding that evaluates MLLMs across four dimensions.
Outcome:	The proposed model performs better in multi-image contexts than open-source models . the model perform better when processing increased visual loads than closed-source ones .

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Meng Lu, Catherine Chen, Carsten Eickhoff

Co-Eval: Augmenting LLM-based Evaluation with Machine Metrics (2025.emnlp-main)

Challenge:	Interpretability in information retrieval (IR) models is coarse-grained and poorly understood . a cross-encoder model extracts traditional relevance signals, such as term frequency and inverse document frequency .
Approach:	They analyze how a common IR model extracts traditional relevance signals . this is similar to the probabilistic ranking function BM25 .
Outcome:	The proposed model extracts traditional relevance signals in early-to-middle layers, similar to BM25 . the model then combine these concepts in later layers, laying the groundwork for future interventions .

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Ling-I Wu, Weijie Wu, Minyu Chen, Jianxin Xue, Guoqiang Li

Challenge:	Existing LLMs suffer from biases and misalignment due to limited functional understanding and knowledge gaps.
Approach:	They introduce a framework that leverages a criteria planner model and optimized machine metrics to enhance the scalability and fairness of LLM-based evaluation.
Outcome:	The proposed framework reduces biases and improves alignment with human preferences, with gains of up to 0.324 in Spearman correlation.

CogAtom: From Cognitive Atoms to Olympiad-level Mathematical Reasoning in Large Language Models (2025.findings-emnlp)

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Zhuofan Chen, Jiyuan He, Yichi Zhang, Xing Hu, Haoxing Wen, Jun Bai, Wenge Rong

Arctic-Text2SQL-R1: Simple Rewards, Strong Reasoning in Text-to-SQL (2026.findings-acl)

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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Zhewei Yao, Guoheng Sun, Łukasz Borchmann, Zheyu Shen, Minghang Deng, Bohan Zhai, Hao Zhang, Ang Li, Yuxiong He

Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey (2025.findings-emnlp)

Challenge:	Translating natural language questions into SQL is a core challenge in natural language understanding and human-computer interaction.
Approach:	They propose a reinforcement learning framework and model family to generate accurate, executable SQL using a lightweight reward signal based solely on execution correctness.
Outcome:	The proposed framework outperforms previous versions of 70B-class systems and achieves state-of-the-art execution accuracy across six diverse Text2SQL benchmarks.

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Zirui Song, Bin Yan, Yuhan Liu, Miao Fang, Mingzhe Li, Rui Yan, Xiuying Chen

Learn to Memorize: Scalable Continual Learning in Semiparametric Models with Mixture-of-Neighbors Induction Memory (2025.acl-long)

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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Guangyue Peng, Tao Ge, Wen Luo, Wei Li, Houfeng Wang

Beyond the Panorama: Training-Free Hierarchical Perception-Reasoning for Fine-Grained Vision in MLLMs (2026.acl-long)

Challenge:	Semiparametric language models (LMs) use static storage, which lacks learning capability and is disconnected from the internal information flow of the parametric models.
Approach:	They reconceptualize the non-parametric memory represented by kNN-LM as a learnable Mixture-of-Neighbors Induction Memory (MoNIM) this synergizes the induction capabilities of attention heads with the memorization strength of feed-forward networks .
Outcome:	The proposed model is a learnable Mixture-of-neighbors induction memory (MoNIM) it synergizes the induction capabilities of attention heads with the memorization strength of feed-forward networks (FFNs).

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Xiaoyang Yi, Jing Chen, Li Peng, Yuru Bao, Jian Zhang

Almieyar-Oryx-BloomBench: A Bilingual Multimodal Benchmark for Cognitively Informed Evaluation of Vision-Language Models (2026.findings-acl)

Challenge:	Existing multimodal large language models (MLLMs) face challenges in fine-grained visual tasks.
Approach:	They propose a training-free hierarchical perception-reasoning framework that enhances fine-grained visual understanding by simulating human perception mechanisms.
Outcome:	The proposed framework enhances fine-grained visual understanding by simulating human perception mechanisms.

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Mohammad Mahdi Abootorabi, Omid Ghahroodi, Anas Madkoor, Marzia Nouri, Doratossadat Dastgheib, Ehsaneddin Asgari

Adapting Bias Evaluation to Domain Contexts using Generative Models (2025.emnlp-main)

Challenge:	Existing evaluations focus on piecemeal or disconnected tasks, obscuring critical cognitive weaknesses and providing little insight for targeted improvement.
Approach:	They propose a bilingual, cognitively human-grounded multimodal benchmark for VLMs that evaluates six levels of cognition through carefully designed image–question–answer tasks.
Outcome:	The proposed framework ensures scalability, cultural inclusivity, and linguistic fidelity.

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Tamara Quiroga, Felipe Bravo-Marquez, Valentin Barriere

De-Anonymization at Scale via Tournament-Style Attribution (2026.acl-long)

Challenge:	Existing approaches to assess social bias in NLP systems face limitations in scalability and fidelity across domains.
Approach:	They propose a domain-adaptive framework that uses prompting with Large Language Models to automatically transform template-based bias datasets into domain-specific variants.
Outcome:	The proposed framework improves the accuracy and contextual relevance of bias evaluations in socially relevant datasets.

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Lirui Zhang, Huishuai Zhang

LCES: Zero-shot Automated Essay Scoring via Pairwise Comparisons Using Large Language Models (2025.emnlp-main)

Challenge:	Large language models (LLMs) are rapidly gaining widespread adoption in real-world use . authors propose a method for attributing authorship among tens of thousands of candidate texts .
Approach:	They propose a large-language-model-based method for attributing authorship among tens of thousands of candidate texts.
Outcome:	The proposed method improves accuracy and ranking precision over previous approaches.

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Takumi Shibata, Yuichi Miyamura

pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training (2026.findings-acl)

Challenge:	Existing approaches to automate essay scoring rely on LLMs to generate absolute scores . however, these methods diverge from human evaluations due to model biases and inconsistent scoring .
Approach:	They propose a method that formulates AES as a pairwise comparison task using large language models.
Outcome:	The proposed method outperforms conventional zero-shot methods in accuracy while maintaining computational efficiency.

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Wenzheng Zhang, Bingzheng Liu

Revisiting Audio-language Pretraining for Learning General-purpose Audio Representation (2026.acl-long)

Challenge:	Existing methods for building efficient large language models with sub 2-bit weights are lacking in accuracy and scalability.
Approach:	They propose a method that decouples parameters by splitting linear layers into two specialized branches.
Outcome:	The proposed method achieves state-of-the-art performance in extremely low-bit quantization.

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Wei-Cheng Tseng, Xuanru Zhou, Mingyue Huo, Yiwen Shao, Hao Zhang, Dong Yu

Decentralized Arena: Towards Democratic and Scalable Automatic Evaluation of Language Models (2026.acl-long)

Challenge:	Existing methods for learning general-purpose audio representations are limited in scope and coverage of audio attributes.
Approach:	They propose to use a 10.7M caption dataset to compare ALP with captioning . they find that contrastive learning yields competitive, transferable representations .
Outcome:	The proposed model yields competitive, transferable representations, while captioning exhibits better scalability.

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Yanbin Yin, Kun Zhou, Zhen Wang, Xiangdong Zhang, Yifei Shao, Shibo Hao, Yi Gu, Jieyuan Liu, Somanshu Singla, Tianyang Liu, Eric P. Xing, Zhengzhong Liu, Haojian Jin, Zhiting Hu

Training Verifier to Assessing Complex Real-World Tool-Use Trajectories (2026.findings-acl)

Challenge:	closed-ended question-based benchmarks struggle with saturation as newer models emerge . crowd-sourced leaderboards rely on costly and slow human judges .
Approach:	They propose a framework that leverages collective intelligence from all large language models to evaluate each other.
Outcome:	a new framework enables a democratic, pairwise evaluation of all large language models . it achieves 97% correlation with human judgements, while significantly reducing the cost.

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Linzhuang Sun, Mingyang Chen, Hao Liang, Tianpeng Li, Zhou Yijie, Chenzheng Zhu, Tianyu Guo, Huanyao Zhang, Jingxuan Wei, Bihui Yu, Fan Yang, Wentao Zhang

Challenge:	Existing methods for training effective AI agents often resort to synthetic data generation.
Approach:	They propose a plug-and-play framework for data quality control in tool-use scenarios . they construct a tool-verify dataset and release a benchmark to assess its performance .
Outcome:	The proposed framework surpasses Qwen2.5-72B-Instruct on Tool-V-Bench and the previous APIGen-MT dataset.

LLM-FK: Multi-Agent LLM Reasoning for Foreign Key Detection in Large-Scale Complex Databases (2026.findings-acl)

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Zijian Tang, Ying Zhang, Sibo Cai, Ruoxuan Wang

PuzzleClone: A DSL-Powered Framework for Synthesizing Verifiable Data (2026.findings-acl)

Challenge:	Existing methods for detecting missing foreign keys are limited in capturing semantic dependencies across schemas.
Approach:	They propose a framework that integrates four agents to detect missing foreign keys . they propose combinatorial search space explosion, ambiguous inference and global inconsistency .
Outcome:	The proposed framework achieves F1-scores above 93% on large-scale MusicBrainz database . it reduces candidate search space by two to three orders of magnitude without losing true FKs .

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Kai Xiong, Yanwei Huang, Rongjunchen Zhang, Kun Chen, Haipang WU, Yingcai Wu

MATA: Multi-Agent Framework for Reliable and Flexible Table Question Answering (2026.findings-acl)

Challenge:	Existing datasets with verifiable answers are limited in reliability, diversity, and scalability . a new approach to generate verifikatable data at scale is needed to improve models' performance .
Approach:	They propose a formal framework for synthesizing verifiable data at scale using a novel DSL-driven approach.
Outcome:	The proposed framework improves performance on a wide range of puzzles and logic benchmarks.

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Sieun Hyeon, Jusang Oh, Sunghwan Steve Cho, Jaeyoung Do

WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback (2026.acl-long)

Challenge:	Recent advances in Large Language Models have significantly improved table understanding tasks . practical deployment of TableQA systems presents several persistent challenges .
Approach:	They propose a multi-agent TableQA framework that leverages multiple reasoning paths and tools built with small language models.
Outcome:	The proposed framework achieves state-of-the-art accuracy and efficient reasoning while avoiding excessive LLM inference.

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Taiwei Shi, Zhuoer Wang, Longqi Yang, Ying-Chun Lin, Zexue He, Mengting Wan, Pei Zhou, Sujay Kumar Jauhar, Sihao Chen, Shan Xia, Hongfei Zhang, Jieyu Zhao, Xiaofeng Xu, Xia Song, Jennifer Neville

Challenge:	Traditional alignment methods rely on human annotations and are subjective and misalignment with real-world user preferences.
Approach:	They propose a framework that leverages in-situ user feedback during conversations with LLMs to create preference datasets automatically.
Outcome:	The proposed framework identifies and classifies user feedback to LLM responses between conversation turns and creates examples of preferred and dispreferred responses according to user preferences.

Beyond Static Benchmarks: Synthesizing Harmful Content via Persona-based Simulation for Robust Evaluation (2026.acl-long)

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Huije Lee, Jisu Shin, Hoyun Song, Changgeon Ko, Jong C. Park

LogosKG: Hardware-Optimized Scalable and Interpretable Knowledge Graph Retrieval (2026.acl-long)

Challenge:	Existing static benchmarks for harmful content detection face limitations in scalability and diversity.
Approach:	They propose a framework for synthesizing harmful content using persona-guided large language model agents.
Outcome:	The proposed framework achieves a high success rate in harmful generation tests across multiple detection systems.

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He Cheng, Yifu Wu, Saksham Khatwani, Maya Kruse, Dmitriy Dligach, Timothy A. Miller, Majid Afshar, Yanjun Gao

Beyond ’Aha!’: Toward Systematic Meta-Abilities Alignment in Large Reasoning Models (2026.findings-acl)

Challenge:	Existing systems struggle to balance efficiency, scalability, and interpretability.
Approach:	They propose a hardware-aligned framework that enables scalable and interpretable k-hop retrieval on large KGs.
Outcome:	The proposed framework scales to billion-edge graphs without loss of retrieval fidelity.

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Zhiyuan Hu, Yibo Wang, Hanze Dong, Yuhui Xu, Amrita Saha, Caiming Xiong, Bryan Hooi, Junnan Li

See or Say Graphs: Agent-Driven Scalable Graph Understanding with Vision-Language Models (2026.findings-acl)

Challenge:	Prior work has shown that outcome-based reinforcement learning (RL) can incidentally elicit advanced reasoning behaviors such as self-correction, backtracking, and verification.
Approach:	They explicitly align large reasoning models with three meta-abilities: deduction, induction, and abduction, using automatically generated, self-verifiable tasks.
Outcome:	The proposed model aligns models with deduction, induction, and abduction meta-abilities using automatically generated, self-verifiable tasks.

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Shuo Han, Yukun Cao, Zezhong Ding, Zengyi Gao, S Kevin Zhou, Xike Xie

SynthRL: Scaling Visual Reasoning with Verifiable Data Synthesis (2026.findings-acl)

Challenge:	Existing studies have explored textual graph descriptions and visual modalities for VLMs to understand graphs.
Approach:	They propose a unified framework that enhances both scalability and modality coordination in graph understanding by integrating textual and visual modalities.
Outcome:	GraphVista scales to large graphs, 200 larger than those used in existing benchmarks, and consistently outperforms existing textual, visual, and fusion-based methods.

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Zijian Wu, Jinjie Ni, Xiangyan Liu, Zichen Liu, Hang Yan, Michael Qizhe Shieh

Jailbreak-Zero: A Path to Pareto Optimal Red Teaming for Large Language Models (2026.acl-long)

Challenge:	SynthRL synthesizes over 3.3K additional verifiable, challenging questions from approximately 8K seed samples.
Approach:	They propose a scalable and guaranteed pipeline for automatic data scaling in reasoning-oriented RL training.
Outcome:	The proposed pipeline synthesizes over 3.3K additional verifiable, challenging questions from approximately 8K seed samples.

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Kai Hu, Abhinav Aggarwal, Mehran Khodabandeh, David Zhang, Eric Hsin, Li Chen, Ankit Jain, Matt Fredrikson, Akash Bharadwaj