Papers by Chen Wu
Attention-guided Self-reflection for Zero-shot Hallucination Detection in Large Language Models (2025.emnlp-main)
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| Challenge: | Hallucination is a significant barrier to the effective application of Large Language Models (LLMs). |
| Approach: | They propose an Attention-Guided SElf-Reflection approach for hallucination detection in Large Language Models. |
| Outcome: | The proposed method significantly outperforms existing methods in zero-shot hallucination detection on four widely-used LLMs across three different halluciation benchmarks. |
Enhanced Metaphor Detection via Incorporation of External Knowledge Based on Linguistic Theories (2021.findings-acl)
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| Challenge: | Existing methods for metaphor detection take little consideration on linguistic theories of metaphor detection. |
| Approach: | They propose two BERT-based models for metaphor detection based on examples and definitions of words from the Oxford Dictionary. |
| Outcome: | The proposed models achieve state-of-the-art performance on two established metaphor datasets and are highly interpretable. |
End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network (2020.coling-main)
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| Challenge: | Emotion-cause pair extraction (ECPE) aims to extract emotion expressions and their corresponding causes in a document simultaneously. |
| Approach: | They propose to model pair-level contexts so that to capture dependency information among local neighborhood candidate pairs. |
| Outcome: | The proposed model extracts emotion-cause pairs and their causes from documents . it is based on a benchmark Chinese emotion-case pair extraction corpus . |
TeleMelody: Lyric-to-Melody Generation with a Template-Based Two-Stage Method (2022.emnlp-main)
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Zeqian Ju, Peiling Lu, Xu Tan, Rui Wang, Chen Zhang, Songruoyao Wu, Kejun Zhang, Xiang-Yang Li, Tao Qin, Tie-Yan Liu
| Challenge: | a new lyric-to-melody generation system bridges the gap between lyrics and melodies . previous generation systems lack paired data and lack of control on generated melodie. |
| Approach: | They develop a lyric-to-melody generation system with music template to bridge the gap between lyrics and melodies. |
| Outcome: | The proposed system bridges the gap between lyrics and melodies by using music template. |
Enhancing Reinforcement Learning for Radiology Report Generation with Evidence-aware Rewards and Self-correcting Preference Learning (2026.acl-long)
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| Challenge: | Recent reinforcement learning approaches have advanced radiology report generation (RRG) however, there are two limitations: report-level rewards offer limited evidence-grounded guidance for clinical faithfulness . |
| Approach: | They propose a method that uses group-wise evidence-aware alignment rewards and self-correcting preference learning to build a reliable, disease-agnostic preference dataset without human supervision. |
| Outcome: | ESC-RL promotes clinically faithful, disease-aligned reward and supports continual self-improvement during training. |
PropGenie: A Multi-Agent Conversational Framework for Real Estate Assistance (2026.eacl-demo)
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| Challenge: | PropGenie is a multi-agent framework based on large language models (LLMs) it provides comprehensive real estate assistance in real-world scenarios . |
| Approach: | They propose a multi-agent framework based on large language models to deliver comprehensive real estate assistance in real-world scenarios. |
| Outcome: | The proposed framework outperforms a general-purpose LLM and a domain-specific chatbot in real-world scenarios. |
Codec-SUPERB: An In-Depth Analysis of Sound Codec Models (2024.findings-acl)
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Haibin Wu, Ho-Lam Chung, Yi-Cheng Lin, Yuan-Kuei Wu, Xuanjun Chen, Yu-Chi Pai, Hsiu-Hsuan Wang, Kai-Wei Chang, Alexander Liu, Hung-yi Lee
| Challenge: | Researchers have developed a sound codec that can be used as tokenizers for preserving audio data and minimizing data transmission latency. |
| Approach: | They propose to use codec-SUPERB to assess codec models across representative sound applications and signal-level metrics rooted in sound domain knowledge. |
| Outcome: | The proposed codec-SUPERB model is evaluated on selected experimental settings. |
Towards Multi-label Unknown Intent Detection (2022.coling-1)
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| Challenge: | Existing methods for multi-class unknown intent detection assume that each utterance has only one intent, which is not true because utterrances often contain multiple intents. |
| Approach: | They propose a task to detect whether an utterance contains the unknown intent by recognizing whether all intents contained in the utterant are known. |
| Outcome: | The proposed method significantly reduces the FPR95 on the MultiWOZ 2.3 dataset by 12.25% compared to the best baseline. |
Full-Step-DPO: Self-Supervised Preference Optimization with Step-wise Rewards for Mathematical Reasoning (2025.findings-acl)
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| Challenge: | Existing approaches to improve long-chain mathematical reasoning focus on the first erroneous step, but ignore all other steps and rely heavily on external signals. |
| Approach: | They propose a DPO framework that leverages step-wise rewards from the entire reasoning chain instead of optimizing only the first erroneous step. |
| Outcome: | The proposed framework improves on in-domain and out-of-domain mathematical reasoning benchmarks. |
A Cognitive Evaluation Benchmark of Image Reasoning and Description for Large Vision-Language Models (2025.naacl-long)
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| Challenge: | Large Vision-Language Models (LVLMs) are hardly comprehensively evaluated for their cognitive abilities. |
| Approach: | They propose to evaluate high-level cognitive abilities of Large Vision-Language Models (LVLMs) using images with rich semantics. |
| Outcome: | The proposed evaluation benchmark consists of 251 images along with comprehensive annotations. |
A Survey of LLM-based Agents in Medicine: How far are we from Baymax? (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) are transforming healthcare through their ability to understand and assist with medical tasks. |
| Approach: | They analyze system profiles, clinical planning, medical reasoning frameworks, and external capacity enhancement. |
| Outcome: | The findings highlight the future directions in medical reasoning, physical system integration, and training simulations. |
The JDDC Corpus: A Large-Scale Multi-Turn Chinese Dialogue Dataset for E-commerce Customer Service (2020.lrec-1)
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| Challenge: | Existing datasets for human-like dialogue tasks are deficient due to the complexity of human conversations. |
| Approach: | They construct a large-scale Chinese E-commerce conversation corpus with 1 million dialogues, 20 million utterances, and 150 million words. |
| Outcome: | The proposed dataset includes 1 million multi-turn dialogues, 20 million utterances, and 150 million words. |
Masked Thought: Simply Masking Partial Reasoning Steps Can Improve Mathematical Reasoning Learning of Language Models (2024.acl-long)
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Changyu Chen, Xiting Wang, Ting-En Lin, Ang Lv, Yuchuan Wu, Xin Gao, Ji-Rong Wen, Rui Yan, Yongbin Li
| Challenge: | Despite the advances in large language models, they still face difficulties with multi-step reasoning tasks. |
| Approach: | They propose a method that randomly masks certain tokens within the chain of thought to improve model accuracy by 5% over standard supervised fine-tuning. |
| Outcome: | The proposed method improves accuracy and accuracy by 5% over standard fine-tuning with a few codes modified. |
Autoregressive Speech Synthesis without Vector Quantization (2025.acl-long)
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Lingwei Meng, Long Zhou, Shujie Liu, Sanyuan Chen, Bing Han, Shujie Hu, Yanqing Liu, Jinyu Li, Sheng Zhao, Xixin Wu, Helen M. Meng, Furu Wei
| Challenge: | MELLE is a novel language modeling approach for text-to-speech synthesis that generates continuous tokens from text . authors demonstrate that it reduces the need for vector quantization and improves model robustness . |
| Approach: | They propose to autoregressively generate continuous mel-spectrogram frames directly from text condition, bypassing vector quantization. |
| Outcome: | The proposed model achieves superior performance across multiple metrics and is more streamlined. |
A Learnable Skill Combination Strategy for Multi-task Learning in Natural Language Understanding (2026.findings-acl)
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| Challenge: | a novel multi-task learning framework for domain-specific natural language understanding tasks addresses these limitations by combing multiple tasks into a single framework. |
| Approach: | They propose a multi-task learning framework that decomposes the language model into modular skill components and employs a dynamic, learnable skill-combination mechanism to adaptively handle diverse tasks. |
| Outcome: | The proposed framework surpasses conventional multi-task learning approaches in performance. |
Advancing Zero-shot Text-to-Speech Intelligibility across Diverse Domains via Preference Alignment (2025.acl-long)
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| Challenge: | Existing zero-shot text-to-speech systems struggle in challenging scenarios such as tongue twisters, repeated words, code-switching, and cross-lingual synthesis. |
| Approach: | They propose a dataset that leverages preference alignment techniques to improve performance . they also extend the Direct Preference Optimization framework to accommodate diverse TTS architectures . |
| Outcome: | The proposed dataset improves intelligibility, similarity, and audio quality for multiple models across domains. |
HATA: Trainable and Hardware-Efficient Hash-Aware Top-k Attention for Scalable Large Model Inference (2025.findings-acl)
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Ping Gong, Jiawei Yi, Shengnan Wang, Juncheng Zhang, Zewen Jin, Ouxiang Zhou, Ruibo Liu, Guanbin Xu, Youhui Bai, Bowen Ye, Kun Yuan, Tong Yang, Gong Zhang, Renhai Chen, Feng Wu, Cheng Li
| Challenge: | Existing top-k attention methods struggle to strike a balance between efficiency and accuracy. |
| Approach: | They propose a top-k attention approach that integrates low-overhead techniques into the Top-k Attention process to achieve 7.2 speedup compared to vanilla full attention. |
| Outcome: | The proposed approach achieves 7.2 speedup compared to current top-k attention methods while maintaining model accuracy. |
RSMeM: Knowledge-Enhanced Memory Evolution for Remote Sensing Agents with Systematic Evaluation (2026.acl-long)
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Bingxian Wu, Yu Zhang, Zonghao Guo, Tang Liu, Chen Qian, Yuxiang Lu, Xingbo Du, Yanghao Li, Yidan Zhang, Chi Chen, Ling Yao, Chenghu Zhou, Maosong Sun
| Challenge: | Existing RS agents built on general-purpose LLMs are domain-agnostic, resulting in brittle and error-prone workflows. |
| Approach: | They propose a knowledge-enhanced memory evolution mechanism that bootstraps RS agents with pre-distilled domain knowledge and iteratively integrates online experience for robust multi-step tool execution. |
| Outcome: | Experiments show that the new model improves tool-use performance and accuracy . iteratively, iteration of the model integrates online experience for robust multi-step tool execution . |
The Elephant in the Room: Exploring the Role of Neutral Words in Language Model Group-Agnostic Debiasing (2025.findings-acl)
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Xinwei Guo, Jiashi Gao, Junlei Zhou, Jiaxin Zhang, Guanhua Chen, Xiangyu Zhao, Quanying Liu, Haiyan Wu, Xin Yao, Xuetao Wei
| Challenge: | Large Language Models (LLMs) are increasingly integrated into our daily lives, raising ethical concerns, especially about perpetuating stereotypes. |
| Approach: | They propose a method that incorporates a neutral word semantics-based loss function to alleviate the deterioration of the LMS during debiasing. |
| Outcome: | The proposed method alleviates the deterioration of the Language Modeling Score (LMS) by incorporating a neutral word semantics-based loss function. |
LLMs as Lab Engineers: A Benchmark for Analytical Method Lifecycle Management (2026.findings-acl)
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Xiaoyi Chen, Mahsa Monshizadeh, Chaoqi Zhang, Jianjun Lang, Yang Wu, Genevieve Mortensen, Xiaozhong Liu, Haixu Tang
| Challenge: | General-purpose commercial models outperform domain-specialized ones, while RAG and reasoning significantly improve performance. |
| Approach: | They propose a benchmark to evaluate LLMs' capabilities in analytical chemistry scenarios. |
| Outcome: | The proposed framework outperforms existing benchmarks focused on factual knowledge and provides practical guidance for analytical chemistry challenges. |
SmartBench: Is Your LLM Truly a Good Chinese Smartphone Assistant? (2025.emnlp-main)
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| Challenge: | Existing evaluation benchmarks for Large Language Models focus on objective tasks like mathematics and coding in English, which do not reflect the practical use cases of on-device LLMs in real-world mobile scenarios. |
| Approach: | They propose a benchmark to evaluate the capabilities of on-device Large Language Models in Chinese mobile contexts. |
| Outcome: | The proposed framework evaluates on-device LLMs and MLLMs in Chinese . it provides a standardized framework for evaluating LLM performance on real smartphones . |
Counterfactual Supporting Facts Extraction for Explainable Medical Record Based Diagnosis with Graph Network (2021.naacl-main)
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| Challenge: | Existing methods provide explanations based on a precise medical knowledge base, which is disease-specific and difficult to obtain for experts in reality. |
| Approach: | They propose a method to extract supporting facts from irregular EMR without external knowledge bases by constructing a hierarchical graph network and using it to obtain causal relationship between multi-granularity features and diagnosis results. |
| Outcome: | The proposed method diagnoses four types of EMR correctly and provides accurate supporting facts for the results. |
Learning In-context Learning for Named Entity Recognition (2023.acl-long)
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Jiawei Chen, Yaojie Lu, Hongyu Lin, Jie Lou, Wei Jia, Dai Dai, Hua Wu, Boxi Cao, Xianpei Han, Le Sun
| Challenge: | Existing methods to recognize entities in text are limited by the diversity of entity types and the lack of high-quality annotations. |
| Approach: | They propose an in-context learning-based NER approach that can inject in-const NER ability into PLMs and recognize entities of novel types on-the-fly using only a few demonstrative instances. |
| Outcome: | The proposed method outperforms the PLMs+fine-tuning counterparts on 4 few-shot NER datasets and significantly outperformed the Plms+initialized extractors. |
RAVEN: Robust Advertisement Video Violation Temporal Grounding via Reinforcement Reasoning (2025.acl-industry)
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| Challenge: | Existing methods for detecting ads video violations lack precise temporal grounding, noisy annotations, and limited generalization. |
| Approach: | They propose a framework that integrates curriculum reinforcement learning with large language models to enhance reasoning and cognitive capabilities for violation detection. |
| Outcome: | The proposed framework achieves superior performance in violation category accuracy and temporal interval localization. |
AD-KD: Attribution-Driven Knowledge Distillation for Language Model Compression (2023.acl-long)
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| Challenge: | Existing knowledge distillation methods focus on the transfer of model-specific knowledge but overlook data-specific information. |
| Approach: | They propose an attribution-driven knowledge distillation approach which explores the token-level rationale behind the teacher model and transfers attribution knowledge to the student model. |
| Outcome: | The proposed method outperforms state-of-the-art methods on the GLUE benchmark and shows that it is more efficient than existing methods. |
LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression (2020.coling-main)
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Yihuan Mao, Yujing Wang, Chufan Wu, Chen Zhang, Yang Wang, Quanlu Zhang, Yaming Yang, Yunhai Tong, Jing Bai
| Challenge: | Existing models that use knowledge distillation are memory-intensive and latency-prohibitive . Existing solutions that use this knowledge distilling framework are expensive . |
| Approach: | They propose a solution that uses weight pruning, matrix factorization and knowledge distillation to learn a smaller model. |
| Outcome: | The proposed model reduces the training overheads by an order of magnitude on public datasets while preserving state-of-the-art accuracy. |
Boosting Large Language Models with Continual Learning for Aspect-based Sentiment Analysis (2024.findings-emnlp)
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| Challenge: | Existing studies focus on improving the performance of domain-specific models based on the target dataset. |
| Approach: | They propose a Large Language Model-based Continual Learning (LLM-CL) model for ABSA that learns the target domain’s ability while maintaining the history domains’ abilities. |
| Outcome: | The proposed model obtains new state-of-the-art over 19 datasets. |
I-MCTS: Enhancing Agentic AutoML via Introspective Monte Carlo Tree Search (2026.findings-eacl)
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| Challenge: | Existing LLM-based agents struggle with low diversity and suboptimal code generation. |
| Approach: | They propose an approach that iteratively expands tree nodes through an introspective process that meticulously analyzes solutions and results from parent and sibling nodes. |
| Outcome: | The proposed approach shows a 4% improvement in performance compared to the strong open-source AutoML agents. |
Your Co-Workers Matter: Evaluating Collaborative Capabilities of Language Models in Blocks World (2024.findings-acl)
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| Challenge: | Existing studies on how large language model agents collaborate with humans in equal roles emphasize the importance of coordination and communication. |
| Approach: | They propose to use chain-of-thought prompts to evaluate different collaboration perspectives, from independent to more complex, dependent tasks. |
| Outcome: | The proposed model significantly improves the evaluation metric. |
ChessArena: A Chess Testbed for Evaluating Strategic Reasoning Capabilities of Large Language Models (2026.acl-long)
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| Challenge: | Existing evaluation frameworks focus on isolated question-answering tasks that may not capture the essential aspects of strategic reasoning. |
| Approach: | They evaluate 13 large language models across over 800 games in chess . they use a chessian-based framework to test strategic reasoning and pattern recognition . |
| Outcome: | The proposed framework improves performance and basic understanding of large language models. |
Tracking Life’s Ups and Downs: Mining Life Events from Social Media Posts for Mental Health Analysis (2025.acl-long)
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| Challenge: | Existing studies have indicated that major life events can greatly impact individuals’ mental health, but shedding its light on social media data is challenging due to the complexity and ambiguity nature of life events. |
| Approach: | They propose to extract life events mentioned in posts on social media to uncover a social media event dataset which includes 12 major life event categories that are likely to occur in everyday life. |
| Outcome: | The proposed dataset includes 12 life event categories that are likely to occur in everyday life and is human-annotated under iterative procedure and boasts a high level of quality. |
From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework (2023.findings-acl)
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Yangyi Chen, Hongcheng Gao, Ganqu Cui, Lifan Yuan, Dehan Kong, Hanlu Wu, Ning Shi, Bo Yuan, Longtao Huang, Hui Xue, Zhiyuan Liu, Maosong Sun, Heng Ji
| Challenge: | Existing models of robustness evaluation are incomprehensive, impractical, and invalid . |
| Approach: | They propose a framework for automatic robustness evaluation that shifts towards model-centric evaluation to further exploit the advantages of adversarial attacks. |
| Outcome: | The proposed framework is based on a model-centric evaluation protocol and a robustness evaluation protocol. |
Llama SLayer 8B: Shallow Layers Hold the Key to Knowledge Injection (2024.findings-emnlp)
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| Challenge: | Existing methods to augment pre-trained large language models require extensive computational efforts and massive data volumes, challenging the widespread accessibility of LLM research. |
| Approach: | They propose a post-pretraining strategy of selectively enhancing shallow layers while pruning less effective deep ones to augment pretrained large language models. |
| Outcome: | The proposed approach improves performance on the corpus of code & math and a legal corpus and is widely applicable. |
Empathy Prediction from Diverse Perspectives (2025.acl-long)
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Francine Chen, Scott Carter, Tatiana Lau, Nayeli Suseth Bravo, Sumanta Bhattacharyya, Kate Sieck, Charlene C. Wu
| Challenge: | Empathy from perspectives is a prediction tool that uses a rater’s perspective to predict the rater's empathy towards a story. |
| Approach: | They developed a model that uses a rater’s perspective as context for predicting the rater's empathy towards a story. |
| Outcome: | The proposed model improves on the EmpathyFromPerspectives dataset and compares it with baseline models. |
Robust Machine Reading Comprehension by Learning Soft labels (2020.coling-main)
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| Challenge: | Neural models have achieved great success on the task of machine reading comprehension, which are typically trained on hard labels. |
| Approach: | They propose a robust training method for machine reading comprehension models to address label sparseness problem by using three strategies to train models on soft labels. |
| Outcome: | The proposed method improves the baseline model performance and achieves state-of-the-art performance on NewsQA and QUOREF. |
STARS: A Unified Framework for Singing Transcription, Alignment, and Refined Style Annotation (2025.findings-acl)
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Wenxiang Guo, Yu Zhang, Changhao Pan, Zhiyuan Zhu, Ruiqi Li, ZheTao Chen, Wenhao Xu, Fei Wu, Zhou Zhao
| Challenge: | Existing automated singing annotation (ASA) methods tackle isolated aspects of the annotation pipeline. |
| Approach: | They propose a framework that addresses transcription, alignment, and refined style annotations. |
| Outcome: | The proposed framework delivers comprehensive multi-level annotations encompassing: (1) precise phoneme-audio alignment, (2) robust note transcription and temporal localization, (3) expressive vocal technique identification, and (4) global stylistic characterization including emotion and pace. |
An Empirical Study of Frame Selection for Text-to-Video Retrieval (2023.findings-emnlp)
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| Challenge: | Existing methods for text-to-video retrieval select a subset of frames to represent video content . current methods only explore video contents while ignoring relevancy to texts . |
| Approach: | They propose to use a subset of frames to represent video content for TVR . they analyze six different frame selection methods to determine their effectiveness . |
| Outcome: | The proposed method improves retrieval efficiency without sacrificing visual details . the proposed method explores the video contents while ignoring relevancy to texts . |
Chain-of-Scrutiny: Detecting Backdoor Attacks for Large Language Models (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities across various domains, but are vulnerable to backdoor attacks. |
| Approach: | They propose a chain-of-scrutiny approach which leverages LLMs’ unique reasoning abilities to mitigate backdoor attacks. |
| Outcome: | The proposed model is well-suited for the popular API-only LLM deployments, enabling detection at minimal cost and with little data. |
Pruning Adatperfusion with Lottery Ticket Hypothesis (2022.findings-naacl)
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| Challenge: | Pre-trained language models are computationally expensive to fine-tune and require large storage. |
| Approach: | They propose a method to identify the influence of each adapter module and a way to prune adapters based on the Lottery Ticket Hypothesis. |
| Outcome: | The proposed model reduces size significantly while keeping performance intact. |
Learning Geometry-Aware Representations for New Intent Discovery (2024.acl-long)
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| Challenge: | Existing methods for intent classification fail to distinguish new intents due to intertwined centers . a novel framework that learns geometry-aware representations to maximally separate all intents is proposed . |
| Approach: | They propose a new intent discovery framework that learns geometry-aware representations to maximally separate all intents. |
| Outcome: | The proposed framework achieves a new state-of-the-art performance on three benchmarking datasets. |
Doc-React: Multi-page Heterogeneous Document Question-answering (2025.acl-short)
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Junda Wu, Yu Xia, Tong Yu, Xiang Chen, Sai Sree Harsha, Akash V Maharaj, Ruiyi Zhang, Victor Bursztyn, Sungchul Kim, Ryan A. Rossi, Julian McAuley, Yunyao Li, Ritwik Sinha
| Challenge: | Existing methods for integrating information across multiple modalities are suboptimal for multi-page, multimodal documents. |
| Approach: | They propose an adaptive iterative framework that balances information gain and uncertainty reduction at each step. |
| Outcome: | The proposed framework captures relevant multimodal content and achieves strong performance on complex QA tasks. |
ObjChangeVR: Object State Change Reasoning from Continuous Egocentric Views in VR Environments (2026.eacl-long)
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| Challenge: | Recent advances in multimodal large language models (MLLMs) have shown remarkable capabilities in scene understanding. |
| Approach: | They propose a framework that combines viewpoint-aware and temporal-based retrieval to identify relevant frames, along with cross-view reasoning that reconciles inconsistent evidence from multiple viewpoints. |
| Outcome: | Extensive experiments show that the proposed framework outperforms baseline approaches across multiple MLLMs. |
Uncertainty-Aware Routing for Principled Alignment with MoE Dynamics (2026.acl-long)
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Yilong Chen, Junyuan Shang, Yuchen Feng, Zhenyu Zhang, Naibin Gu, Ziqi Wang, Tingwen Liu, Shuohuan Wang, Yu Sun, Hua Wu, Haifeng Wang
| Challenge: | Mixture-of-Experts (MoE) is a cornerstone for scaling LLMs, yet its training dynamics remain poorly understood, often leading to sub-optimal specialization. |
| Approach: | They propose to use Helmholtz Free Energy and Router Entropy to study the MoE lifecycle and identify a universal Three-Stage Phase Transition . |
| Outcome: | The proposed model reduces perplexity and improves expert distinctiveness, offering a principled path toward thermodynamically aligned computation. |
HonestBait: Forward References for Attractive but Faithful Headline Generation (2023.findings-acl)
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| Challenge: | Current approaches to generating attractive headlines often learn directly from data based on clicks and views . clickbait models fail to reveal how much interest is raised by the writing style and how much is due to the event or topic itself . |
| Approach: | They propose a framework for generating headlines using forward references . they use a dataset containing pairs of fake news and verified news . |
| Outcome: | The proposed framework yields more attractive headlines while maintaining high veracity . the framework is based on a dataset containing fake news with verified news . |
Cool-Fusion: Fuse Large Language Models without Training (2025.acl-long)
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| Challenge: | Cool-Fusion is a simple yet effective approach to combine two or more heterogeneous large language models . |
| Approach: | They propose a method that fuses the knowledge of two or more heterogeneous large language models to leverage complementary strengths. |
| Outcome: | The proposed method increases accuracy from three strong source LLMs on GSM8K by 17.4%. |
Named Entity Recognition via Noise Aware Training Mechanism with Data Filter (2021.findings-acl)
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| Challenge: | Existing methods for named entity recognition (NER) do not distinguish noisy from hard samples. |
| Approach: | They propose a noise-aware-with-filter method to help model identify noisy samples . they propose 'incomplete trust' loss function which boosts L CRF with a robust term . |
| Outcome: | The proposed method outperforms the existing methods on six real-world Chinese and English NER datasets. |
SEGMENT+: Long Text Processing with Short-Context Language Models (2024.emnlp-main)
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Wei Shi, Shuang Li, Kerun Yu, Jinglei Chen, Zujie Liang, Xinhui Wu, Yuxi Qian, Feng Wei, Bo Zheng, Jiaqing Liang, Jiangjie Chen, Yanghua Xiao
| Challenge: | Existing frameworks that increase context window do not guarantee robust performance across long input tasks. |
| Approach: | They propose a framework that enables language models to handle extended inputs within limited context windows efficiently. |
| Outcome: | The framework improves performance on long-document question-answering and Needle-in-a-Haystack tasks. |
SURE: Mutually Visible Objects and Self-generated Candidate Labels For Relation Extraction (2025.coling-main)
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| Challenge: | Joint relation extraction models face high computational complexity, complex network architectures, difficult parameter tuning and limited interpretability. |
| Approach: | They develop a candidate label marker mechanism that prioritizes strategic label selection over simple label generation. |
| Outcome: | The proposed candidate label marks improve the SOTA methods by 2.5%, 1.9%, 1.2% . the proposed candidate labels improve the performance of the proposed methods . |
Explainable Chain-of-Thought Reasoning: An Empirical Analysis on State-Aware Reasoning Dynamics (2025.findings-emnlp)
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Sheldon Yu, Yuxin Xiong, Junda Wu, Xintong Li, Tong Yu, Xiang Chen, Ritwik Sinha, Jingbo Shang, Julian McAuley
| Challenge: | Recent advances in chain-of-thought prompting have demonstrated the ability of large language models to perform multi-step reasoning. |
| Approach: | They propose a framework to analyze latent dynamics of CoT trajectories for interpretability . they segment generated CoT into discrete reasoning steps and abstract each step into a spectral embedding based on token-level Gram matrices . |
| Outcome: | The proposed framework segments generated CoT steps into discrete reasoning steps, abstracts each step into a spectral embedding based on token-level Gram matrices, and clusters these embeddements into semantically meaningful latent states. |
TriEx: A Game-based Tri-View Framework for Explaining Internal Reasoning in Multi-Agent LLMs (2026.acl-long)
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| Challenge: | Existing explainability methods for large language models have been limited in capturing interaction-dependent belief dynamics and multi-agent reasoning. |
| Approach: | They propose a tri-view explainability framework that instruments sequential decision making with aligned artifacts. |
| Outcome: | The proposed framework enables analysis of explanation faithfulness, belief dynamics, and evaluator reliability, revealing systematic mismatches between what agents say, what they believe, and what they do. |
Retrieval and Reasoning on KGs: Integrate Knowledge Graphs into Large Language Models for Complex Question Answering (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have performed impressively in various NLP tasks, but their inherent hallucination phenomena severely challenge their credibility in complex reasoning. |
| Approach: | They propose to integrate explainable Knowledge Graphs (KGs) with LLMs to alleviate hallucinations . they construct subgraphs to enhance the retrieval capabilities of KGs via CoT reasoning. |
| Outcome: | Extensive experiments on two KGQA datasets show that the proposed model achieves convincing performance compared to strong baselines. |
How Well Do LLMs Handle Cantonese? Benchmarking Cantonese Capabilities of Large Language Models (2025.findings-naacl)
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| Challenge: | Cantonese has scant representation in NLP research, especially compared to other languages from similarly developed regions. |
| Approach: | They propose to evaluate Cantonese LLM performance in factual generation, mathematical logic, complex reasoning, and general knowledge in Cantonesian. |
| Outcome: | The proposed models will evaluate Cantonese's performance in factual generation, mathematical logic, complex reasoning, and general knowledge in Cantone. |
Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models (2024.findings-acl)
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| Challenge: | Existing studies on the confidence calibration of LLMs have not explored the effects of different prompting strategies on LLM performance. |
| Approach: | They propose Fact-and-Reflection prompting which improves LLM confidence calibration . they propose to use human cognition to elicit known "facts" and ask model to "reflect" over them . |
| Outcome: | The proposed method lowers the expected calibration error by 23.5% on multi-purpose QA tasks. |
Distributional Clarity: The Hidden Driver of RL-Friendliness in Large Language Models (2026.acl-long)
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| Challenge: | RL-friendly models exhibit intra-class compactness and inter-class separation in probability assignments . under identical training, Qwen models achieve substantial gains, while others like Llama yield limited improvements. |
| Approach: | They propose a method to quantify distributional clarity in probability space . they show distributional clearness is a trainable property underlying RL-Friendliness . |
| Outcome: | The proposed model families achieve substantial gains under identical training, while others like Llama yield limited improvements. |
Uncovering Scaling Laws for Large Language Models via Inverse Problems (2025.findings-emnlp)
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Arun Verma, Zhaoxuan Wu, Zijian Zhou, Xiaoqiang Lin, Zhiliang Chen, Rachael Hwee Ling Sim, Rui Qiao, Jingtan Wang, Nhung Bui, Xinyuan Niu, Wenyang Hu, Gregory Kang Ruey Lau, Zi-Yu Khoo, Zitong Zhao, Xinyi Xu, Apivich Hemachandra, See-Kiong Ng, Bryan Kian Hsiang Low
| Challenge: | Large Language Models (LLMs) have achieved remarkable success across diverse domains. |
| Approach: | inverse problems can efficiently uncover scaling laws that guide the building of LLMs, authors argue . authors propose brute-force approaches to improve LLM training costs due to high costs . |
| Outcome: | This paper advocates that inverse problems can efficiently uncover scaling laws that guide the building of LLMs to achieve the desirable performance with significantly better cost-effectiveness. |
Beyond Surface-Level Pattern Trap: LLM Agents for Faster and Smarter Cross-Architecture Code Migration (2026.findings-acl)
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| Challenge: | cross-architecture code migration is a resource-intensive and errorprone task. |
| Approach: | a framework for cross-architecture code migration is proposed to decouple implementation details through functional mining and code refactoring. |
| Outcome: | a new framework improves performance and correctness over state-of-the-art frameworks on OpenCV migration tasks. |
I-AM-G: Interest Augmented Multimodal Generator for Item Personalization (2024.emnlp-main)
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| Challenge: | e-commerce and recommender systems lack a framework for personalized generation . a new framework extracts tags from multimodal information of items that the user has interacted with . |
| Approach: | They propose a framework that extracts tags from multimodal information and rewrites item description . they then use a decoupled text-to-text and image-to image retriever to search for similar item text . |
| Outcome: | The proposed framework can generate results aligned with user preferences . it can be used in e-commerce and recommender systems to win over diverse user base . |
Bridging the Capability Gap: Joint Alignment Tuning for Harmonizing LLM-based Multi-Agent Systems (2025.findings-emnlp)
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Minghang Zhu, Zhengliang Shi, Zhiwei Xu, Shiguang Wu, Lingjie Wang, Pengjie Ren, Zhaochun Ren, Zhumin Chen
| Challenge: | Existing methods for fine-tuning agents are often inadequate . a multi-agent system can solve complex tasks by dividing responsibilities among specialized agents . |
| Approach: | a new framework is proposed to improve agents collaboration through iterative alignment. |
| Outcome: | The proposed framework outperforms state-of-the-art benchmarks on held-in and held-out tasks. |
Leveraging Entity Information for Cross-Modality Correlation Learning: The Entity-Guided Multimodal Summarization (2024.findings-acl)
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| Challenge: | Multimodal Summarization with Multimodal Output (MSMO) is a new approach to produce a multimodal summary that integrates both text and relevant images. |
| Approach: | They propose an Entity-Guided Multimodal Summarization model that integrates both text and relevant images to produce a multimodal summary. |
| Outcome: | The proposed model integrates text-image and entity-image information and refines image selection through knowledge distillation from a pre-trained vision-language model. |
Reasoning in a Combinatorial and Constrained World: Benchmarking LLMs on Natural-Language Combinatorial Optimization (2026.findings-acl)
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| Challenge: | NLCO evaluates large language models for combinatorial optimization (CO) . existing evaluations emphasize relatively simple reasoning competencies . |
| Approach: | They propose a combinatorial optimization benchmark that evaluates large language models on CO reasoning. |
| Outcome: | The proposed model can handle combinatorial optimization without writing code or calling external solvers. |
Towards Boosting LLMs-driven Relevance Modeling with Progressive Retrieved Behavior-augmented Prompting (2025.coling-industry)
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| Challenge: | Existing approaches to relevance modeling have lacked generalization and accuracy . recent studies have focused on capturing the semantic relationships between queries and items . |
| Approach: | They propose a framework that integrates world knowledge stored in LLMs with specialized domain knowledge represented by user behavior data for promising performance. |
| Outcome: | The proposed framework can handle full-scale search traffics of Alipay with acceptable cost and latency. |
Jailbreak Open-Sourced Large Language Models via Enforced Decoding (2024.acl-long)
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Hangfan Zhang, Zhimeng Guo, Huaisheng Zhu, Bochuan Cao, Lu Lin, Jinyuan Jia, Jinghui Chen, Dinghao Wu
| Challenge: | Existing studies show that Large Language Models can be misused to generate undesired content. |
| Approach: | They propose to use large language models to manipulate the generation process to generate undesired content without heavy computations or prompt designs. |
| Outcome: | The proposed method shows that open-sourced large language models could be misused to generate undesired content without heavy computations or prompt designs. |
DOSE: Data Selection for Multi-Modal LLMs via Off-the-Shelf Models (2026.findings-acl)
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| Challenge: | Existing data filtering methods are expensive because they are trained on the same data they are meant to screen. |
| Approach: | They propose to use off-the-shelf pretrained models that have never seen the target data to select training samples for larger and stronger multimodal models without task-specific training. |
| Outcome: | The proposed method can achieve comparable or even better results than those trained on the full dataset in standard VQA and math benchmarks. |
Unsupervised Multi-scale Expressive Speaking Style Modeling with Hierarchical Context Information for Audiobook Speech Synthesis (2022.coling-1)
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| Challenge: | a recent study has shown that expressiveness of audiobooks is limited by the averaged global-scale speaking style representation. |
| Approach: | They propose an unsupervised multi-scale context-sensitive text-to-speech model for audiobooks . they use hierarchical context encoder to predict global-scale contextual style embeddings . |
| Outcome: | The proposed model outperforms existing models on a real-world Mandarin audio dataset. |
On the Vulnerability of Safety Alignment in Open-Access LLMs (2024.findings-acl)
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Jingwei Yi, Rui Ye, Qisi Chen, Bin Zhu, Siheng Chen, Defu Lian, Guangzhong Sun, Xing Xie, Fangzhao Wu
| Challenge: | Large language models (LLMs) are susceptible to malicious exploitation, but are often rejected and limited harmfulness is limited. |
| Approach: | They propose two types of reverse alignment techniques: reverse supervised fine-tuning (RSFT) and reverse preference optimization (RPO). |
| Outcome: | The proposed methods can significantly enhance the success rate and harmfulness of jailbreak attacks, but they face high rejection rates and limited harmfulness. |
Grafting Pre-trained Models for Multimodal Headline Generation (2022.emnlp-industry)
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| Challenge: | Existing approaches to generate video headlines with pre-trained language models are labor intensive and impractical. |
| Approach: | They propose to graft the encoder from the pre-trained video-language model on the generative pre-trainer model and propose a consensus fusion mechanism for the integration of different components. |
| Outcome: | The proposed model achieves strong results on a brand-new dataset collected from real-world applications. |
Knowledge-augmented Financial Market Analysis and Report Generation (2024.emnlp-industry)
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| Challenge: | Existing methods to generate financial market analysis text require extensive financial knowledge and skill of financial analysts. |
| Approach: | They propose a task to generate financial market analysis reports using financial market data using a financial knowledge graph. |
| Outcome: | The proposed framework outperforms large-scale language models and retrieval-augmented baselines in the financial market analysis generation task. |
Forging Multiple Training Objectives for Pre-trained Language Models via Meta-Learning (2022.findings-emnlp)
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| Challenge: | Empirical studies show that learning multiple training objectives in a single model makes the learned language representation barely converge to the desired optimum. |
| Approach: | They propose a meta-learning-based adaptive sampler which learns latent sampling pattern on arbitrary pre-training objectives. |
| Outcome: | Empirical studies show that learning multiple objectives in a single model makes it difficult to achieve the desired optimum. |
Defense Against Prompt Injection Attack by Leveraging Attack Techniques (2025.acl-long)
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| Challenge: | Recent attacks leverage LLMs’ instruction-following abilities and their inabilities to distinguish instructions injected in the data content. |
| Approach: | They invert the intention of prompt injection methods to develop novel defense methods based on previous training-free attack methods by repeating the attack process with the original input instruction rather than the injected instruction. |
| Outcome: | The proposed methods outperform existing defense approaches, achieving state-of-the-art results. |
Optimizing User Profiles via Contextual Bandits for Retrieval-Augmented LLM Personalization (2026.acl-long)
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Linfeng Du, Ye Yuan, Zichen Zhao, Fuyuan Lyu, Emiliano Penaloza, Xiuying Chen, Zipeng Sun, Jikun Kang, Laurent Charlin, Xue Liu, Haolun Wu
| Challenge: | Existing approaches for personalizing large language models require modifying parameters. |
| Approach: | They propose a lightweight approach to personalizing large language models via retrieval augmentation . relevance serves as an unreliable proxy for utility, they argue . |
| Outcome: | The proposed framework outperforms strong heuristic and retrieval-augmented baselines on nine personalization tasks. |
Mitigating Lost in Multi-turn Conversation via Curriculum RL with Verifiable Accuracy and Abstention Rewards (2026.acl-long)
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Ming Li, Pei Chen, Zhenhao Zhang, Tao Yang, Xinyang Zhang, Han Li, Tianyu Cao, Ming Zeng, Zhuofeng Wu, Meng Jiang, Huasheng Li, Lihong Li, Bing Yin
| Challenge: | Large Language Models exhibit strong capabilities in single-turn instruction following but suffer from Lost-in-Conversation (LiC) when instructions are revealed progressively in multi-turn settings, models get "Lost in Conversation" |
| Approach: | They propose a framework that encourages models to generate correct answers and judge solvability in multi-turn conversations. |
| Outcome: | The proposed framework improves models' ability to balance problem-solving with abstention . it reduces premature answering behaviors that cause lost-in-conversation (LiC) |
Generative Annotation for ASR Named Entity Correction (2025.emnlp-main)
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Yuanchang Luo, Daimeng Wei, Shaojun Li, Hengchao Shang, Jiaxin Guo, Zongyao Li, Zhanglin Wu, Xiaoyu Chen, Zhiqiang Rao, Jinlong Yang, Hao Yang
| Challenge: | Existing named entity correction models fail to transcribe domain-speciffcnamed entities when theforms of the wrongly-transcribed words and the ground-truth entity are signiffcantly different. |
| Approach: | They propose a method that utilizes speech sound features to retrieve candidate entities . it uses speech sound feature to annotate entityerrors in ASR transcripts . |
| Outcome: | The proposed method can bring signiffcant improvement to entity accuracy. |
A Survey on LLM-based Conversational User Simulation (2026.eacl-long)
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Bo Ni, Yu Wang, Leyao Wang, Branislav Kveton, Franck Dernoncourt, Yu Xia, Hongjie Chen, Reuben Luera, Samyadeep Basu, Subhojyoti Mukherjee, Puneet Mathur, Nesreen K. Ahmed, Junda Wu, Li Li, Huixin Zhang, Ruiyi Zhang, Tong Yu, Sungchul Kim, Jiuxiang Gu, Zhengzhong Tu, Alexa Siu, Zichao Wang, Seunghyun Yoon, Nedim Lipka, Namyong Park, Zihao Lin, Trung Bui, Yue Zhao, Tyler Derr, Ryan A. Rossi
| Challenge: | Recent advances in large language models (LLMs) have enabled high-fidelity generation of synthetic user conversation. |
| Approach: | They propose a taxonomy covering user granularity and simulation objectives . they analyze core techniques and evaluation methodologies to help them understand the latest developments . |
| Outcome: | The proposed model enables high-fidelity generation of synthetic user conversation. |
Tracking the Limits of Knowledge Propagation: How LLMs Fail at Multi-Step Reasoning with Conflicting Knowledge (2026.eacl-long)
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| Challenge: | Existing benchmarks for analyzing the performance of Large Language Models (LLMs) focus on single knowledge updates and fact recall, but do not consider how these updates affect downstream reasoning. |
| Approach: | They propose a benchmark to study how LLMs propagate new knowledge when it conflicts with the model's parametric knowledge. |
| Outcome: | The proposed benchmark compared models with no updated facts to show that the new methods worsen performance and improve reasoning performance. |
Data Interpreter: An LLM Agent for Data Science (2025.findings-acl)
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Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, Li Zhang, Lingyao Zhang, Min Yang, Mingchen Zhuge, Taicheng Guo, Tuo Zhou, Wei Tao, Robert Tang, Xiangtao Lu, Xiawu Zheng, Xinbing Liang, Yaying Fei, Yuheng Cheng, Yongxin Ni, Zhibin Gou, Zongze Xu, Yuyu Luo, Chenglin Wu
| Challenge: | Large Language Models (LLMs) excel in various domains but face challenges when applied to data science workflows due to their complex, multi-stage nature. |
| Approach: | They propose a hierarchical graph-based agent that represents complexity and a progressive strategy for step-by-step verification, refinement, and consistent context management. |
| Outcome: | The proposed agent surpasses state-of-the-art baselines on the MATH dataset and performs better on InfiAgent-DABench. |
CIL: Contrastive Instance Learning Framework for Distantly Supervised Relation Extraction (2021.acl-long)
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| Challenge: | Existing methods to reduce noise from DS generated training data are not effective for distantly supervised relation extraction (DSRE) |
| Approach: | They propose a multi-instance learning framework to reduce DS noise by dividing training instances into several bags and using them as new data units. |
| Outcome: | The proposed framework improves on NYT10, GDS and KBP with significant improvements over existing methods. |
Training LLMs for Divide-and-Conquer Reasoning Elevates Test-Time Scalability (2026.acl-long)
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Xiao Liang, Zhong-Zhi Li, Zhenghao Lin, Eric Hanchen Jiang, Hengyuan Zhang, Yelong Shen, Kai-Wei Chang, Ying Nian Wu, Yeyun Gong, Weizhu Chen
| Challenge: | Large language models have demonstrated strong reasoning capabilities through step-by-step chain-of-thought (CoT) reasoning, but their strictly sequential nature constrains test-time scalability. |
| Approach: | They propose an end-to-end reinforcement learning framework to enhance LLMs' DAC-style reasoning capacity by decomposing a problem into subproblems and solving them sequentially. |
| Outcome: | The proposed model surpasses CoT by 8.6% and 6.3% on competition-level benchmarks and is available at the [github.com/MasterVito/DAC-RL]. |
Visualizing Trends of Key Roles in News Articles (D19-3)
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| Challenge: | a demonstration system visualizes news trend of key roles based on natural language processing techniques . semantic role labelling and word embeddings can help users understand news topics . |
| Approach: | They propose a system that visualizes the news trend of key roles based on natural language processing techniques. |
| Outcome: | The proposed system analyzes the news trend of key roles using semantic role labelling . it also analyzes how similarities between key roles and news topics change over time . |
CCTC: A Cross-Sentence Chinese Text Correction Dataset for Native Speakers (2022.coling-1)
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| Challenge: | Chinese text correction datasets focus on detecting and correcting Chinese spelling errors and grammatical errors. |
| Approach: | They propose a Chinese text correction dataset for native speakers . they manually annotated 1,500 Chinese texts written by native speakers. |
| Outcome: | The proposed dataset can detect and correct Chinese spelling errors and grammatical errors. |
E-VarM: Enhanced Variational Word Masks to Improve the Interpretability of Text Classification Models (2022.coling-1)
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Ling Ge, ChunMing Hu, Guanghui Ma, Junshuang Wu, Junfan Chen, JiHong Liu, Hong Zhang, Wenyi Qin, Richong Zhang
| Challenge: | Empirical studies show that our approach outperforms the SOTA methods in improving the interpretability of text classification models. |
| Approach: | They propose an enhanced variational word masks approach that exploits the Variational Information Bottleneck to obtain task-specific words. |
| Outcome: | Empirical results show that the proposed method outperforms the SOTA methods in improving the interpretability of the model. |
Unveiling Selection Biases: Exploring Order and Token Sensitivity in Large Language Models (2024.findings-acl)
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| Challenge: | Using zero-shot or few-shot prompting, Large Language Models have been widely adopted in downstream applications. |
| Approach: | They propose to quantify the impact of option order and token usage on LLMs and propose mitigation strategies to enhance model performance. |
| Outcome: | The proposed mitigation strategies improve model performance and reduce the impact of token and order sensitivity on LLMs. |
Beyond Noise: Characterizing Creative Potential in Unverifiable LLM Hallucinations (2026.acl-long)
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Yu Yan, Chunhong Zhang, Haiyu Zhao, Ziyang Zeng, Zihao Liu, Yongkang Wu, Jianzhou Diao, YiJie Chen, Shujie Wang, Zheng Hu
| Challenge: | Large Language Models generate outputs that extend beyond established knowledge . prior work does not characterize the unverifiable space as a whole . |
| Approach: | They propose a novelty-verifiability characterization that distinguishes Creative Synthesis from Groundless Fabrication by a conceptual creation task. |
| Outcome: | The proposed model distinguishes Creative Synthesis (Region A) from Groundless Fabrication (Regium B) it shows that Region A is non-negligible and robust, persisting across generation strategies, models, domains, and embedding choices. |
HyperExpan: Taxonomy Expansion with Hyperbolic Representation Learning (2021.findings-emnlp)
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| Challenge: | Existing taxonomies have limited coverage due to expensive manual curation process. |
| Approach: | They propose an algorithm that expands existing taxonomies to preserve their structure in a more expressive hyperbolic embedding space and learns to represent concepts and their relations with a hyperbolical Graph Neural Network. |
| Outcome: | The proposed algorithm outperforms baseline models with representation learning in a Euclidean feature space and achieves state-of-the-art performance on the taxonomy expansion benchmarks. |
Flaming-hot Initiation with Regular Execution Sampling for Large Language Models (2025.findings-naacl)
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Weizhe Chen, Zhicheng Zhang, Guanlin Liu, Renjie Zheng, Wenlei Shi, Chen Dun, Zheng Wu, Xing Jin, Lin Yan
| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities across various domains since the release of ChatGPT . a key challenge in developing these general capabilities is efficiently sourcing diverse, high-quality data. |
| Approach: | They introduce Flaming-hot Initiation with Regular Execution (FIRE) sampling to efficiently find good responses by promoting diversity. |
| Outcome: | The proposed method enhances inference-time generation quality and benefits training in the alignment stage. |
LLMs Trust Humans More, That’s a Problem! Unveiling and Mitigating the Authority Bias in Retrieval-Augmented Generation (2025.acl-long)
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Yuxuan Li, Xinwei Guo, Jiashi Gao, Guanhua Chen, Xiangyu Zhao, Jiaxin Zhang, Quanying Liu, Haiyan Wu, Xin Yao, Xuetao Wei
| Challenge: | Large language models (LLMs) generate outputs that stray from user input or contravene established knowledge. |
| Approach: | They propose a new phenomenon, Authority Bias, where LLMs favor one knowledge source over the other . they propose atomic information that generates conflicts and a Conflict Detection Enhanced Query framework . |
| Outcome: | The proposed framework reduces Authority bias in large language models . it detects conflicts, performs credibility assessment on conflicting paragraphs, and detects perturbed text . |
Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks (2024.naacl-long)
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Zhaofeng Wu, Linlu Qiu, Alexis Ross, Ekin Akyürek, Boyuan Chen, Bailin Wang, Najoung Kim, Jacob Andreas, Yoon Kim
| Challenge: | Recent language models possess impressive performance across a wide range of tasks . however, they often rely on narrow, non-transferable procedures for task-solving . |
| Approach: | They propose to evaluate language models using "counterfactual" task variants that deviate from standard tasks. |
| Outcome: | The proposed framework shows that language models perform better on a wide range of tasks compared to the default conditions. |
QBridge: Bridging Natural Language and SQL via Gold Query Rewriting with Agentic Refinement (2026.acl-long)
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| Challenge: | Natural language to SQL (NL2SQL) is an intuitive interface for querying structured data . but real user questions are noisy, ambiguous, and weakly grounded to database semantics. |
| Approach: | They propose an agentic feedback-driven NL2SQL framework that bridges natural language and SQL via Gold Query. |
| Outcome: | The proposed framework outperforms strong prompting and agentic baselines on spider, BIRD, and three robustness variants on NL2SQL. |
Exploiting Emotion-Semantic Correlations for Empathetic Response Generation (2023.findings-emnlp)
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Zhou Yang, Zhaochun Ren, Wang Yufeng, Xiaofei Zhu, Zhihao Chen, Tiecheng Cai, Wu Yunbing, Yisong Su, Sibo Ju, Xiangwen Liao
| Challenge: | Empathetic response generation aims to generate empathetic responses by understanding the speaker’s emotional feelings from the language of dialogue. |
| Approach: | They propose a dynamical Emotion-Semantic Correlation Model (ESCM) which constructs dynamic emotion-semantics through the interaction of context and emotions. |
| Outcome: | The proposed model understands emotions more accurately and expresses fluent and informative empathetic responses. |
Tailoring Diagnostic Modeling to Individual Learners: Personalized Distractor Generation via MCTS-Guided Reasoning Reconstruction (2026.acl-long)
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| Challenge: | Current distractor generation methods produce shared distractors for all students, ignoring individual variations in reasoning, which limits their diagnostic effectiveness. |
| Approach: | They propose a method which tailors distractors to each student’s specific cognitive flaws, inferred from their past question-answering (QA) history. |
| Outcome: | The proposed framework outperforms existing methods in generating plausible distractors and adapts to group-level settings. |
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)
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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He
| Challenge: | Document images are characterized by higher resolutions, denser content, and more complex structural layouts. |
| Approach: | They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition. |
| Outcome: | The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead. |
Towards Reliable Large Audio Language Model (2025.findings-acl)
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Ziyang Ma, Xiquan Li, Yakun Song, Wenxi Chen, Chenpeng Du, Jian Wu, Yuanzhe Chen, Zhuo Chen, Yuping Wang, Yuxuan Wang, Xie Chen
| Challenge: | Recent advances in large audio language models (LALMs) have demonstrated impressive results and promising prospects in universal understanding and reasoning across speech, music, and general sound. |
| Approach: | They propose to use training-free and training-based methods to enhance LALM reliability to different extents. |
| Outcome: | The proposed methods improve the reliability of large audio language models to different extents. |
Step Guided Reasoning: Improving Mathematical Reasoning using Guidance Generation and Step Reasoning (2025.emnlp-main)
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| Challenge: | Existing approaches to improve mathematical reasoning require extensive datasets for training or depend on few-shot methods that compromise computational accuracy. |
| Approach: | They propose a training-free adaptation framework that efficiently equips general-purpose pre-trained language models with enhanced mathematical reasoning capabilities. |
| Outcome: | The proposed framework outperforms Qwen2.5-72B-Math-Instruct on MMLU-STEM with a score of 90.9%, compared to 87.3%. |
Let The Jury Decide: Fair Demonstration Selection for In-Context Learning through Incremental Greedy Evaluation (2025.findings-acl)
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| Challenge: | Existing demonstration selection strategies focus on optimizing performance metrics such as accuracy. |
| Approach: | They propose a framework for selecting fair and representative demonstrations that improve group fairness in In-Context Learning. |
| Outcome: | The proposed framework improves fairness metrics without compromising accuracy. |
CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents (2025.findings-acl)
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Tianqi Xu, Linyao Chen, Dai-Jie Wu, Yanjun Chen, Zecheng Zhang, Xiang Yao, Zhiqiang Xie, Yongchao Chen, Shilong Liu, Bochen Qian, Anjie Yang, Zhaoxuan Jin, Jianbo Deng, Philip Torr, Bernard Ghanem, Guohao Li
| Challenge: | Existing benchmarks for MLM agents in interactive environments are limited by their focus on a single environment, lack of detailed and generalized evaluation methods, and the complexity of constructing tasks and evaluators. |
| Approach: | They propose a cross-environment agent benchmark framework that integrates graph-based evaluation and task generation methods. |
| Outcome: | The proposed framework supports multiple devices and can be easily extended to any environment with a Python interface. |
A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAM𝛥 Integration into Upcycled MoE (2026.acl-long)
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Hao Zhou, Tianhao Li, Zhijun Wang, Shuaijie She, Linjuan Wu, Hao-Ran Wei, Baosong Yang, Jiajun Chen, Shujian Huang
| Challenge: | Large Language Models (LLMs) are expensive and require extensive Continued Pre-Training and data-intensive alignment to expand. |
| Approach: | They propose a method which upcycles a dense model into a Mixture-of-Experts architecture, allocating different experts to different languages. |
| Outcome: | Experiments show that the proposed model upcycles a dense model into a Mixture-of-Experts(MoE) architecture, allocating different experts to different languages. |
FLRC: Fine-grained Low-Rank Compressor for Efficient LLM Inference (2025.emnlp-main)
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| Challenge: | Low-rank compression can reduce memory usage and computational demand, but results are poor during decoding. |
| Approach: | They propose a fine-grained low-rank compression algorithm that determines optimal rank allocation for each layer and incorporates progressive low-ranked decoding to maintain text generation quality. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on summarization tasks and on understanding tasks. |
Can We Trust AI Doctors? A Survey of Medical Hallucination in Large Language and Large Vision-Language Models (2025.findings-acl)
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Zhihong Zhu, Yunyan Zhang, Xianwei Zhuang, Fan Zhang, Zhongwei Wan, Yuyan Chen, QingqingLong QingqingLong, Yefeng Zheng, Xian Wu
| Challenge: | Hallucination is a critical challenge for large language models and large vision-language models (LVLMs) however, dedicated research on medical hallucinations remains unexplored. |
| Approach: | They provide a unified perspective on medical hallucination for both LLMs and LVLMs, and delve into its causes. |
| Outcome: | The proposed models have demonstrated impressive performance on a variety of medical benchmarks. |
SafeInt: Shielding Large Language Models from Jailbreak Attacks via Safety-Aware Representation Intervention (2025.findings-emnlp)
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| Challenge: | Jailbreak attacks exploit vulnerabilities in large language models to induce undesirable behavior . existing defenses cannot dynamically adjust representations based on harmfulness of queries . |
| Approach: | They propose a representation-aware representation method that shields LLMs from jailbreak attacks . SafeInt relocates jailbreak-related representations into the rejection region . |
| Outcome: | The proposed method outperforms baseline defenses while maintaining utility . it relocates jailbreak-related representations into the rejection region . |
Exploring In-Context Learning for Knowledge Grounded Dialog Generation (2023.findings-emnlp)
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| Challenge: | Existing knowledge grounded dialog generation models are prone to hallucination and produce factually inaccurate outputs. |
| Approach: | They propose a retrieval-based framework which leverages in-context learning and retrieval techniques to enhance LLMs on knowledge grounded dialog generation. |
| Outcome: | The proposed framework outperforms existing training-based models on a large-scale knowledge graph with 1M+ facts and is expected to perform knowledge-intensive tasks. |
Dynamic Feature Fusion for Sign Language Translation Using HyperNetworks (2025.findings-naacl)
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| Challenge: | Using RGB and keypoint streams, sign language translation is highly dependent on the brain's ability to process color, shape, and motion simultaneously. |
| Approach: | They propose a hypernetwork-based fusion method that extracts salient features from RGB and keypoint streams and introduces self-distillation and SST contrastive learning to maintain feature advantages while aligning the global semantic space. |
| Outcome: | The proposed method achieves state-of-the-art performance on two public sign language datasets, reducing model parameters by about two-thirds. |
Infinity-Parser: Layout-Aware Reinforcement Learning with High-quality Document Parsing Dataset (2026.findings-acl)
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Baode Wang, Biao Wu, Weizhen Li, Meng Fang, Zuming Huang, Jun Huang, Yanjie Liang, Haozhe Wang, Ling Chen, Wei Chu, Yuan Qi
| Challenge: | Existing supervised fine-tuning methods struggle to generalize across document types, leading to poor performance. |
| Approach: | They propose layoutRL, a reinforcement learning framework that optimizes layout understanding through composite rewards integrating normalized edit distance, paragraph count accuracy, and reading order preservation. |
| Outcome: | The proposed model outperforms specialized document parsing systems and general-purpose vision-language models on a broad range of document types, languages, and structural complexities. |
Neural Machine Translation for Agglutinative Languages via Data Rejuvenation (2025.acl-srw)
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| Challenge: | Recent years, advances in Neural Machine Translation (NMT) heavily rely on large-scale parallel corpora. |
| Approach: | They propose to combine fine-grained inactive sample identification with target-side rejuvenation to improve translation quality from agglutinative languages. |
| Outcome: | The proposed framework improves on four low-resource agglutinative language tasks. |
MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct (2025.findings-acl)
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Run Luo, Haonan Zhang, Longze Chen, Ting-En Lin, Xiong Liu, Yuchuan Wu, Min Yang, Yongbin Li, Minzheng Wang, Pengpeng Zeng, Lianli Gao, Heng Tao Shen, Yunshui Li, Hamid Alinejad-Rokny, Xiaobo Xia, Jingkuan Song, Fei Huang
| Challenge: | a new framework for image-text instruction data evolution improves MLLM performance . lack of high-quality instruction data remains a major bottleneck in ML modeling . |
| Approach: | They propose a multimodal instruction data evolution framework that iteratively enhances data quality through fine-grained perception, cognitive reasoning, and interaction evolution. |
| Outcome: | The proposed approach improves MLLM performance in nine vision-language tasks while using significantly less data. |
From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decoding (2021.acl-long)
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Shan Wu, Bo Chen, Chunlei Xin, Xianpei Han, Le Sun, Weipeng Zhang, Jiansong Chen, Fan Yang, Xunliang Cai
| Challenge: | Experimental results show that Synchronous Semantic Decoding (SSD) can achieve state-of-the-art unsupervised semantic parsing performance on multiple datasets. |
| Approach: | They propose an unsupervised method which solves the semantic gap and the structure gap by leveraging paraphrasing and grammar-constrained decoding. |
| Outcome: | The proposed method can solve the semantic gap and structure gap on multiple datasets. |
Towards a Mechanistic Understanding of Large Reasoning Models: A Survey of Training, Inference, and Failures (2026.acl-long)
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Yi Hu, Jiaqi Gu, Ruxin Wang, Zijun Yao, Hao Peng, Xiaobao Wu, Jianhui Chen, Muhan Zhang, Liangming Pan
| Challenge: | Recent research has shown that reinforcement learning can elicit intriguing emergent reasoning behaviors. |
| Approach: | They propose a comprehensive survey of the mechanistic understanding of large reasoning models . they organize findings into three core dimensions: 1) training dynamics, 2) reasoning mechanisms, and 3) unintended behaviors. |
| Outcome: | This paper synthesizes the mechanistic understanding of large reasoning models into three dimensions . authors outline a roadmap for future studies including improved interpretability and methodologies . |
AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities (2023.findings-acl)
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| Challenge: | Existing methods to build a strong multilingual multimodal representation model are lacking in good-quality text-image pairs. |
| Approach: | They propose a method to build a strong multilingual multimodal representation model using English text-image pairs instead of a model from scratch. |
| Outcome: | The proposed model outperforms the original CLIP model on multilingual multimodal benchmarks. |
AnnaAgent: Dynamic Evolution Agent System with Multi-Session Memory for Realistic Seeker Simulation (2025.findings-acl)
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Ming Wang, Peidong Wang, Lin Wu, Xiaocui Yang, Daling Wang, Shi Feng, Yuxin Chen, Bixuan Wang, Yifei Zhang
| Challenge: | Existing models of seeker simulations are limited by the cost and ethical concerns of involving real seekers in mental health research. |
| Approach: | They propose an emotional and cognitive dynamic agent system equipped with tertiary memory to enable dynamic control of the simulator's configurations. |
| Outcome: | The proposed system achieves more realistic seeker simulation compared to baselines. |
CondenseFlow: Scalable Latent Space Collaboration via Semantic Compression for Multi-Agent Systems (2026.findings-acl)
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| Challenge: | Full-state latent communication in LLMs suffers from memory overhead scaling linearly with collaboration rounds. |
| Approach: | They propose a lightweight module that uses learnable semantic probes to compress KV caches into fixed-size representations. |
| Outcome: | The proposed module reduces KV cache memory by over 99% and inference latency by approximately 20% on seven benchmarks spanning six models . it outperforms text-based methods by 1.7 percentage points on average across all configurations while outperforming existing methods by 1.7%. |
EvoMD-LLM: Learning the Language of Species Evolution in Reactive Molecular Dynamics (2026.findings-acl)
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| Challenge: | Existing models operate on static molecular representations or rely on external tools for reasoning. |
| Approach: | They propose a framework that reformulates species-level molecular dynamics as a symbolic temporal language modeling problem. |
| Outcome: | The proposed model outperforms neural networks and language-based baselines on multiple temporal prediction tasks and generates plausible interpretations of reaction dynamics. |
Semantic-aware Contrastive Learning for More Accurate Semantic Parsing (2022.emnlp-main)
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| Challenge: | Existing studies on semantic parsing use Maximum Likelihood Estimation (MLE) to train discriminative semantic parses. |
| Approach: | They propose a semantic-aware contrastive learning algorithm which can learn to distinguish fine-grained meaning representations and take the overall sequence-level semantic into consideration. |
| Outcome: | The proposed algorithm improves on two standard datasets and gets state-of-the-art performance over existing methods. |
From Graph to Word Bag: Introducing Domain Knowledge to Confusing Charge Prediction (2024.lrec-main)
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| Challenge: | Existing charge prediction methods have shown impressive performance, but they face significant challenges when dealing with confusing charges, such as Snatch and Robbery. |
| Approach: | They propose a novel approach which introduces domain knowledge regarding constituent elements to guide the model in making judgments on confusing charges, much like a judge’s reasoning process. |
| Outcome: | The proposed approach maintains exceptional performance in imbalanced label distributions. |
PRISM: Probing Reasoning, Instruction, and Source Memory in LLM Hallucinations (2026.acl-long)
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Yuhe Wu, Guangyu Wang, Yuran Chen, Jiatong Zhang, Yutong Zhang, Yujie Chen, Jiaming Shang, Guang Zhang, Zhuang Liu
| Challenge: | Existing benchmarks for hallucination evaluation rely on mixed queries and posterior evaluation, which quantifies hallucinosity severity but offers limited insight into where and why they occur. |
| Approach: | They propose a controlled benchmark that disentangles hallucinations into four dimensions: knowledge missing, knowledge errors, reasoning errors, and instruction-following errors. |
| Outcome: | The proposed model disentangles hallucinations into four dimensions: knowledge missing, knowledge errors, reasoning errors, and instruction-following errors. |
PFDial: A Structured Dialogue Instruction Fine-tuning Method Based on UML Flowcharts (2025.findings-acl)
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Ming Zhang, Yuhui Wang, Yujiong Shen, Tingyi Yang, Changhao Jiang, Yilong Wu, Shihan Dou, Qinhao Chen, Zhiheng Xi, Zhihao Zhang, Yi Dong, Zhen Wang, Zhihui Fei, Mingyang Wan, Tao Liang, Guojun Ma, Qi Zhang, Tao Gui, Xuanjing Huang
| Challenge: | Large Language Models (LLMs) have shown remarkable progress in dialogue and reasoning, but they struggle to solve strictly constrained dialogue tasks. |
| Approach: | They construct a dataset that contains 12,705 high-quality Chinese dialogue instructions from 440 flowcharts containing 5,055 process nodes. |
| Outcome: | The proposed model outperforms GPT-4o models on backward transitions and outperformed GPT-42 models on the same dataset. |
KCAT: A Knowledge-Constraint Typing Annotation Tool (P19-3)
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Sheng Lin, Luye Zheng, Bo Chen, Siliang Tang, Zhigang Chen, Guoping Hu, Yueting Zhuang, Fei Wu, Xiang Ren
| Challenge: | Recent years Natural Language Processing community has seen a surge of interest in fine-grained entity typing (FET) given an entity mention (i.e. a sequence of token spans representing an entity), FET aims at uncovering its contextdependent type. |
| Approach: | They propose an efficient Knowledge Constraint Fine-grained Entity Typing Annotation Tool which further improves the entity typing process through entity linking together with some practical functions. |
| Outcome: | The proposed tool improves the entity typing process by linking the candidate types with some practical functions. |
Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network (P18-1)
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| Challenge: | Existing models for matching dialogue responses rely on semantic and functional dependencies . a recent study only uses the last utterance in context for matching a reply . |
| Approach: | They propose a model that matches a response with its multi-turn context using attention. |
| Outcome: | The proposed model outperforms the state-of-the-art models on two large-scale multi-turn response selection tasks. |
Mixture of Decoding: An Attention-Inspired Adaptive Decoding Strategy to Mitigate Hallucinations in Large Vision-Language Models (2025.findings-acl)
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| Challenge: | Large Vision-Language Models (LVLMs) have impressive capabilities across visual tasks, yet they remain hindered by the persistent challenge of hallucinations. |
| Approach: | They propose a novel approach that dynamically adapts decoding strategies by evaluating the correctness of the model’s attention on image tokens to distinguish the correct attention. |
| Outcome: | Extensive experiments show that the proposed approach outperforms existing decoding methods across multiple mainstream benchmarks, effectively mitigating hallucinations in LVLMs. |
BiKT: Enabling Bidirectional Knowledge Transfer Between Pretrained Models and Sequential Downstream Tasks (2024.findings-emnlp)
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| Challenge: | Existing frameworks adapt from initial pretrained model to each downstream task directly, but ignore sequential nature of downstream tasks and feedback effect on pretrained models. |
| Approach: | They propose a framework to enable bidirectional knowledge transfer between pretrained models and downstream tasks in rounds. |
| Outcome: | The proposed framework improves on 9 GLUE datasets and 6 SuperGLUEs. |
An Iterative Emotion Interaction Network for Emotion Recognition in Conversations (2020.coling-main)
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| Challenge: | Emotion recognition in conversations (ERC) is a task that aims to recognize the emotion of each utterance in conversations. |
| Approach: | They propose an iterative emotion interaction network which uses iterativly predicted emotion labels instead of gold emotion labels to explicitly model the emotion interaction. |
| Outcome: | The proposed method retains state-of-the-art performance on two datasets and achieves high accuracy. |
UnifEE: Unified Evidence Extraction for Fact Verification (2023.eacl-main)
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| Challenge: | Existing models extract evidence in both sentences and table cells from Wikipedia dumps, ignoring potential connections between them. |
| Approach: | They propose a model which uses a mixed evidence graph to extract the evidence in both formats without manually designed conversion rules. |
| Outcome: | The proposed model outperforms existing models and improves the verification step. |
FAITH: Factuality Alignment through Integrating Trustworthiness and Honestness (2026.findings-acl)
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| Challenge: | Existing approaches to correct factually inaccurate outputs are lacking the semantic richness needed to properly understand its internal states of trustworthiness and honesty. |
| Approach: | They propose a framework for factuality alignment that integrates natural-language uncertainty signals with external knowledge and computes confidence scores and semantic entropy from LLM outputs. |
| Outcome: | Extensive experiments on four knowledge-intensive benchmarks show that FAITH improves the factual accuracy and truthfulness of Large Language Models (LLMs). |
MES-RAG: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to RAG (2025.findings-naacl)
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| Challenge: | Existing approaches to retrieve entity information are limited by document level retrieval and intermingled storage of information from different entities. |
| Approach: | They propose a framework that enhances entity-specific query handling . MES-RAG introduces proactive security measures that ensure system integrity . |
| Outcome: | Experimental results show that MES-RAG improves accuracy and recall . the framework can be integrated into existing RAG architectures . |
PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation (2020.emnlp-main)
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| Challenge: | Existing techniques for natural language understanding and generation use autoencoding and/or autoregressive objectives to train models. |
| Approach: | They propose a self-supervised pre-training scheme that pre-trains an autoencoding and autoregressive language model on a large unlabeled corpus for generating new text conditioned on context. |
| Outcome: | The proposed scheme achieves state-of-the-art results on a variety of language generation benchmarks covering generative question answering, abstractive summarization and conversational response generation. |
The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation (P18-1)
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Mia Xu Chen, Orhan Firat, Ankur Bapna, Melvin Johnson, Wolfgang Macherey, George Foster, Llion Jones, Mike Schuster, Noam Shazeer, Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Zhifeng Chen, Yonghui Wu, Macduff Hughes
| Challenge: | In recent years, the emergence of seq2seq models has revolutionized the field of machine translation by replacing traditional phrase-based approaches with neural machine translation (NMT) systems based on the encoder-decoder paradigm. |
| Approach: | They propose to use a convolutional seq2seq model to combine the strengths of the two approaches. |
| Outcome: | The proposed architectures outperform the existing models on the WMT’14 benchmark dataset. |
LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey (2026.findings-acl)
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Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu, Jizhou Guo, Yankai Chen, Chunyu Miao, Hoang H Nguyen, Yue Zhou, Weizhi Zhang, Liancheng Fang, Hanrong Zhang, Fangxin Wang, Pengfei Zhang, Langzhou He, Yangning Li, Dongyuan Li, Renhe Jiang, Philip S. Yu
| Challenge: | Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents. |
| Approach: | They propose to integrate human-provided information, feedback, or control into the agent system to enhance system performance, reliability, and safety. |
| Outcome: | The proposed systems improve system performance, reliability, and safety by integrating human-provided information, feedback, or control into the agent system. |
MOBA-E2C: Generating MOBA Game Commentaries via Capturing Highlight Events from the Meta-Data (2022.findings-emnlp)
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| Challenge: | e-sports game competitions lack commentators because of the shortage of professional human commentators. |
| Approach: | They propose a data-driven MOBA commentary generation framework for MOBA games . they use a rule-based generator and a generative GPT generator to generate commentaries . |
| Outcome: | The proposed model generates commentaries based on the game meta-data and a rule-based generator and generative GPT generator. |
KELE: Residual Knowledge Erasure for Enhanced Multi-hop Reasoning in Knowledge Editing (2025.findings-emnlp)
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| Challenge: | Existing knowledge editing techniques show limitations when applied to multi-hop reasoning . residual single-hop knowledge causes edited models to revert to original answers . |
| Approach: | They propose a knowledge editing method that incorporates a Knowledge Erasure mechanism for Large language model Editing (KELE) they propose an erasure function for residual knowledge and an injection function for new knowledge . |
| Outcome: | The proposed method significantly improves multi-hop reasoning capability of edited models. |
DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models (2024.acl-long)
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Damai Dai, Chengqi Deng, Chenggang Zhao, R.x. Xu, Huazuo Gao, Deli Chen, Jiashi Li, Wangding Zeng, Xingkai Yu, Y. Wu, Zhenda Xie, Y.k. Li, Panpan Huang, Fuli Luo, Chong Ruan, Zhifang Sui, Wenfeng Liang
| Challenge: | Mixture-of-Experts (MoE) architectures face challenges in ensuring expert specialization . despite the promising performance, scaling language models to an extremely large scale is associated with exceedingly high computational costs. |
| Approach: | They propose an architecture that allows for ultimate expert specialization by segmenting experts into mN ones and activating mK from them. |
| Outcome: | The proposed architecture achieves comparable performance with GShard with 2B parameters and computation. |
ArchiDocGen: Multi-Agent Framework for Expository Document Generation in the Architectural Industry (2025.acl-industry)
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Junjie Jiang, Haodong Wu, Yongqi Zhang, Songyue Guo, Bingcen Liu, Caleb Chen Cao, Ruizhe Shao, Chao Guan, Peng Xu, Lei Chen
| Challenge: | drafting method statements is labor-intensive and time-consuming . traditional methods involve using static templates filled in manually by engineers . |
| Approach: | They propose a framework that automates method statement generation by using multi-agent collaboration. |
| Outcome: | The proposed framework achieves 4.38 ContentScore, excelling in specialization, completeness, organization, and clarity. |
HAG: Hierarchical Demographic Tree-based Agent Generation for Topic-Adaptive Simulation (2026.acl-long)
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| Challenge: | Existing approaches to Agent-Based Modeling fail to adapt to unseen topics absent from data. |
| Approach: | They propose a Hierarchical Agent Generation framework that formalizes population generation as a two-stage decision process. |
| Outcome: | The proposed framework outperforms baseline models in a multi-domain benchmark and comprehensive evaluation framework. |
Benchmarking Foundation Models with Retrieval-Augmented Generation in Olympic-Level Physics Problem Solving (2025.findings-emnlp)
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| Challenge: | a new study examines the potential of retrieval-augmented generation (RAG) with foundation models to enhance expert-level reasoning. |
| Approach: | They introduce PhoPile, a high-quality multimodal dataset specifically designed for Olympiad-level physics. |
| Outcome: | The proposed model can be used to solve Olympiad-level physics problems. |
Learning from Adjective-Noun Pairs: A Knowledge-enhanced Framework for Target-Oriented Multimodal Sentiment Classification (2022.coling-1)
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| Challenge: | Existing methods to determine sentiment polarity of opinion target are inconsistent and lack visual attention. |
| Approach: | They propose a framework which can exploit adjective-noun pairs extracted from images to improve visual attention and sentiment prediction capability of the TMSC task. |
| Outcome: | The proposed framework outperforms state-of-the-art on two public datasets. |
Incorporating External Knowledge into Machine Reading for Generative Question Answering (D19-1)
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| Challenge: | Existing knowledge-aware QA models do not have commonsense and background knowledge to answer nontrivial questions. |
| Approach: | They propose a new neural model which exploits external knowledge to generate answers in natural language for a given question with context. |
| Outcome: | The proposed model improves answer quality over existing models without knowledge and knowledge-aware models, a study shows . state officials in Hawaii confirmed that president Barack Obama was born in the U.S. |
Unleashing the Reasoning Potential of LLMs by Critique Fine-Tuning on One Problem (2025.emnlp-main)
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| Challenge: | Critique Fine-Tuning (CFT) is a promising paradigm for unlocking the reasoning capabilities of large language models. |
| Approach: | They propose a method that leverages critique data generated from a single math problem to improve reasoning accuracy. |
| Outcome: | The proposed method surpasses one-shot RLVR while requiring 15 to 20 times less compute. |
AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning (2025.emnlp-demos)
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Zhong Zhang, Yaxi Lu, Yikun Fu, Yupeng Huo, Shenzhi Yang, Yesai Wu, Han Si, Xin Cong, Haotian Chen, Yankai Lin, Xie Xie, Wei Zhou, Wang Xu, Zhou Su, Zhongwu Zhai, Xiaoming Liu, null Meiyudong, Jianming Xu, Hongyan Tian, Chongyi Wang, Chi Chen, Yuan Yao, Zhiyuan Liu, Maosong Sun
| Challenge: | Large language model agents have enabled GUI-based automation, but their deployment is limited by noisy data, poor generalization, and lack of support for non-English GUIs. |
| Approach: | They propose an 8B-parameter GUI agent built for robust and efficient on-device GUI interaction. |
| Outcome: | The proposed GUI agent achieves promising performance on five public benchmarks and proposed Chinese benchmark CAGUI. |
T2DR: A Two-Tier Deficiency-Resistant Framework for Incomplete Multimodal Learning (2025.findings-acl)
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| Challenge: | Existing incomplete multimodal learning frameworks are inadequate for integrating multimodal data. |
| Approach: | They propose a framework for incomplete multimodal learning that is deficiency-resistant and provides two modules to address fine-grained deficiencies. |
| Outcome: | The proposed framework outperforms the SOTA models on two well-known multimodal benchmarks. |
UniConv: Unifying Retrieval and Response Generation for Large Language Models in Conversations (2025.acl-long)
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Fengran Mo, Yifan Gao, Chuan Meng, Xin Liu, Zhuofeng Wu, Kelong Mao, Zhengyang Wang, Pei Chen, Zheng Li, Xian Li, Bing Yin, Meng Jiang
| Challenge: | Existing conversational search systems are usually built with two different models . this separation restricts the system from leveraging the model's intrinsic knowledge simultaneously . Existing studies for developing unified models cannot fully address the aspects of understanding conversational context, managing retrieval independently, and generating responses. |
| Approach: | They propose to unify dense retrieval and response generation for large language models in conversation by fine-tuning and mitigating data discrepancy. |
| Outcome: | The proposed model can outperform existing models on five conversational search datasets and reduce inconsistency risks while mitigating data discrepancy. |
TriSPrompt: A Hierarchical Soft Prompt Model for Multimodal Rumor Detection with Incomplete Modalities (2025.findings-emnlp)
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| Challenge: | Existing multimodal rumor detection methods focus on learning joint modality representations from complete multimodal training data, rendering them ineffective in addressing the common occurrence of missing modalities in real-world scenarios. |
| Approach: | They propose a hierarchical soft prompt model TriSPrompt which integrates three types of prompts to effectively detect rumors in incomplete multimodal data. |
| Outcome: | The proposed model achieves an accuracy gain of over 13% compared to state-of-the-art models. |
Listening Like Humans: Semantics-Guided Noise-Robust Multimodal Speech Recognition (2026.acl-long)
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| Challenge: | Severe acoustic degradation results in unreliable ASR outputs . et al., 2024b): critical concerns regarding reliability and fairness of ASR . |
| Approach: | They propose a multimodal framework that reframes ASR as semantics-guided speech reconstruction. |
| Outcome: | The proposed framework achieves an average reduction in WER while also attaining 98.71% BERTScore and 96.7% USE over advanced baselines. |
Learning Disentangled Semantic Representations for Zero-Shot Cross-Lingual Transfer in Multilingual Machine Reading Comprehension (2022.acl-long)
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| Challenge: | Existing methods to zero-shot transfer knowledge from rich-resource to low-resourced languages are limited due to linguistic discrepancies in different languages. |
| Approach: | They propose a multilingual MRC framework equipped with a Siamese Semantic Disentanglement Model to disassociate semantics from syntax in models learned by multilingual pre-trained models. |
| Outcome: | The proposed model disassociates semantics from syntax in multilingual models. |
AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)
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Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Xin Guo, Dingwen Yang, Chenyang Liao, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang
| Challenge: | Large language models (LLMs) are promising foundations to build generally-capable agents . however, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents. |
| Approach: | They propose a framework that features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
| Outcome: | The proposed framework features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
Mitigating Hallucinations of Large Language Models in Medical Information Extraction via Contrastive Decoding (2024.findings-emnlp)
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Derong Xu, Ziheng Zhang, Zhihong Zhu, Zhenxi Lin, Qidong Liu, Xian Wu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen
| Challenge: | Medical Information Extraction (MIE) tasks are a fundamental component of medical NLP. |
| Approach: | They propose an alternative adaptive constraint strategy to adjust the scale and scope of contrastive tokens. |
| Outcome: | The proposed approach selectively enhances the identification and classification capabilities while minimizing the influence of other inherent abilities in LLMs. |
Translation or Recitation? Calibrating Evaluation Scores for Machine Translation of Extremely Low-Resource Languages (2026.acl-short)
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| Challenge: | Existing studies show that performance across low-resource settings is variable, resulting in a significant barrier for the MT community. |
| Approach: | They propose to use FRED Difficulty Metrics to contextualize reported performance across different language pairs to determine whether breakthroughs reported in other contexts are artifacts of benchmark collection. |
| Outcome: | The proposed metrics explain a significant portion of result variability rather than model capability. |
Revisiting Interpolation Augmentation for Speech-to-Text Generation (2024.findings-acl)
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Chen Xu, Jie Wang, Xiaoqian Liu, Qian Dong, Chunliang Zhang, Tong Xiao, JingBo Zhu, Dapeng Man, Wu Yang
| Challenge: | Existing approaches to speech-to-text generation tasks are limited by the lack of extensive labeled datasets. |
| Approach: | They propose to use interpolation augmentation to construct virtual training samples by transforming inputs and labels to enhance generalization in other domains. |
| Outcome: | The proposed approach significantly improves performance across diverse tasks, architectures, and data scales, offering a promising avenue for more robust S2T systems in resource-constrained settings. |
DIDS: Domain Impact-aware Data Sampling for Large Language Model Training (2025.emnlp-main)
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Weijie Shi, Jipeng Zhang, Yaguang Wu, Jingzhi Fang, Shibo Zhang, Yao Zhao, Hao Chen, Ruiyuan Zhang, Yue Cui, Jia Zhu, Sirui Han, Jiajie Xu, Xiaofang Zhou
| Challenge: | Existing approaches for optimizing domain-level sampling strategies struggle with maintaining intra-domain consistency and accurately measuring domain impact. |
| Approach: | They propose to use a Fisher-Information Matrix-guided metric to measure domain impact to ensure intra-domain consistency and accuracy. |
| Outcome: | The proposed model achieves 3.4% higher average performance while maintaining comparable training efficiency. |
Cognitive-Level Adaptive Generation via Capability-Aware Retrieval and Style Adaptation (2025.findings-emnlp)
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| Challenge: | Large Language Models struggle to adapt content to users with differing cognitive capacities, leading to cognitive misalignment. |
| Approach: | They propose a cognitive-level alignment framework that aligns both knowledge complexity and presentation style with user cognition. |
| Outcome: | The proposed framework aligns knowledge complexity and presentation style with user cognition. |
From Selection to Generation: A Survey of LLM-based Active Learning (2025.acl-long)
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Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie, Junda Wu, Xintong Li, Ryan Aponte, Hanjia Lyu, Joe Barrow, Hongjie Chen, Franck Dernoncourt, Branislav Kveton, Tong Yu, Ruiyi Zhang, Jiuxiang Gu, Nesreen K. Ahmed, Yu Wang, Xiang Chen, Hanieh Deilamsalehy, Sungchul Kim, Zhengmian Hu, Yue Zhao, Nedim Lipka, Seunghyun Yoon, Ting-Hao Kenneth Huang, Zichao Wang, Puneet Mathur, Soumyabrata Pal, Koyel Mukherjee, Zhehao Zhang, Namyong Park, Thien Huu Nguyen, Jiebo Luo, Ryan A. Rossi, Julian McAuley
| Challenge: | Large Language Models (LLMs) have been used for selection and training of data for active learning. |
| Approach: | They propose an intuitive taxonomy that categorizes LLM-based active learning techniques and discuss the transformative roles they can play in the active learning loop. |
| Outcome: | The proposed model can generate entirely new data instances and provide more cost-effective annotations with fewer labeled data instances. |
MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions (2023.emnlp-main)
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| Challenge: | Existing methods for retraining from scratch are limited and only work on the recall of edited facts. |
| Approach: | They propose a benchmark method that allows users to ask multi-hop questions to assess whether edited models correctly answer questions where the answer should change as an entailed consequence of edited facts. |
| Outcome: | The proposed method outperforms existing models and scales well with LLMs (up to 175B) it is based on a memory-based approach that stores all edited facts externally while prompting the language model iteratively to generate answers consistent with the edited facts. |
DialMed: A Dataset for Dialogue-based Medication Recommendation (2022.coling-1)
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| Challenge: | Existing studies on medication recommendation mainly rely on EHRs, but some details of interactions between doctors and patients may be ignored or omitted in EHR. |
| Approach: | They propose to use medical dialogues to recommend medications with medical dialogue data . they propose to model dialogue structure and disease knowledge aware network . |
| Outcome: | The proposed method is a promising solution to recommend medications with medical dialogues. |
A Survey of RAG-Reasoning Systems in Large Language Models (2025.findings-emnlp)
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Yangning Li, Weizhi Zhang, Yuyao Yang, Wei-Chieh Huang, Yaozu Wu, Junyu Luo, Yuanchen Bei, Henry Peng Zou, Xiao Luo, Yusheng Zhao, Chunkit Chan, Yankai Chen, Zhongfen Deng, Yinghui Li, Hai-Tao Zheng, Dongyuan Li, Renhe Jiang, Ming Zhang, Yangqiu Song, Philip S. Yu
| Challenge: | a survey of RAG-based reasoning-based approaches shows that it is not effective for multi-step inferences. |
| Approach: | They map how advanced reasoning optimizes each stage of RAG . they show how retrieved knowledge supply missing premises and expand context for complex inference . |
| Outcome: | The proposed frameworks achieve state-of-the-art across knowledge-intensive benchmarks. |
Classic4Children: Adapting Chinese Literary Classics for Children with Large Language Model (2025.findings-naacl)
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| Challenge: | Recent large language models (LLMs) overlook children’s reading preferences, which poses challenges in CLA. |
| Approach: | They propose a method that augments large language models with children's reading preferences for adaptation by obtaining characters' personalities and narrative structure as additional information for fine-grained instruction tuning. |
| Outcome: | The proposed method significantly improves performance in automatic and human evaluation. |
EX-FEVER: A Dataset for Multi-hop Explainable Fact Verification (2024.findings-acl)
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| Challenge: | Existing studies on fact verification lack a high-quality dataset for explainability . existing systems lack evidence retrieval and veracity prediction, limiting the ability to verify a claim . |
| Approach: | They propose a dataset for multi-hop explainable fact verification that summarises and modifies Wikipedia documents. |
| Outcome: | The proposed dataset aims to improve the accuracy of multi-hop explainable fact verification systems. |
Derailer-Rerailer: Adaptive Verification for Efficient and Reliable Language Model Reasoning (2025.findings-acl)
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| Challenge: | Existing prompting methods struggle with complex tasks and reasoning stability, limiting their practical deployment. |
| Approach: | They propose a framework that adaptively balances reasoning accuracy and computational efficiency by employing a lightweight Derailer mechanism to assess reasoning stability and selectively triggers an advanced Rerailer verification process only when necessary. |
| Outcome: | The proposed framework achieves significant accuracy improvements (8-11%) while maintaining 2-3 times better efficiency than existing verification methods. |
ShopSimulator: Evaluating and Exploring RL-Driven LLM Agent for Shopping Assistants (2026.acl-long)
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Pei Wang, Yanan Wu, Xiaoshuai Song, Weixun Wang, Gengru Chen, Zhongwen Li, Kezhong Yan, Qi Liu, Ken Deng, Shuaibing Zhao, Shaopan Xiong, Xuepeng Liu, Xuefeng Chen, Wanxi Deng, Wenbo Su, Bo Zheng
| Challenge: | Existing studies on large language model-based agents focus on evaluation benchmarks without training support. |
| Approach: | They propose a large-scale Chinese shopping simulation environment that uses large language models to train agents. |
| Outcome: | The proposed model performs poorly in a large-scale and challenging shopping environment in China. |
Developing and Utilizing a Large-Scale Cantonese Dataset for Multi-Tasking in Large Language Models (2025.findings-emnlp)
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Jiyue Jiang, Alfred Kar Yin Truong, Yanyu Chen, Qinghang Bao, Sheng Wang, Pengan Chen, Jiuming Wang, Lingpeng Kong, Yu Li, Chuan Wu
| Challenge: | Cantonese is considered a low-resource language due to the dominance of Mandarin . rich colloquial vocabulary of Cantone, English loanwords, and code-switching characteristics add to the complexity of corpus collection and processing. |
| Approach: | We collect Cantonese texts from open source corpora, Hong Kong-specific forums, Wikipedia . we refine the model through supervised fine-tuning on curated Cantonesian tasks . |
| Outcome: | The model achieves state-of-the-art (SOTA) performance on four Cantonese benchmarks. |
Reliable Use of Lemmas via Eligibility Reasoning and Section-Aware Reinforcement Learning (2026.acl-short)
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| Challenge: | Recent large language models (LLMs) perform strongly on mathematical benchmarks but often import conclusions without validating assumptions. |
| Approach: | They propose a model that encodes a lemma specification and trains with reinforcement learning and section-aware loss masking to assign penalty to the section responsible for errors. |
| Outcome: | The proposed model performs well on benchmarks but often misapplyes lemmas . the model is able to encode the specification and train with reinforcement learning . |
SpecAgent: A Speculative Retrieval and Forecasting Agent for Code Completion (2026.acl-long)
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George Ma, Anurag Koul, Qi Chen, Yawen Wu, Sachit Kuhar, Yu Yu, Aritra Sengupta, Varun Kumar, Murali Krishna Ramanathan
| Challenge: | Large Language Models (LLMs) excel at code-related tasks but struggle in real software repositories. |
| Approach: | They propose a large-scale agent that injects repository context at inference time to improve both latency and code-generation quality by proactively exploring repository files during indexing and constructing speculative context. |
| Outcome: | Experiments show that SpecAgent achieves 9–11% relative performance gains compared to baselines while significantly reducing inference latency. |
A Holistic Approach to Reference-Free Evaluation of Machine Translation (2023.acl-short)
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| Challenge: | Traditional machine translation evaluation relies on reference written by humans . reference-free evaluation gets rid of labor-intensive annotations, which can pivot easily to new domains . |
| Approach: | They propose a reference-free evaluation approach that characterizes evaluation as two aspects: fluency and faithfulness. |
| Outcome: | The proposed approach outperforms SOTA reference-fee metrics on machine translation datasets. |
From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems (2025.findings-emnlp)
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Zekun Zhou, Xiaocheng Feng, Lei Huang, Xiachong Feng, Ziyun Song, Ruihan Chen, Liang Zhao, Weitao Ma, Yuxuan Gu, Baoxin Wang, Dayong Wu, Guoping Hu, Ting Liu, Bing Qin
| Challenge: | rapid development of artificial intelligence (AI) technologies has inspired researchers to explore how AI can accelerate and enhance research. |
| Approach: | They organize the relevant studies into three main categories: hypothesis formulation, hypothesis validation, and manuscript publication. |
| Outcome: | The authors summarize the current state of research in three main areas: hypothesis formulation, hypothesis validation, and manuscript publication. |
Structural Bias for Aspect Sentiment Triplet Extraction (2022.coling-1)
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| Challenge: | Existing structural bias adapters for aspect sentiment triplet extraction are under-confident . a large-scale dataset for ASTE shows the adapter is effective and efficient to a larger scale. |
| Approach: | They propose to use a structural adapter to integrate structural bias into pretrained language models . they propose to add a relative position structure in place of the syntactic dependency structure . |
| Outcome: | The proposed adapter achieves state-of-the-art performance over strong baselines, but with a light parameter demand and low latency. |
GenPT: Beyond Self-Report for Reliable LLM Psychometrics via Generative Projective Testing (2026.acl-long)
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Ming Wang, Shuang Wu, Bixuan Wang, Lu Lin, Yuxin Chen, Xiaocui Yang, Daling Wang, Shi Feng, Yifei Zhang, Yufan Sun
| Challenge: | Large language models (LLMs) inherit contamination from training corpora, directional bias under social-desirability framing, and limited responsiveness to context beyond the item text. |
| Approach: | They propose a paradigm that reformulates TAT, Rorschach, and SCT with newly generated stimuli and organises assessment as a three-stage pipeline. |
| Outcome: | The proposed paradigm reformulates TAT, Rorschach, and SCT with newly generated stimuli and organises assessment as a three-stage pipeline. |
Consistent Prototype Learning for Few-Shot Continual Relation Extraction (2023.acl-long)
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| Challenge: | Existing methods for few-shot continual relation extraction are overfitting memory samples, resulting in insufficient activation of old relations and limited ability to handle confusion of similar classes. |
| Approach: | They propose a few-shot continual relation extraction task that uses memory-enhanced modules to train a model on incrementally few-shot data to avoid forgetting old relations. |
| Outcome: | The proposed method outperforms existing methods on two commonly-used datasets. |
TrendSim: Simulating Trending Topics in Social Media Under Poisoning Attacks with LLM-based Multi-agent System (2025.findings-naacl)
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Zeyu Zhang, Jianxun Lian, Chen Ma, Yaning Qu, Ye Luo, Lei Wang, Rui Li, Xu Chen, Yankai Lin, Le Wu, Xing Xie, Ji-Rong Wen
| Challenge: | Trending topics bring in a new channel for poisoning attacks, resulting in negative impacts on society. |
| Approach: | They propose an LLM-based multi-agent system to simulate trending topics in social media . they propose a time-aware interaction mechanism, centralized message dissemination, and an interactive system . |
| Outcome: | The proposed system simulates trending topics under poisoning attacks on social media platforms. |
TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities (2023.acl-demo)
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Zhe Zhao, Yudong Li, Cheng Hou, Jing Zhao, Rong Tian, Weijie Liu, Yiren Chen, Ningyuan Sun, Haoyan Liu, Weiquan Mao, Han Guo, Weigang Gou, Taiqiang Wu, Tao Zhu, Wenhang Shi, Chen Chen, Shan Huang, Sihong Chen, Liqun Liu, Feifei Li, Xiaoshuai Chen, Xingwu Sun, Zhanhui Kang, Xiaoyong Du, Linlin Shen, Kimmo Yan
| Challenge: | Several pre-training models of different modalities are showing a rising trend of homogeneity in their model structures. |
| Approach: | They propose a toolkit that supports pre-training models of different modalities. |
| Outcome: | The proposed toolkit can match the performance of the original implementations on text, vision, and audio benchmarks. |
Act-Adaptive Margin: Dynamically Calibrating Reward Models for Subjective Ambiguity (2026.acl-long)
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Feiteng Fang, Dingwei Chen, Xiang Huang, Ting-En Lin, Yuchuan Wu, Xiong Liu, Jing Ye, Ziqiang Liu, Haonan Zhang, Liang Zhu, Hamid Alinejad-Rokny, Min Yang, Yongbin Li
| Challenge: | Existing approaches to reward modeling in reinforcement learning tasks are limited when dealing with ambiguous preferences. |
| Approach: | They propose to use AAM to dynamically calibrate preference margins using the Bradley-Terry model's internal parameter knowledge to improve reward modeling in subjective tasks. |
| Outcome: | The proposed approach improves reward modeling by dynamically calibrating preference margins using the model’s internal parameter knowledge. |
MTG: A Benchmark Suite for Multilingual Text Generation (2022.findings-naacl)
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| Challenge: | Using MTG, we train and evaluate multilingual text generation models using human-annotated data. |
| Approach: | They propose a multilingual multiway text generation dataset with 400k human-annotated data that includes four generation tasks across five languages. |
| Outcome: | The proposed dataset includes four generation tasks across five languages (English, German, French, Spanish and Chinese) it provides comprehensive evaluations with diverse generation scenarios. |
EFUF: Efficient Fine-Grained Unlearning Framework for Mitigating Hallucinations in Multimodal Large Language Models (2024.emnlp-main)
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| Challenge: | Existing methods to eliminate hallucinations require expensive human annotation . hallucination in multimodal large language models poses unique challenges for current research . |
| Approach: | They propose a fine-grained unlearning framework that performs gradient ascent to eliminate hallucinations without paired data. |
| Outcome: | The proposed method reduces hallucinations while preserving quality with modest computational overhead. |
RouteMoA: Dynamic Routing without Pre-Inference Boosts Efficient Mixture-of-Agents (2026.acl-long)
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Jize Wang, Han Wu, Zhiyuan You, Yiming Song, Yijun Wang, Zifei Shan, Yining Li, Songyang Zhang, Xinyi Le, Cailian Chen, Xinping Guan, Dacheng Tao
| Challenge: | Existing methods for mixing-of-agents (MoA) lack model selection criteria and struggle with large model pools. |
| Approach: | They propose a mixture-of-agents framework with dynamic routing that uses a lightweight scorer to perform initial screening and refines the model scores through self- and cross-assessment. |
| Outcome: | The proposed framework outperforms existing methods for large model pools and tasks . it reduces cost by 89.8% and latency by 63.6% in the large-scale model pool. |
M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis (2025.emnlp-main)
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ChengYan Wu, Bolei Ma, Yihong Liu, Zheyu Zhang, Ningyuan Deng, Yanshu Li, Baolan Chen, Yi Zhang, Yun Xue, Barbara Plank
| Challenge: | Existing studies focus on English-centric aspects of sentiment analysis, limiting scope for multilingual evaluation and research. |
| Approach: | They propose to use a multilingual dataset to analyze aspects with associated sentiment elements in text. |
| Outcome: | The proposed dataset is the most extensive multilingual parallel dataset for ABSA to date. |
XtraGPT: Context-Aware and Controllable Academic Paper Revision via Human-AI Collaboration (2026.acl-long)
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Nuo Chen, Andre Lin HuiKai, Jiaying Wu, Junyi Hou, Zining Zhang, Qian Wang, Xidong Wang, Bingsheng He
| Challenge: | Existing systems are designed for general-purpose scientific text generation and fail to support high-quality scientific writing beyond surface-level polishing. |
| Approach: | They propose a human-AI collaboration framework for academic paper revision based on criteria-guided intent alignment and context-aware modeling. |
| Outcome: | The proposed framework outperforms existing LLMs and rivals the quality of proprietary ones. |
Memp: Exploring Agent Procedural Memory (2026.findings-acl)
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Runnan Fang, Yuan Liang, Xiaobin Wang, Jialong Wu, Shuofei Qiao, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang
| Challenge: | Large Language Models (LLMs) based agents suffer from brittle procedural memory that is manually engineered or entangled in static parameters. |
| Approach: | They propose a procedural-memory repository that distills past agent trajectories into fine-grained, step-by-step instructions and higher-level, script-like abstractions. |
| Outcome: | The proposed repository can be used to improve agents' performance on travelplanner and Alfworld. |
Editing the Moving World: Model Editing for Video LLMs (2026.acl-long)
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Qian Zhang, Xinye Li, Xiaokai Wu, Junhao Xu, Zhanyue Qin, Qingbin Liu, Junxian Cai, Xi Chen, Bolin Zhang, Zhiying Tu, Dianhui Chu, Xiaoyan Yu, Dianbo Sui
| Challenge: | Existing models for knowledge editing focus on knowledge-level or static visual domains, overlooking dynamic semantics. |
| Approach: | They propose a benchmark for modeling large language models using six representative models . they analyze the strengths and limitations of existing models and identify new directions . |
| Outcome: | The proposed benchmark extends existing models from static modalities to dynamic video scenarios. |
Unraveling Babel: Exploring Multilingual Activation Patterns of LLMs and Their Applications (2024.emnlp-main)
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| Challenge: | Recent studies have focused on how large language models process multiple languages, but internal mechanisms of LLMs remain insufficiently explored. |
| Approach: | They propose to convert dense LLMs into fine-grained MoE architectures and analyze their activation patterns using expert activation frequency heatmaps. |
| Outcome: | The proposed method outperforms random expert pruning and exceeds models in some languages. |
LEMON: Reviving Stronger and Smaller LMs from Larger LMs with Linear Parameter Fusion (2024.acl-long)
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| Challenge: | Existing methods to train a stronger and smaller model with the help of large models are limited by the model size and performance. |
| Approach: | They propose to learn competent initial points for smaller models by fusing parameters from larger models and introduce controllable receptive fields to model prior parameter characteristics. |
| Outcome: | The proposed method outperforms baselines in terms of effectiveness and efficiency. |
HuatuoGPT, Towards Taming Language Model to Be a Doctor (2023.findings-emnlp)
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Hongbo Zhang, Junying Chen, Feng Jiang, Fei Yu, Zhihong Chen, Guiming Chen, Jianquan Li, Xiangbo Wu, Zhang Zhiyi, Qingying Xiao, Xiang Wan, Benyou Wang, Haizhou Li
| Challenge: | Experimental results show that the distilled language model outperforms its teacher model (ChatGPT) in most cases. |
| Approach: | They propose a Large Language Model (LLM) that leverages both distilled data from **ChatGPT** and real-world data from**doctors** in the supervised fine-tuning stage. |
| Outcome: | The proposed model outperforms the teacher model in most cases by using additional real-world data and RLMF to align the language model with the merits of both sources. |
SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation (2025.findings-acl)
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| Challenge: | Existing process annotation approaches are computationally expensive. |
| Approach: | They propose a compression-based approach that transforms reasoning steps into code and normalizes them through Abstract Syntax Tree. |
| Outcome: | The proposed method outperforms existing methods on Best-of-N strategy and ProcessBench. |
RAVEN++: Pinpointing Fine-Grained Violations in Advertisement Videos with Active Reinforcement Reasoning (2025.emnlp-industry)
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Deyi Ji, Yuekui Yang, Liqun Liu, Peng Shu, Haiyang Wu, Shaogang Tang, Xudong Chen, Shaoping Ma, Tianrun Chen, Lanyun Zhu
| Challenge: | Recent advances in large language models have improved the detection of non-compliant content, but critical gaps persist in fine-grained understanding, explainability, and generalization. |
| Approach: | They propose a framework that combines active reinforcement learning, fine-grained violation understanding and progressive multi-stage training. |
| Outcome: | The proposed framework outperforms general-purpose LLMs and specialized models in fine-grained violation understanding, explainability, and generalization. |
Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have shown remarkable performance on question-answering tasks due to their superior capabilities in natural language understanding and generation. |
| Approach: | They propose a structured taxonomy that categorizes the methodology of synthesizing LLMs and knowledge graphs for QA according to the categories of QA and the KG’s role when integrating with LLM. |
| Outcome: | The proposed taxonomy categorizes the methods according to the categories of QA and the KG’s role when integrating with LLMs. |
DuReader-Retrieval: A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine (2022.emnlp-main)
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| Challenge: | Existing datasets for non-English passage retrieval are lacking in quality and accuracy. |
| Approach: | They present a large-scale Chinese dataset for passage retrieval . they reduce false negatives by manually annotating results pooled from multiple retrievers . |
| Outcome: | The proposed dataset reduces false negatives in development and testing sets and removes similar training queries. |
ProLongVid: A Simple but Strong Baseline for Long-context Video Instruction Tuning (2025.emnlp-main)
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Rui Wang, Bohao Li, Xiyang Dai, Jianwei Yang, Yi-Ling Chen, Zhen Xing, Yifan Yang, Dongdong Chen, Xipeng Qiu, Zuxuan Wu, Yu-Gang Jiang
| Challenge: | Existing approaches to adapt image-focused models for video understanding have not been successful in analyzing long video sequences. |
| Approach: | They propose a video instruction dataset that outperforms existing video instruction data for fine-tuning MLLMs by incrementally increasing input context length. |
| Outcome: | The proposed model outperforms existing models on video benchmarks and outperformed proprietary models on VideoMME even with a compact 7B model. |
ProBench: Judging Multimodal Foundation Models on Open-ended Multi-domain Expert Tasks (2025.findings-acl)
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| Challenge: | Solving expert-level multimodal tasks requires strong user query understanding, domain-specific knowledge, and advanced reasoning abilities. |
| Approach: | They propose a benchmark of open-ended user queries encapsulating professional expertise and advanced reasoning. |
| Outcome: | The proposed benchmark is publicly accessible at TBC. |
LADM: Long-context Training Data Selection with Attention-based Dependency Measurement for LLMs (2025.acl-long)
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| Challenge: | Long-context modeling has drawn more attention in the area of Large Language Models (LLMs). |
| Approach: | They propose a Long-context data selection framework with Attention-based Dependency Measurement which can efficiently identify high-quality long-contrast data from a large-scale, multi-domain pre-training corpus. |
| Outcome: | The proposed framework significantly boosts the performance of LLMs on multiple long-context tasks with only 1B tokens for continual training. |
Anti-Length Shift: Dynamic Outlier Truncation for Training Efficient Reasoning Models (2026.acl-long)
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Wei Wu, Liyi Chen, Congxi Xiao, Tianfu Wang, Qimeng Wang, Chengqiang Lu, Yan Gao, null Yiwu, Yao Hu, Hui Xiong
| Challenge: | Existing efficient reasoning methods rely on explicit length penalties for excessive verbosity on simple queries. |
| Approach: | They propose a training-time intervention that selectively suppresses redundant tokens . they find length shift occurs when models generate unnecessary reasoning on trivial inputs - a phenomenon that is often unexplored . |
| Outcome: | The proposed method reduces inference token usage by 78% while increasing accuracy compared to the initial policy and surpasses state-of-the-art efficient reasoning methods. |
IPR: Intelligent Prompt Routing with User-Controlled Quality-Cost Trade-offs (2025.emnlp-industry)
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Aosong Feng, Balasubramaniam Srinivasan, Yun Zhou, Zhichao Xu, Kang Zhou, Sheng Guan, Yueyan Chen, Xian Wu, Ninad Kulkarni, Yi Zhang, Zhengyuan Shen, Dmitriy Bespalov, Soumya Smruti Mishra, Yifei Teng, Darren Yow-Bang Wang, Haibo Ding, Lin Lee Cheong
| Challenge: | Existing systems require users to manually select models or employ rigid routing rules that fail to capture the continuous spectrum of query complexity. |
| Approach: | They propose a quality-constrained intelligent prompt routing framework that automatically selects optimal models based on predicted response quality and user-specified tolerance levels. |
| Outcome: | The proposed framework achieves 43.9% cost reduction while maintaining quality parity with strongest model in the Claude family and processes requests with sub-150ms latency. |
AttnPO: Attention-Guided Process Supervision for Efficient Reasoning (2026.acl-long)
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Shuaiyi Nie, null Dingsiyu, Wenyuan Zhang, Linhao Yu, Tianmeng Yang, Yao Chen, Weichong Yin, Yu Sun, Hua Wu, Tingwen Liu
| Challenge: | Existing trajectory-level length penalties fail to effectively shorten reasoning length and degrade accuracy, as they treat all reasoning steps uniformly and lack fine-grained signals to distinguish redundancy from necessity. |
| Approach: | They propose a low-overhead process-supervised RL framework that leverages the model’s intrinsic attention signals for step-level credit assignment. |
| Outcome: | The proposed framework reduces reasoning length while improving performance across 9 benchmarks. |
MimicLM: Zero-Shot Voice Imitation through Autoregressive Modeling of Pseudo-Parallel Speech Corpora (2026.findings-acl)
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Tao Feng, Yuxiang Wang, Yuancheng Wang, Xueyao Zhang, Dekun Chen, Chaoren Wang, Xun Guan, Zhizheng Wu
| Challenge: | Existing approaches to voice imitation use complex model design and a quality ceiling when synthetic speech is used as training *sources*. |
| Approach: | They propose a model that uses synthetic speech as training *sources* while retaining real recordings as *targets*. |
| Outcome: | The proposed model outperforms existing methods in naturalness while maintaining competitive similarity scores across speaker identity, accent, and emotion dimensions. |
SWAM: Adaptive Sliding Window and Memory-Augmented Attention Model for Rumor Detection (2025.emnlp-main)
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| Challenge: | Existing methods for rumor detection on social media focus on static graphs, ignoring dynamic and incremental propagation . rumour detection on the social media platform is crucial to mitigating harmful effects of rumors. |
| Approach: | They propose a sliding window and memory-augmented attention model for rumor detection . they use a dynamic propagation graph and memory to capture the long-term dependency . |
| Outcome: | The proposed model is compared with the state-of-the-art models on two public datasets. |
InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Model (2025.findings-acl)
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Yuhang Zang, Xiaoyi Dong, Pan Zhang, Yuhang Cao, Ziyu Liu, Shengyuan Ding, Shenxi Wu, Yubo Ma, Haodong Duan, Wenwei Zhang, Kai Chen, Dahua Lin, Jiaqi Wang
| Challenge: | Despite the promising performance of Large Vision Language Models, they sometimes generate incorrect outputs. |
| Approach: | They propose a multi-modal reward model that aligns LVLMs with human preferences. |
| Outcome: | The proposed model achieves excellent results on the latest multi-modal reward model benchmark and shows competitive performance on text-only reward model. |
Medical Graph RAG: Evidence-based Medical Large Language Model via Graph Retrieval-Augmented Generation (2025.acl-long)
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Junde Wu, Jiayuan Zhu, Yunli Qi, Jingkun Chen, Min Xu, Filippo Menolascina, Yueming Jin, Vicente Grau
| Challenge: | GraphRAG framework is designed to enhance LLMs in generating evidence-based medical responses. |
| Approach: | They propose a graph-based Retrieval-augmented generation framework to enhance LLMs in generating evidence-based medical responses. |
| Outcome: | The proposed framework outperforms state-of-the-art models on 9 medical Q&A benchmarks, 2 health fact-checking datasets, and a long-form generation test set. |
CompileAgent: Automated Real-World Repo-Level Compilation with Tool-Integrated LLM-based Agent System (2025.acl-long)
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Li Hu, Guoqiang Chen, Xiuwei Shang, Shaoyin Cheng, Benlong Wu, LiGangyang LiGangyang, Xu Zhu, Weiming Zhang, Nenghai Yu
| Challenge: | CompileAgent is the first LLM-based agent framework dedicated to repo-level compilation. |
| Approach: | They propose a LLM-based agent framework dedicated to repo-level compilation. |
| Outcome: | The proposed method significantly improves compilation success rate, ranging from 10% to 71%. |
Detection of Multiple Mental Disorders from Social Media with Two-Stream Psychiatric Experts (2023.emnlp-main)
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| Challenge: | Existing mental disease detection methods are not backed by domain knowledge and thus fail to produce interpretable results. |
| Approach: | They propose a framework that can learn the shared clues of all diseases while also capturing the specificity of each single disease. |
| Outcome: | Experiments on the detection of 7 diseases show that the proposed model can boost detection performance by more than 10%, especially in relatively rare classes. |
PresentAgent: Multimodal Agent for Presentation Video Generation (2025.emnlp-demos)
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| Challenge: | Existing methods for generating static slides or text summaries are limited to producing narrated presentations. |
| Approach: | They propose a multimodal agent that transforms long-form documents into narrated presentations. |
| Outcome: | The present agent produces fully synchronized visual and spoken content that closely mimics human-style presentations. |
Aligning Paralinguistic Understanding and Generation in Speech LLMs via Multi-Task Reinforcement Learning (2026.eacl-industry)
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Minseok Kim, Jingxiang Chen, Seong-Gyun Leem, Yin Huang, Rashi Rungta, Zhicheng Ouyang, Haibin Wu, Surya Teja Appini, Ankur Bansal, Yang Bai, Yue Liu, Florian Metze, Ahmed A Aly, Anuj Kumar, Ariya Rastrow, Zhaojiang Lin
| Challenge: | Using paralinguistic cues is challenging for speech large language models, authors say . limited training data, annotation difficulty, and models exploiting lexical shortcuts are challenges . a recent study shows that modeling paralinguistic reasoning with multitask RL improves paralinguistics understanding . |
| Approach: | They propose multi-task reinforcement learning with chain-of-thought prompting that elicits explicit affective reasoning. |
| Outcome: | The proposed model improves paralinguistics understanding over baselines and strong proprietary models by 8-12% on Expresso, IEMOCAP, and RAVDESS. |
Certainty in Uncertainty: Reasoning over Uncertain Knowledge Graphs with Statistical Guarantees (2025.emnlp-main)
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Yuqicheng Zhu, Jingcheng Wu, Yizhen Wang, Hongkuan Zhou, Jiaoyan Chen, Evgeny Kharlamov, Steffen Staab
| Challenge: | Existing methods produce only point estimates, without quantifying predictive uncertainty—limiting their reliability in high-stakes applications where understanding confidence in predictions is crucial. |
| Approach: | They propose a framework that generates prediction intervals guaranteed to contain the true score with a user-specified level of confidence. |
| Outcome: | The proposed framework generates prediction intervals guaranteed to contain the true score with a user-specified level of confidence. |
Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators (2023.emnlp-main)
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| Challenge: | Large language models outperform information retrieval techniques for downstream knowledge-intensive tasks when being prompted to generate world knowledge. |
| Approach: | They propose a COmpreheNsive kNowledge Evaluation framework to evaluate generated knowledge from six important perspectives . they conduct extensive empirical analysis of generated knowledge on two widely studied knowledge-intensive tasks . |
| Outcome: | The proposed framework evaluates generated knowledge from six important perspectives on two knowledge-intensive tasks. |
CLOWER: A Pre-trained Language Model with Contrastive Learning over Word and Character Representations (2022.coling-1)
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Borun Chen, Hongyin Tang, Jiahao Bu, Kai Zhang, Jingang Wang, Qifan Wang, Hai-Tao Zheng, Wei Wu, Liqian Yu
| Challenge: | Pre-trained language models (PLMs) have achieved remarkable performance gains across numerous downstream tasks in natural language understanding. |
| Approach: | They propose a Chinese pre-trained language model that implicitly encodes words into characters . they propose 'contrastive learning over word' and 'character' representations to improve learning . |
| Outcome: | The proposed model can encode words into fine-grained representations without modification of production pipelines. |
JARVIS or Ultron? A Survey on the Safety and Security Threats of Computer-Using Agents (2026.acl-long)
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Ada Chen, Yongjiang Wu, Junyuan Zhang, Jingyu Xiao, Shu Yang, Jen-tse Huang, Kun Wang, Wenxuan Wang, Shuai Wang
| Challenge: | Recent advances in computer-using agents have created new safety and security risks . despite the impressive capabilities of CUAs, there are still significant security risks. |
| Approach: | They propose a systematization of knowledge on the safety and security threats of Computer-Using Agents. |
| Outcome: | The proposed framework provides a framework for assessing the safety and security risks of computer-using agents. |
Many Heads Are Better Than One: Improved Scientific Idea Generation by A LLM-Based Multi-Agent System (2025.acl-long)
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Haoyang Su, Renqi Chen, Shixiang Tang, Zhenfei Yin, Xinzhe Zheng, Jinzhe Li, Biqing Qi, Qi Wu, Hui Li, Wanli Ouyang, Philip Torr, Bowen Zhou, Nanqing Dong
| Challenge: | Recent AI methods have shown promise in tasks such as hypothesis generation and experimental design, but they fail to replicate the collaborative nature of real-world scientific practices. |
| Approach: | They propose a virtual scientific system that mimics the collaborative nature of scientific research by organizing a team of agents to generate, evaluate, and refine research ideas. |
| Outcome: | The proposed system outperforms the state-of-the-art method in producing new scientific ideas and offers valuable insights to guide future research. |
Improving Large Language Models Function Calling and Interpretability via Guided-Structured Templates (2025.emnlp-main)
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Hy Dang, Tianyi Liu, Zhuofeng Wu, Jingfeng Yang, Haoming Jiang, Tao Yang, Pei Chen, Zhengyang Wang, Helen Wang, Huasheng Li, Bing Yin, Meng Jiang
| Challenge: | Large language models (LLMs) have strong reasoning and tool-use capabilities, yet fail in real-world tool-interactions due to incorrect parameterization, poor tool selection, or misinterpretation of user intent. |
| Approach: | They propose a curriculum-inspired framework that leverages structured reasoning templates to guide LLMs through more deliberate step-by-step instructions for generating function calls. |
| Outcome: | The proposed framework reduces tool-use errors and improves interpretability and transparency of tool-using agents. |
OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking (2025.emnlp-main)
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Zekun Xi, Wenbiao Yin, Jizhan Fang, Jialong Wu, Runnan Fang, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang
| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated remarkable progress in machine writing such as open domain long-form generation. |
| Approach: | They propose a slow-thinking machine writing framework that emulates the human-like process of iterative expansion and reflection to improve the knowledge density of generated articles. |
| Outcome: | The proposed framework improves the knowledge density of generated articles without compromising metrics such as coherence and depth. |
Combining Curriculum Learning and Knowledge Distillation for Dialogue Generation (2021.findings-emnlp)
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| Challenge: | Existing studies have shown that curriculum learning facilitates dialogue generation tasks while knowledge distillation can yield significant performance boosts for student models. |
| Approach: | They propose a combination of curriculum learning and knowledge distillation for dialogue generation models . they cluster training cases according to their complexity and employ an adversarial training strategy . |
| Outcome: | The proposed model improves compared with baselines. |
PLAWBENCH: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice (2026.acl-long)
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Yuzhen Shi, Huanghai Liu, Yiran HU, Song Gaojie, Xu Xinran, Yubo Ma, Tianyi Tang, Li Zhang, Qingjing Chen, Feng Di, Wenbo Lv, Weiheng Wu, Kexin Yang, Sen Yang, Wei Wang, Rongyao Shi, Qiu Yuanyang, Yuemeng Qi, Zhang Jingwen, Sui Xiaoyu, Yifan Chen, Zhang Yi, An Yang, Bowen Yu, Dayiheng Liu, Junyang Lin, Weixing Shen, Bing Zhao, Charles L. A. Clarke, HU Wei
| Challenge: | Existing benchmarks for large language models (LLMs) are coarse, single-dimensional metrics and do not explicitly assess fine-grained legal reasoning. |
| Approach: | They propose a Practical Law Benchmark to evaluate large language models in real-world legal practice scenarios. |
| Outcome: | The proposed model is based on 850 questions and 13 scenarios with expert-designed evaluation rubrics. |
CINO: A Chinese Minority Pre-trained Language Model (2022.coling-1)
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| Challenge: | Existing multilingual pre-trained language models do not perform well on some low-resource languages. |
| Approach: | They propose a multilingual pre-trained language model for Chinese minority languages . they collect documents from Wikipedia and construct two classification datasets . |
| Outcome: | The proposed model outperforms baseline models on various classification tasks. |
MulDimIF: A Multi-Dimensional Constraint Framework for Evaluating and Improving Instruction Following in Large Language Models (2026.findings-acl)
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Junjie Ye, Caishuang Huang, Zhuohan Chen, Wenjie Fu, Chenyuan Yang, Leyi Yang, Yilong Wu, Peng Wang, Meng Zhou, Xiaolong Yang, Tao Gui, Qi Zhang, Zhongchao Shi, Jianping Fan, Xuanjing Huang
| Challenge: | Existing research has focused on constraint categories, offering little guidance for improving instruction following abilities. |
| Approach: | They propose a multi-dimensional constraint framework that allows for instruction following . they construct 9,106 code-verifiable samples and evaluate 18 LLMs . |
| Outcome: | The proposed framework improves instruction following performance without compromising general performance. |
STELLA: A Multimodal LLM for Protein Functional Annotation via Unified Sequence-Structure Encoding (2026.findings-acl)
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Hongwang Xiao, Wenjun Lin, Xi Chen, Hui Wang, Kai Chen, Jiashan Li, Yuancheng Sun, Sicheng Dai, Boya Wu, Qiwei Ye
| Challenge: | a multimodal protein language model (LLM) integrates sequence, structure, and function into functional annotation. |
| Approach: | They propose a multimodal protein language model that synergistically aligns bimodal representations with the textual modality to advance protein functional annotation. |
| Outcome: | The proposed model synergizes bimodal representations with the textual modality to advance protein functional annotation. |
Tandem: Riding Together with Large and Small Language Models for Efficient Reasoning (2026.findings-acl)
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Zichuan Fu, Xian Wu, Guojing Li, Yejing Wang, Yijun Chen, Zhao Zihao, Luo Yixuan, Hanyu Yan, Yefeng Zheng, Xiangyu Zhao
| Challenge: | Recent advances in large language models (LLMs) have catalyzed the rise of reasoningintensive inference paradigms, where models perform explicit step-by-step reasoning before generating final answers. |
| Approach: | They propose a large-small LLM collaboration framework that synergizes large and small language models to achieve high-quality reasoning with significantly reduced computational cost. |
| Outcome: | The proposed framework outperforms the mentor LLM while preserving the benefits of the thinking paradigm of LLMs. |
Training Deeper Neural Machine Translation Models with Transparent Attention (D18-1)
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| Challenge: | Existing NMT models are shallow in comparison to convolutional models used for both text and vision tasks. |
| Approach: | They propose to modify the attention mechanism to ease the optimization of deeper models by a simple modification to the seq2seq with attention paradigm. |
| Outcome: | The proposed model achieves consistent gains of 0.7-1.1 BLEU on the benchmark WMT’14 English-German and WMT'15 Czech-English tasks. |
Making Parameter-efficient Tuning More Efficient: A Unified Framework for Classification Tasks (2022.coling-1)
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Xin Zhou, Ruotian Ma, Yicheng Zou, Xuanting Chen, Tao Gui, Qi Zhang, Xuanjing Huang, Rui Xie, Wei Wu
| Challenge: | Large pre-trained language models (PLMs) have demonstrated superior performance in industrial applications. |
| Approach: | They propose a framework that re-uses existing parameter-efficient methods with a unified classifier. |
| Outcome: | The proposed framework improves the efficiency of existing parameter-efficient methods with a unified classifier. |
CausalEval: Towards Better Causal Reasoning in Language Models (2025.naacl-long)
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Longxuan Yu, Delin Chen, Siheng Xiong, Qingyang Wu, Dawei Li, Zhikai Chen, Xiaoze Liu, Liangming Pan
| Challenge: | Large language models (LLMs) have been used for a variety of tasks, including problem-solving, decision-making, and understanding of the world. |
| Approach: | They propose a review of existing methods aimed at enhancing LMs for causal reasoning . they categorize existing methods as reasoning engines or as helpers providing knowledge or data to traditional methods . |
| Outcome: | The proposed methods perform better than existing methods on a range of tasks. |
Beyond Hard Masks: Progressive Token Evolution for Diffusion Language Models (2026.acl-long)
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Linhao Zhong, Linyu Wu, Bozhen Fang, Tianjian Feng, Chenchen Jing, Wen Wang, Jiaheng Zhang, Hao Chen, Chunhua Shen
| Challenge: | Existing Diffusion Language Models rely on hard binary masking and discrete token assignments, which hinder the revision of early decisions. |
| Approach: | They propose a diffusion-based language modeling approach that replaces hard binary masks with evolving soft token distributions. |
| Outcome: | The proposed approach outperforms existing DLMs on multiple benchmarks. |
Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion (2025.acl-long)
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Jianqing Zhu, Huang Huang, Zhihang Lin, Juhao Liang, Zhengyang Tang, Khalid Almubarak, Mosen Alharthi, Bang An, Juncai He, Xiangbo Wu, Fei Yu, Junying Chen, Ma Zhuoheng, Yuhao Du, He Zhang, Saied Alshahrani, Emad A. Alghamdi, Lian Zhang, Ruoyu Sun, Haizhou Li, Benyou Wang, Jinchao Xu
| Challenge: | In the evolving landscape of large language models, the predominant focus has been on English and Chinese. |
| Approach: | They propose to utilize Arabic-specific vocabulary in the tokenizer to accelerate decoding. |
| Outcome: | The proposed model achieves decent performance comparable to the best Arabic LLMs across various Arabic benchmarks. |
Your Vision-Language Model Itself Is a Strong Filter: Towards High-Quality Instruction Tuning with Data Selection (2024.findings-acl)
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Ruibo Chen, Yihan Wu, Lichang Chen, Guodong Liu, Qi He, Tianyi Xiong, Chenxi Liu, Junfeng Guo, Heng Huang
| Challenge: | Existing data selection methods for instruction-following large language models rely on unreliable scores or use downstream tasks for selection. |
| Approach: | They propose a method that utilizes the VLM itself as a filter to select high-quality instruction-tuning data. |
| Outcome: | The proposed method can reach better results compared to full data settings with merely about 15% samples and can achieve superior performance against competitive baselines. |
GAM: Hierarchical Graph-based Agentic Memory for LLM Agents (2026.acl-long)
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Zhaofen Wu, Hanrong Zhang, Fulin Lin, Wujiang Xu, Xinran Xu, Yankai Chen, Henry Peng Zou, Shaowen Chen, Weizhi Zhang, Xue Liu, Philip S. Yu, Hongwei Wang
| Challenge: | Current unified stream-based memory systems facilitate context updates but remain vulnerable to interference from transient noise. |
| Approach: | They propose a hierarchical Graph-based Agentic Memory framework that explicitly decouples memory encoding from consolidation to resolve conflict between rapid context perception and stable knowledge retention. |
| Outcome: | The proposed framework outperforms state-of-the-art benchmarks on LoCoMo and LongDialQA. |
Sentiment Word Aware Multimodal Refinement for Multimodal Sentiment Analysis with ASR Errors (2022.findings-acl)
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| Challenge: | Existing models for multimodal sentiment analysis are limited in their capacity to be deployed in the real world. |
| Approach: | They propose a model that can dynamically refine erroneous sentiment words by leveraging multimodal sentiment clues. |
| Outcome: | The proposed model surpasses the state-of-the-art models on three datasets. |
Towards IP Intelligence: Benchmarking Large Language Models on Intellectual Property Knowledge and Practice (2026.findings-acl)
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Qiyao Wang, Guhong Chen, Hongbo Wang, Huaren Liu, Minghui Zhu, Zhifei Qin, Li Linwei, Yilin Yue, Shiqiang Wang, Jiayan Li, Wu Yihang, Ziqiang Liu, Longze Chen, Run Luo, Liyang Fan, Jiaming Li, Lei Zhang, Kan Xu, Hamid Alinejad-Rokny, Chengming Li, Shiwen Ni, Yuan Lin, Min Yang
| Challenge: | Existing datasets and benchmarks focus only on patents or cover limited aspects of the IP field, lacking alignment with real-world scenarios. |
| Approach: | They propose a bilingual IP task taxonomy and a large-scale bilingual benchmark to evaluate LLMs in real-world IP practice. |
| Outcome: | The proposed model achieves only 75.8% accuracy, indicating room for improvement . open-source IP and law-oriented models lag behind closed-source general-purpose models . |
Can LLMs Hear the Dogwhistle? (2026.findings-acl)
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Yifan Liu, Yi Lin, Xinwei Guo, Ziwei Wang, Jiaxin Zhang, Guanhua Chen, Haiyan Wu, Xiangyu Zhao, Xin Yao, Xuetao Wei
| Challenge: | Existing safety benchmarks focus on explicitly harmful content, but ignore context-dependent expressions such as dogwhistles. |
| Approach: | They propose a benchmark for evaluating LLM safety under dogwhistle-driven prompts . their findings expose a blind spot in current safety evaluation practices . |
| Outcome: | The proposed benchmark compared safety performance with toxic terms using dogwhistle-driven prompts. |
DECOR: Improving Coherence in L2 English Writing with a Novel Benchmark for Incoherence Detection, Reasoning, and Rewriting (2024.emnlp-main)
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| Challenge: | Existing automated writing evaluation systems only detect incoherence in writing . a recent study has found that incorporating specific reasons for incohence improves the quality of rewrites . |
| Approach: | They propose a benchmark that includes expert annotations for detecting incoherence in L2 English writing, identifying the underlying reasons, and rewriting the incoerent sentences. |
| Outcome: | The proposed benchmark improves coherence in L2 English writing by fine-tuning models . the authors find that incorporating specific reasons improves quality of rewrites . |
CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models (2025.findings-naacl)
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Shangda Wu, Yashan Wang, Ruibin Yuan, Guo Zhancheng, Xu Tan, Ge Zhang, Monan Zhou, Jing Chen, Xuefeng Mu, Yuejie Gao, Yuanliang Dong, Jiafeng Liu, Xiaobing Li, Feng Yu, Maosong Sun
| Challenge: | Current music information retrieval systems struggle to meet linguistic diversity challenges . current systems struggle with text queries in non-English languages . |
| Approach: | They propose a music information retrieval system that supports both ABC notation and MIDI . CLaMP 2 includes a multilingual text encoder and a multiple-modal music encoder . |
| Outcome: | The proposed system achieves state-of-the-art results in multilingual semantic search and music classification across modalities. |
ObfusLM: Privacy-preserving Language Model Service against Embedding Inversion Attacks (2025.acl-long)
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Yu Lin, Ruining Yang, Yunlong Mao, Qizhi Zhang, Jue Hong, Quanwei Cai, Ye Wu, Huiqi Liu, Zhiyu Chen, Bing Duan, Sheng Zhong
| Challenge: | Recent studies show that obfuscation techniques for MLaaS are susceptible to embedding inversion attacks (EIAs). |
| Approach: | They propose a model obfuscation framework that protects client inputs from embedding inversion attacks by obliviously obbing models. |
| Outcome: | The proposed framework outperforms existing works in utility by 10% with a nearly 80% resistance rate against embedding inversion attacks. |
An Effective Automated Speaking Assessment Approach to Mitigating Data Scarcity and Imbalanced Distribution (2024.findings-naacl)
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| Challenge: | Automated speaking assessment (ASA) typically involves automatic speech recognition (ASR) and hand-crafted feature extraction from the transcript of a learner’s speech. |
| Approach: | They propose to use metric-based classification and loss re-weighting to model the impact of different SSL-based embedding features on the CEFR score. |
| Outcome: | The proposed model outperforms baselines on the ICNALE benchmark dataset, achieving a significant improvement of more than 10% in CEFR prediction accuracy. |
Wider & Closer: Mixture of Short-channel Distillers for Zero-shot Cross-lingual Named Entity Recognition (2022.emnlp-main)
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| Challenge: | Existing mainstream methods for zero-shot cross-lingual named entity recognition ignore the rich and complementary information lying in the intermediate layers of pre-trained language models and domain-invariant information is easily lost during transfer. |
| Approach: | They propose a mixture of short-channel distillers to fully interact the rich hierarchical information in the teacher model and to transfer knowledge to the student model sufficiently and efficiently. |
| Outcome: | The proposed method shows great generalization and compatibility across languages and fields. |
Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise Reward (2025.findings-emnlp)
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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
| 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. |
Text2Tree: Aligning Text Representation to the Label Tree Hierarchy for Imbalanced Medical Classification (2023.findings-emnlp)
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| Challenge: | Existing approaches to medical text classification are struggling with imbalanced data distribution and rare labels. |
| Approach: | They propose a framework-agnostic algorithm that only utilizes internal label hierarchy in training deep learning models. |
| Outcome: | The proposed approach performs better on public datasets and real-world medical records than existing methods. |
Trident: Self-Supervised Preference Alignment via Triplet Regularization (2026.findings-acl)
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| Challenge: | Large vision-Language Models suffer from noisy supervision and semantic ambiguity in self-supervised settings. |
| Approach: | They propose a self-supervised framework that constructs reliable preference triplets . they propose 'trident' objective that enforces semantic separation between the triplet components . |
| Outcome: | The proposed framework outperforms state-of-the-art RLHF and RLAIF benchmarks on LLaVA-1.5-7B and achieves 95.70% precision on POPE using only 4k self-generated triplets and a single epoch. |
KnowVrDU: A Unified Knowledge-aware Prompt-Tuning Framework for Visually-rich Document Understanding (2024.lrec-main)
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Yunqi Zhang, Yubo Chen, Jingzhe Zhu, Jinyu Xu, Shuai Yang, Zhaoliang Wu, Liang Huang, Yongfeng Huang, Shuai Chen
| Challenge: | Existing methods for integrating layout and image features into pre-training language models are not suitable for few-shot settings. |
| Approach: | They propose to reformulate VrDU tasks into a single question-answering format with task-specific prompts and train the pre-trained model with the parameter-efficient prompt tuning method. |
| Outcome: | The proposed framework can be used in few-shot settings and reduces data requirements. |
None of the Above, Less of the Right Parallel Patterns in Human and LLM Performance on Multi-Choice Questions Answering (2025.findings-acl)
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| Challenge: | Multiple-choice exam questions with “None of the above” (NA) options have been extensively studied in educational testing . however, their impact on Large Language Models (LLMs) evaluation remains underexplored . |
| Approach: | They conduct systematic experiments with 28 LLMs on the MMLU benchmark to examine how NA options affect model performance and confidence calibration. |
| Outcome: | The results highlight important implications for benchmark design and raise questions about LLMs’ ability to handle uncertainty in real-world applications. |
When Efficiency Meets Safety: A Benchmark Security Analysis of KV Cache Compression in Large Language Models (2026.acl-long)
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| Challenge: | Key-Value (KV) caching is widely used in large language models to enable long-context inference efficiently, yet its security implications remain underexplored. |
| Approach: | They propose a history-aware, per-head feedback merging strategy that prevents safety degradation while maintaining efficiency. |
| Outcome: | The proposed strategy prevents safety degradation while maintaining efficiency. |
From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan (2026.acl-long)
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Lei Yang, Leiyu Pan, Bojian Xiong, Renren Jin, Shaowei Zhang, Yue Chen, Ling Shi, Jiang Zhou, Junru Wu, Zhen Wang, Jianxiang Peng, Juesi Xiao, Tianyu Dong, Zhuowen Han, Zhuo Chen, Yuqi Ren, Deyi Xiong
| Challenge: | Large language models have achieved remarkable success across a wide range of tasks, yet their performance remains heavily biased toward high-resource languages. |
| Approach: | They propose a pipeline for advancing Tibetan language modeling through multilingual continual pre-training with Tibetan, Chinese, and English. |
| Outcome: | The proposed model outperforms open-source and Tibetan-focused models on diverse tasks. |
Neural Mixed Counting Models for Dispersed Topic Discovery (2020.acl-main)
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| Challenge: | Existing methods for inference of parameter parameters are time-consuming and difficult to use. |
| Approach: | They propose two efficient neural mixed counting models that use the negative binomial distribution as the prior for dispersed topic discovery. |
| Outcome: | The proposed models outperform state-of-the-art models in terms of perplexity and topic coherence on real-world datasets. |
Efficient Self-Evaluation for Diffusion Language Models via Sequence Regeneration (2026.acl-long)
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| Challenge: | Non-sequential and bidirectional nature of diffusion large language models makes direct likelihood-based self-evaluation challenging. |
| Approach: | They propose a self-evaluation confidence quantification method for diffusion large language models that quantifies confidence by computing the probability of regenerating tokens in the entire generated sequence, given the full context. |
| Outcome: | The proposed method is correlated with semantic coherence and answer accuracy. |
Pre-training Language Model as a Multi-perspective Course Learner (2023.findings-acl)
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Beiduo Chen, Shaohan Huang, Zihan Zhang, Wu Guo, Zhenhua Ling, Haizhen Huang, Furu Wei, Weiwei Deng, Qi Zhang
| Challenge: | Experimental results show that our method significantly improves ELECTRA’s average performance by 2.8% and 3.2% absolute points respectively on GLUE and SQuAD 2.0 benchmarks. |
| Approach: | They propose a multi-perspective course learning method to fetch many degrees and visual angles for sample-efficient pre-training and to fully leverage the relationship between generator and discriminator. |
| Outcome: | The proposed method improves ELECTRA's performance on GLUE and SQuAD 2.0 benchmarks and overshadows recent advanced ELECL-style models under the same settings. |
Language Resource Efficient Learning for Captioning (2021.findings-emnlp)
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| Challenge: | XE loss and SC loss are both considered to be performance degradations for captioning tasks. |
| Approach: | They propose to generalize the single pairwise comparison in SC loss and use multiple generalized pairwise compares to reduce noise in baseline. |
| Outcome: | The proposed method outperforms state-of-the-art models on a video caption dataset using only half of the language resources. |
GraphEval36K: Benchmarking Coding and Reasoning Capabilities of Large Language Models on Graph Datasets (2025.findings-naacl)
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| Challenge: | Large language models (LLMs) have demonstrated significant capabilities in processing and understanding text data. |
| Approach: | They propose a structure-based instruction-based method to enhance LLM performance on complex graph tasks. |
| Outcome: | The proposed framework outperforms open-source models on graph problem-solving, but the gap is narrowing. |
Thinking Economically: A Hierarchical Framework for Adaptive-Complexity Reasoning in LLMs (2026.findings-acl)
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Yubo Gao, Haotian Wu, Hong Chen, Junquan Huang, Yibo Yan, Jungang Li, Zihao Dongfang, Sicheng Tao, PS Tan, Jie Zhang, Xuming Hu
| Challenge: | Existing efficiency methods for Chain-of-Thought (CoT) generate excessively long rationales without commensurate accuracy gains. |
| Approach: | They propose a training framework that operationalizes this principle through coarse-to-fine budgeting. |
| Outcome: | Experiments on GSM8K and MATH500 show that HAB surpasses standard CoT in accuracy and reduces token usage, achieving stronger performance-efficiency trade-off than baselines. |
Decoding Time Series with LLMs: A Multi-Agent Framework for Cross-Domain Annotation (2026.findings-eacl)
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Minhua Lin, Zhengzhang Chen, Yanchi Liu, Xujiang Zhao, Zongyu Wu, Junxiang Wang, Xiang Zhang, Suhang Wang, Haifeng Chen
| Challenge: | Time series data is ubiquitous across various domains, including manufacturing, finance, and healthcare. |
| Approach: | They propose a multi-agent system to generate general and domain-specific annotations for time series data. |
| Outcome: | The proposed system outperforms existing methods on synthetic and real-world datasets. |
Is Grokking Worthwhile? Functional Analysis and Transferability of Generalization Circuits in Transformers (2026.findings-acl)
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| Challenge: | Large Language Models struggle with the "curse of two-hop reasoning" in compositional tasks. |
| Approach: | They propose to form a "Generalization Circuit" during a prolonged "grokking" phase . they argue that grokkking is the process of integrating memorized atomic facts into an easy-acquire reasoning path. |
| Outcome: | The proposed model is superior to non-grokked models, but it requires a large computational cost . the study shows that grokking is not the sudden acquisition of a new reasoning paradigm . |
LoRA Meets Dropout under a Unified Framework (2024.findings-acl)
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| Challenge: | Parameter-efficientfinetuning (PEFT) has gained popularity as a lightweight approach for model customization. |
| Approach: | They propose a parameter-efficient dropout method that is overfitting-prone and parameter-freezed. |
| Outcome: | The proposed method is superior to existing methods and compares with transformer-specific methods. |
MELoRA: Mini-Ensemble Low-Rank Adapters for Parameter-Efficient Fine-Tuning (2024.acl-long)
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Pengjie Ren, Chengshun Shi, Shiguang Wu, Mengqi Zhang, Zhaochun Ren, Maarten Rijke, Zhumin Chen, Jiahuan Pei
| Challenge: | Large language models (LLMs) are the default paradigm for natural language processing (NLP) as the models’ scale and the diversity of tasks increase, fine-tuning becomes infeasible. |
| Approach: | They propose to freeze original pretrained weights and train a group of mini LoRAs with only a small number of parameters and reduce their rank by 8 times . |
| Outcome: | The proposed model uses fewer trainable parameters while maintaining a higher rank, thereby offering improved performance potential. |
From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction Tuning (2024.naacl-long)
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| Challenge: | Large Language Models (LLMs) have achieved remarkable success in aligning with user intentions. |
| Approach: | They develop local and global explanation methods and a feed-forward-based method for input-output attribution to investigate the impact of instruction tuning on user intentions. |
| Outcome: | The proposed method compares explanations from pre-trained and instruction-tuned models . it empowers LLMs to recognize the instruction parts of user prompts, it encourages response generation . |
Guardian-as-an-Advisor: Advancing Next-Generation Guardian Models for Trustworthy LLMs (2026.findings-acl)
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Yue Huang, Haomin Zhuang, Jiayi Ye, Han Bao, Yanbo Wang, Hang Hua, Siyuan Wu, Pin-Yu Chen, Xiangliang Zhang
| Challenge: | prevailing taxonomies neglect robustness and honesty, yielding safer-on-paper but less useful systems. |
| Approach: | They propose a soft-gating pipeline where a guardian predicts a binary risk label plus a concise explanation and prepends this advice to the original query for re-inference. |
| Outcome: | The proposed model maintains safety while reducing over-refusal. |
EvoHyper: Evolving Hypergraph Topologies for Unified Collaboration in Multi-Agent Communication (2026.findings-acl)
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Heng Zhang, Yihao Zhong, Lubin Gan, Zhihe Chen, Jiajun Wu, Yuling Shi, Xiaodong Gu, Hao Zhang, Haochen You, Jin Huang
| Challenge: | Existing methods for multi-agent collaboration use a fixed communication graph and manage collaboration structure and shared memory in separate modules. |
| Approach: | They propose a framework that uses an evolving hypergraph topology for multi-agent collaboration. |
| Outcome: | The proposed framework achieves 3.2% to 7.8% accuracy gains over state-of-the-art methods and efficient, reducing token consumption by up to 23.5%. |
Incorporating Instructional Prompts into a Unified Generative Framework for Joint Multiple Intent Detection and Slot Filling (2022.coling-1)
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| Challenge: | Existing approaches to multiple intent detection and slot filling focus on task-specific components to capture the relationships between intents and slots. |
| Approach: | They propose a Unified Generative framework that captures the relationships between intents and slots in an utterance and formulates the task as a question-answering problem. |
| Outcome: | The proposed framework surpasses baselines on full-data and multi-intent benchmarks on 5-shot and 10-shot scenarios. |
Deep Learning on Graphs for Natural Language Processing (2021.naacl-tutorials)
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| Challenge: | Graph Neural Networks (GNNs) are powerful tools for non-Euclidean data modeling and are used in many graph-related NLP tasks. |
| Approach: | This tutorial will cover applying deep learning on graph techniques to NLP using Graph Neural Networks (GNNs) Graph4NLP is the first library for researchers and practitioners for easy use of GNNs for various NLP tasks. |
| Outcome: | This tutorial will cover the latest developments in deep learning on graph techniques and their applications in various NLP tasks. |
MT-R1-Zero: Advancing LLM-based Machine Translation via R1-Zero-like Reinforcement Learning (2025.findings-emnlp)
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| Challenge: | Large-scale reinforcement learning (RL) methods have proven effective in enhancing the reasoning abilities of large language models. |
| Approach: | They propose an open-source adaptation of the R1-Zero RL framework for machine translation (MT) their code is available at https://github.com/fzp0424/MT-R1-zero. |
| Outcome: | The proposed framework surpasses towerinstruct-7B-v0.2 on the english-chinese benchmark by 1.26 points. |
OMIBench: Benchmarking Olympiad-Level Multi-Image Reasoning in Large Vision-Language Models (2026.acl-long)
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Qiguang Chen, Chengyu Luan, Jiajun Wu, Qiming Yu, Yi Yang, Yizhuo Li, Jingqi Tong, Xiachong Feng, Libo Qin, Wanxiang Che
| Challenge: | Existing multimodal reasoning benchmarks for large vision-language models emphasize single-image analysis and fail to exploit contextual information across multiple images. |
| Approach: | They propose a benchmark to evaluate Olympiad-level reasoning when evidence is distributed over multiple images. |
| Outcome: | The proposed model outperforms existing models on bi-image Olympiads and Gemini-3-Pro on multimodal Olympiad-level reasoning tasks. |
Inner Thinking Transformer: Leveraging Dynamic Depth Scaling to Foster Adaptive Internal Thinking (2025.acl-long)
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Yilong Chen, Junyuan Shang, Zhenyu Zhang, Yanxi Xie, Jiawei Sheng, Tingwen Liu, Shuohuan Wang, Yu Sun, Hua Wu, Haifeng Wang
| Challenge: | Large language models face inherent performance bottlenecks under parameter constraints . challenging tokens induce abrupt gradient spikes across layers, exposing stress points . |
| Approach: | They propose an inner thinking transformer that reimagines layer computations as implicit thinking steps. |
| Outcome: | Empirical results show that ITT outperforms Transformer/Loop variants in 11 benchmarks. |
OpenS2S: Advancing Fully Open-Source End-to-End Empathetic Large Speech Language Model (2025.emnlp-demos)
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Chen Wang, Tianyu Peng, Wen Yang, YiNan Bai, Guangfu Wang, Jun Lin, Lanpeng Jia, Lingxiang Wu, Jinqiao Wang, Chengqing Zong, Jiajun Zhang
| Challenge: | Empathetic speech models are increasingly closed off, leaving details about the architecture, data and development opaque to researchers. |
| Approach: | They propose an open-source empathetic speech-to-text model with a streaming interleaved decoding architecture and a data pipeline to enable end-to end training. |
| Outcome: | The proposed model is open-source and transparent, with no data or data required to build it. |
Thinking with Map: Reinforced Parallel Map-Augmented Agent for Geolocalization (2026.findings-acl)
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Yuxiang Ji, Yong Wang, Ziyu Ma, Yiming Hu, Hailang Huang, Xuecai Hu, Guanhua Chen, Liaoni Wu, Xiangxiang Chu
| Challenge: | Existing large vision-language model (LVLM) approaches overlook a common strategy used by humans — using maps. |
| Approach: | They propose a method to equip a vision-language model with the ability to think with maps and optimize it using agentic reinforcement learning and parallel test-time scaling. |
| Outcome: | The proposed method outperforms open- and closed-source models on most metrics. |
VideoQA-TA: Temporal-Aware Multi-Modal Video Question Answering (2025.coling-main)
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| Challenge: | Existing methods for video question answering align visual or textual features directly with large language models, limiting the deep semantic association between modalities and hindering a comprehensive understanding of interactions within spatial and temporal contexts. |
| Approach: | They propose a temporal-aware framework for multi-modal video question answering that aligns videos and questions at fine-grained levels. |
| Outcome: | The proposed framework improves reasoning ability and accuracy of videoQA by aligning videos and questions at fine-grained levels. |
TLM: Token-Level Masking for Transformers (2023.emnlp-main)
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| Challenge: | Structured dropout approaches have been investigated to regularize the multi-head attention mechanism in Transformers. |
| Approach: | They propose a new regularization scheme based on token-level rather than structure-level to reduce overfitting by manipulating the connections between tokens in the multi-head attention via masking. |
| Outcome: | The proposed regularization scheme outperforms attention dropout and DropHead on 18 datasets and can establish a new record on the data-to-text benchmark Rotowire (18.93 BLEU). |
Multi-Persona Thinking for Bias Mitigation in Large Language Models (2026.findings-acl)
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| Challenge: | Large Language Models exhibit social biases, which can lead to harmful stereotypes and unfair outcomes. |
| Approach: | They propose a simple inference-time framework that encourages reasoning from multiple perspectives. |
| Outcome: | The proposed framework reduces bias by encouraging reasoning from multiple perspectives. |
NACL: A General and Effective KV Cache Eviction Framework for LLM at Inference Time (2024.acl-long)
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Yilong Chen, Guoxia Wang, Junyuan Shang, Shiyao Cui, Zhenyu Zhang, Tingwen Liu, Shuohuan Wang, Yu Sun, Dianhai Yu, Hua Wu
| Challenge: | Large Language Models (LLMs) with extended context windows are expensive and infeasible on fixed memory hardware due to the surprisingly large memory consumption of KV Cache. |
| Approach: | They propose a general framework for long-context KV cache eviction that achieves more optimal and efficient evict in a single operation during the encoding phase. |
| Outcome: | The proposed framework improves performance on short- and long-text tasks by 80% and 76% respectively, reducing KV Cache by up to 5 with over 95% performance maintenance. |
Beyond Templates: Dynamic Adaptation of Reasoning Demonstrations via Feasibility-Aware Exploration (2026.findings-acl)
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| Challenge: | Existing reasoning datasets that are designed for powerful LLMs often lead to degraded performance when directly applied to weaker models. |
| Approach: | They propose a data adaptation framework that bridges the capability gap between expert reasoning trajectories and diverse SLMs by employing a selective imitation strategy guided by step-wise adaptability estimation via solution simulation. |
| Outcome: | The proposed framework improves generalization and data efficiency over static fine-tuning and can be applied to large models with limited model capacity. |
Akan Cinematic Emotions (ACE): A Multimodal Multi-party Dataset for Emotion Recognition in Movie Dialogues (2025.findings-acl)
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David Sasu, Zehui Wu, Ziwei Gong, Run Chen, Pengyuan Shi, Lin Ai, Julia Hirschberg, Natalie Schluter
| Challenge: | Akan Cinematic Emotions (AkaCE) is the first multimodal emotion dialogue dataset for an African language . it contains 385 emotion-labeled dialogues and 6162 utterances across audio, visual, and textual modalities, along with word-level prosodic prominence annotations. |
| Approach: | They propose to use AkaCE to analyze African cinematic emotions using word-level prosodic prominence annotations. |
| Outcome: | The Akan Cinematic Emotions (AkaCE) dataset addresses the significant lack of resources for low-resource languages in emotion recognition research. |
Analyzing Code Embeddings for Coding Clinical Narratives (2021.findings-acl)
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| Challenge: | Recent work on automated ICD coding learn mappings between low-dimensional representations of clinical text reports and codes. |
| Approach: | They propose novel neural networks for encoding medical codes based on textual, structural and statistical characteristics using a single deep learning baseline model. |
| Outcome: | The proposed methods improve the accuracy of medical codes based on their textual, structural and statistical characteristics. |
A Structure-Aware Argument Encoder for Literature Discourse Analysis (2022.coling-1)
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Yinzi Li, Wei Chen, Zhongyu Wei, Yujun Huang, Chujun Wang, Siyuan Wang, Qi Zhang, Xuanjing Huang, Libo Wu
| Challenge: | Existing research for argument representation learning treats tokens in sentences equally and ignores the implied structure information of argumentative context. |
| Approach: | They propose to separate tokens into two groups to capture structural information of arguments and to incorporate paragraph-level position information into the model. |
| Outcome: | The proposed model captures structural information of arguments and is able to identify arguments automatically. |
A Hierarchical Reinforced Sequence Operation Method for Unsupervised Text Style Transfer (P19-1)
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| Challenge: | Existing text style transfer methods face three challenges: 1) the transfer is weakly interpretable; 2) generated outputs struggle in content preservation; 3) the trade-off between content and style is intractable. |
| Approach: | They propose a hierarchical reinforced sequence operation method that proposes operation positions and alters the sentence. |
| Outcome: | The proposed method significantly outperforms existing methods on two text style transfer datasets. |
OPAL: Ontology-Aware Pretrained Language Model for End-to-End Task-Oriented Dialogue (2023.tacl-1)
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| Challenge: | Existing task-oriented dialogue systems lack ontology-aware pretraining methods for task-orientated dialogue. |
| Approach: | They propose an ontology-aware pretrained language model (OPAL) for end-to-end task-oriented dialogue (TOD) . they propose to pretrain on large-scale contextual text data to bridge the gap between the pretraining method and downstream tasks. |
| Outcome: | The proposed model achieves an exciting boost and obtains competitive performance even without any TOD data on CamRest676 and MultiWOZ benchmarks. |
DGLF: A Dual Graph-based Learning Framework for Multi-modal Sarcasm Detection (2024.emnlp-main)
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Zhihong Zhu, Kefan Shen, Zhaorun Chen, Yunyan Zhang, Yuyan Chen, Xiaoqi Jiao, Zhongwei Wan, Shaorong Xie, Wei Liu, Xian Wu, Yefeng Zheng
| Challenge: | Existing methods for multimodal sarcasm detection neglect high-order relationships and underestimate high-frequency messages. |
| Approach: | They propose a Dual Graph-based Learning Framework to capture inter-modal inconsistencies . they propose combining a hypergraph and a vanilla graph to achieve enhanced propagation . |
| Outcome: | The proposed model outperforms existing state-of-the-art methods on two benchmark datasets. |
Datamart-Agent: LLM-Driven Game-Theoretic Agent for Data Marketplace Modeling (2026.findings-acl)
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| Challenge: | Existing studies on data marketplaces model static equilibria and complete information, which limits their realism. |
| Approach: | They propose an LLM-driven game-theoretic agent that makes equilibrium-consistent decisions in analytically tractable data marketplaces with evolving and incomplete-information. |
| Outcome: | The proposed framework matches equilibrium-consistent decision execution in a static data marketplace with a dynamic game tree memory and mechanism-guided reflection without updating parameters. |
From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge (2025.emnlp-main)
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Dawei Li, Bohan Jiang, Liangjie Huang, Alimohammad Beigi, Chengshuai Zhao, Zhen Tan, Amrita Bhattacharjee, Yuxuan Jiang, Canyu Chen, Tianhao Wu, Kai Shu, Lu Cheng, Huan Liu
| Challenge: | Recent advances in Large Language Models (LLMs) inspire the "LLM-as-a-judge" paradigm . traditional methods of assessment and evaluation fail in dynamic and open-ended scenarios . |
| Approach: | They propose a paradigm where LLMs are leveraged to perform scoring, ranking, or selection for machine learning evaluation scenarios. |
| Outcome: | The proposed model-based judgment and evaluation paradigms are based on large language models and are compared to the current model-driven evaluation paradigm. |
Don’t Act Blindly: Robust GUI Automation via Action-Effect Verification and Self-Correction (2026.acl-long)
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Yuzhe Zhang, Xianwei Xue, Xingyong Wu, Mengke Chen, Chen Liu, Xinran He, Run Shao, Feiran Liu, Huanmin Xu, Qiutong Pan, Haiwei Wang
| Challenge: | Existing GUI agents assume deterministic environment responses, generating actions without verifying whether previous operations succeeded. |
| Approach: | They propose a GUI agent that explicitly models action outcomes and recovery under noisy environments. |
| Outcome: | The proposed agent reduces failure loops and improves recovery success in noisy environments while maintaining competitive standard task performance. |
Fortify the Shortest Stave in Attention: Enhancing Context Awareness of Large Language Models for Effective Tool Use (2024.acl-long)
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| Challenge: | In this paper, we demonstrate that an inherent waveform pattern in the attention allocation of large language models significantly affects their performance in tasks demanding a high degree of context awareness. |
| Approach: | They propose a method that compensates an attention trough with an attention peak by a process to enhance the model's awareness to various contextual positions. |
| Outcome: | The proposed method improves the performance of a 7B model on the largest tool-use benchmark, comparable to that of GPT-4. |
Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis (2024.lrec-main)
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| Challenge: | Gradient-based explanation methods are increasingly used to interpret neural models in natural language processing (NLP) however, in the context of Aspect-based Sentiment Analysis, only specific dimensions are pertinent. |
| Approach: | They propose a Gradient-based explanation framework that leverages an information bottleneck to refine word embeddings into a concise intrinsic dimension, maintaining essential features and omitting unrelated information. |
| Outcome: | The proposed framework improves both the models’ performance and explanations’ clarity by identifying sentiment-aware features. |
MorphoBench: A Benchmark with Difficulty Adaptive to Model Reasoning (2026.findings-acl)
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Xukai Wang, Xuanbo Liu, Mingrui Chen, Haitian Zhong, Xuanlin Yang, Bohan Zeng, Jinbo Hu, Hao Liang, Junbo Niu, Xuchen Li, Ruitao Wu, Ruichuan An, Yang Shi, Liu Liu, Qiang Liu, Zhouchen Lin, Xu-Yao Zhang, Wentao Zhang, Bin Dong
| Challenge: | Existing benchmarks designed to evaluate the reasoning capabilities of large models are limited in scope and lack flexibility to adapt difficulty according to evolving reasoning capacities of models. |
| Approach: | They propose a benchmark that incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models. |
| Outcome: | The proposed benchmark incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models. |
Improving Mongolian-Chinese Neural Machine Translation with Morphological Noise (P19-2)
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| Challenge: | Existing models for Mongolian-Chinese translation are based on recurrent, convolutional neural networks or completely eliminate recurrence connections. |
| Approach: | They propose a adversarial training model to alleviate the UNK problem in Mongolian-Chinese machine translation by adding a screener to the model to emphasize the added Mongolian morphological noise. |
| Outcome: | The proposed model reduces training time and improves accuracy in Mongolian-Chinese translation tasks. |
Query-Driven Multimodal GraphRAG: Dynamic Local Knowledge Graph Construction for Online Reasoning (2025.findings-acl)
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| Challenge: | Existing approaches to build knowledge graphs with LLMs are constrained by static knowledge bases and ineffective multimodal data integration. |
| Approach: | They propose a Query-Driven Multimodal GraphRAG framework that dynamically constructs local knowledge graphs tailored to query semantics. |
| Outcome: | The proposed framework outperforms unsupervised competitors in cross-modal understanding of complex queries. |
Word Mover’s Embedding: From Word2Vec to Document Embedding (D18-1)
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Lingfei Wu, Ian En-Hsu Yen, Kun Xu, Fangli Xu, Avinash Balakrishnan, Pin-Yu Chen, Pradeep Ravikumar, Michael J. Witbrock
| Challenge: | Recent work has demonstrated that Word Mover’s Distance (WMD) that aligns semantically similar words yields unprecedented KNN classification accuracy. |
| Approach: | They propose a Word Mover’s Distance (WMD) method that aligns semantically similar words to generate unsupervised sentences or documents embeddings. |
| Outcome: | The proposed method consistently outperforms state-of-the-art techniques on 9 benchmark text classification datasets and 22 textual similarity tasks. |
Beyond Detection: Evaluating Fallacy Awareness of LLMs in Interactive Scenarios (2026.acl-long)
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| Challenge: | Large Language Models fail to recognize fallacious reasoning in real-world interactions despite strong performance on static fallacy detection tasks. |
| Approach: | They propose a Chinese benchmark to assess fallacy awareness without explicit cues . they propose 'fate' evaluation framework that assesses fallacy without explicit . |
| Outcome: | The proposed framework assesses fallacy awareness without explicit cues, combining natural dialogue responses and reasoning-based decisions. |
ZARA: Improving Few-Shot Self-Rationalization for Small Language Models (2023.findings-emnlp)
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| Challenge: | Recent studies demonstrate great performance gain for self-rationalization by few-shot prompting LMs with rationale-augmented exemplars. |
| Approach: | They propose to leverage explanations for small LMs to improve few-shot self-rationalization by reducing the problem of plausibility judgement to natural language inference. |
| Outcome: | The proposed approach achieves SOTA performance on the FEB benchmark, for both the task accuracy and the explanation metric. |
R.R.: Unveiling LLM Training Privacy through Recollection and Ranking (2025.findings-acl)
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| Challenge: | Existing privacy attacks focus on membership inference or data extraction, but reconstructing specific personally identifiable information (PII) in training data remains challenging. |
| Approach: | They propose a two-step privacy stealing attack that enables attackers to reconstruct PII entities from scrubbed training data where the PI I entities have been masked. |
| Outcome: | The proposed attack can reconstruct PII entities from scrubbed training data where the PI I entities have been masked. |
Structure-aware Domain Knowledge Injection for Large Language Models (2025.acl-long)
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| Challenge: | Structure-aware Continual Pre-Training (SCPT) and Structure-Aware Supervised Fine-Tuning (SSFT) are two-stage strategies for knowledge injection and alignment that reduces the training corpus needs to 5% while achieving 100% of traditional knowledge injection performance. |
| Approach: | They propose a method to efficiently transform foundation Large Language Models into domain specialists by using two-stage strategies: Structure-aware Continual Pre-Training and Structure-Aware Supervised Fine-Tuning. |
| Outcome: | The proposed method significantly reduces the training corpus needs to a mere 5% while achieving 100% of traditional knowledge injection performance. |
Depression Detection in Clinical Interviews with LLM-Empowered Structural Element Graph (2024.naacl-long)
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| Challenge: | Existing methods for assessing depression only capture part of relevant elements . scarcity of participant data constrains interview modeling due to privacy concerns . |
| Approach: | They propose a structural element graph (SEGA) that transforms clinical interviews into an expertise-inspired directed acyclic graph for comprehensive modeling. |
| Outcome: | The proposed model outperforms baseline methods and powerful LLMs on two real-world clinical datasets. |
Self-ICL: Zero-Shot In-Context Learning with Self-Generated Demonstrations (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) have shown striking ability to adapt to target tasks with a few input-output demonstrations. |
| Approach: | They propose a framework which bootstraps LMs’ intrinsic capabilities to perform zero-shot ICL. |
| Outcome: | The proposed framework outperforms baselines on 23 BIG-Bench Hard tasks on average accuracy and head-to-head comparison. |
GOBench: Stage-Wise Diagnostics and the Visual Paradox in Multimodal Graph Optimization (2026.findings-acl)
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| Challenge: | Existing benchmarks fail to represent multimodal problem specifications, score outcomes only and cannot localize where failures occur along the modeling pipeline. |
| Approach: | They propose a Graph Optimization benchmark that aligns multiple modalities with solver-derived oracles and a diagnostic protocol that evaluates intermediate artifacts as well as end results. |
| Outcome: | Graph Optimization benchmark (GOBench) evaluates intermediate artifacts as well as end results . vision reliably increases inference cost, while reliability impact is regime-dependent . current benchmarks fail to represent multimodal problem specifications, fail to localize failures . |
Crowd Comparative Reasoning: Unlocking Comprehensive Evaluations for LLM-as-a-Judge (2025.acl-long)
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Qiyuan Zhang, Yufei Wang, Yuxin Jiang, Liangyou Li, Chuhan Wu, Yasheng Wang, Xin Jiang, Lifeng Shang, Ruiming Tang, Fuyuan Lyu, Chen Ma
| Challenge: | Existing methods rely on majority voting or criteria expansion to capture detailed and detailed details, often leading to incomplete outcomes. |
| Approach: | They propose a method which introduces additional crowd responses to compare with the candidate responses, thereby exposing deeper and more comprehensive details within the candidate answers. |
| Outcome: | Experiments show that the proposed method improves evaluation reliability and achieves an average gain of 6.7% across five benchmarks. |
ChatMusician: Understanding and Generating Music Intrinsically with LLM (2024.findings-acl)
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Ruibin Yuan, Hanfeng Lin, Yi Wang, Zeyue Tian, Shangda Wu, Tianhao Shen, Ge Zhang, Yuhang Wu, Cong Liu, Ziya Zhou, Liumeng Xue, Ziyang Ma, Qin Liu, Tianyu Zheng, Yizhi Li, Yinghao Ma, Yiming Liang, Xiaowei Chi, Ruibo Liu, Zili Wang, Chenghua Lin, Qifeng Liu, Tao Jiang, Wenhao Huang, Wenhu Chen, Jie Fu, Emmanouil Benetos, Gus Xia, Roger Dannenberg, Wei Xue, Shiyin Kang, Yike Guo
| Challenge: | Despite LLMs' impressive capabilities in musical knowledge, music reasoning remains an unsolved task. |
| Approach: | They propose an open-source large language model (LLM) that integrates intrinsic musical abilities into LLaMA2 and GPT-3.5. |
| Outcome: | The proposed model can understand and generate music with a pure text tokenizer without external multi-modal neural structures or tokenizers. |
SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales (2024.emnlp-main)
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| Challenge: | Existing approaches to elicit confidence from large language models are limited to binary or inaccurate group-level confidence estimates. |
| Approach: | They propose a training framework that teaches LLMs to express more fine-grained confidence estimates. |
| Outcome: | The proposed training framework reduces the confidence calibration error and maintains the performance of the model. |
VaseVQA: Multimodal Agent and Benchmark for Ancient Greek Pottery (2026.findings-eacl)
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Jinchao Ge, Tengfei Cheng, Biao Wu, Zeyu Zhang, Shiya Huang, Judith Bishop, Gillian Shepherd, Meng Fang, Ling Chen, Yang Zhao
| Challenge: | MLLMs that use domain-specific data are limited in understanding cultural heritage artifacts such as ancient Greek pottery . supervised fine-tuning improves adaptation to domain knowledge, but it struggles with deeper reasoning tasks. |
| Approach: | They propose a visual question-answer tool that augments SFT with reinforcement learning using verifiable rewards. |
| Outcome: | The proposed model outperforms baseline models on reasoning-intensive questions on ancient Greek pottery. |
Huatuo-26M, a Large-scale Chinese Medical QA Dataset (2025.findings-naacl)
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Xidong Wang, Jianquan Li, Shunian Chen, Yuxuan Zhu, Xiangbo Wu, Zhiyi Zhang, Xiaolong Xu, Junying Chen, Jie Fu, Xiang Wan, Anningzhe Gao, Benyou Wang
| Challenge: | Large Language Models are a powerful tool for medical research, but the data is a bottleneck. |
| Approach: | They propose to use the largest ever medical Question Answering dataset with 26 Million QA pairs as a fine-tuning data for training large language models. |
| Outcome: | The proposed dataset demonstrates that it can be used to train large language models and improves zero-shot performance on other datasets. |
FinReporting: An Agentic Workflow for Localized Reporting of Cross-Jurisdiction Financial Disclosure (2026.acl-demo)
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Fan Zhang, Mingzi Song, Rania Elbadry, Yankai Chen, Shaobo Wang, Yixi Zhou, Xunwen Zheng, Yueru He, Yuyang Dai, Georgi Nenkov Georgiev, Ayesha Gull, Muhammad Usman Safder, Fan Wu, Liyuan Meng, Fengxian Ji, Junning Zhao, Xueqing Peng, Jimin Huang, YU Chen, Xue Liu, Preslav Nakov, Zhuohan Xie
| Challenge: | FinReporting is an agentic workflow for localized cross-jurisdiction financial reporting . existing approaches assume a single-market setting and overlook structural differences across jurisdictions . |
| Approach: | They propose a workflow that decomposes financial reporting into auditable stages . they use Large Language Models to extract and summarize corporate disclosures . |
| Outcome: | The proposed system decomposes reporting into auditable stages . it improves consistency and reliability under heterogeneous reporting regimes. |
DCE-LLM: Dead Code Elimination with Large Language Models (2025.naacl-long)
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| Challenge: | Dead code can obscure logical errors and be exploited for obfuscation in malware. |
| Approach: | They propose a framework for automated dead code elimination using a codeBERT model with an attribution-based line selector. |
| Outcome: | Experimental results show that DCE-LLM outperforms existing tools for dead code elimination . dead code can obscure logical errors and be exploited for obfuscation in malware . |
On the Encoder-Decoder Incompatibility in Variational Text Modeling and Beyond (2020.acl-main)
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| Challenge: | Existing work has shown that the optimization of variational autoencoders suffers from the posterior collapse problem. |
| Approach: | They propose a variational autoencoder that couples a VAE model with a deterministic autoencoding model and improves the parameters via weight sharing and decoder signal matching. |
| Outcome: | The proposed model improves on benchmark datasets and improves diversity of dialogue generation. |
Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases (N19-1)
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| Challenge: | Existing methods for knowledge base question answering ignore subtle inter-relationships between the question and the KB. |
| Approach: | They propose to model the two-way flow of interactions between questions and KBs using a bidirectional attentive memory network. |
| Outcome: | The proposed method outperforms existing methods on the WebQuestions benchmark and offers better interpretability compared to baselines. |
Bootstrapping Code Translation with Weighted Multilanguage Exploration (2026.acl-long)
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| Challenge: | Existing methods to improve code translation depend on abundant parallel code of high quality, which may not always be available. |
| Approach: | They propose a method that leverages functional invariance and cross-lingual portability of test suites to serve as universal verification oracles for multilingual reinforcement learning. |
| Outcome: | The proposed method leverages functional invariance and cross-lingual portability of test suites to serve as universal verification oracles for multilingual reinforcement learning (RL) training. |
Generative Frame Sampler for Long Video Understanding (2025.findings-acl)
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| Challenge: | Existing video large language models (LMMs) employ an impedance of thousands of frames to understand long videos. |
| Approach: | They propose a plug-and-play module integrated with VideoLLMs to facilitate efficient lengthy video perception. |
| Outcome: | The proposed module boosts the performance of open-source VideoLLMs and proprietary assistants on long-form video benchmarks. |
Decoupled Dialogue Modeling and Semantic Parsing for Multi-Turn Text-to-SQL (2021.findings-acl)
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| Challenge: | Recent work on Text-to-SQL for multi-turn dialogue has attracted great interest . current approaches mostly employ end-to end models and face data sparsity problems . |
| Approach: | They propose a decoupled multi-turn text-to-SQL framework where dialogue context is explicitly solved by an utterance rewrite model and a single-turn Text-toSQl parser are proposed. |
| Outcome: | The proposed method outperforms existing models on SParC and CoSQL datasets without annotated in-domain data. |
AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora (2026.acl-long)
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Jiaxin Bai, Wei Fan, Qi Hu, Qing Zong, Chunyang Li, Hong Ting Tsang, Hongyu Luo, Yauwai Yim, Haoyu Huang, Xiao Zhou, Feng Qin, Tianshi Zheng, Xi Peng, Xin Yao, Huiwen Yang, Leijie Wu, JI Yi, Gong Zhang, Renhai Chen, Yangqiu Song
| Challenge: | Existing knowledge graph construction frameworks require predefined schemas, limiting their scalability and domain coverage. |
| Approach: | They propose a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas. |
| Outcome: | The proposed framework outperforms state-of-the-art models on multi-hop QA tasks and enhances LLM factuality. |
SynGhost: Invisible and Universal Task-agnostic Backdoor Attack via Syntactic Transfer (2025.findings-naacl)
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| Challenge: | Existing attacks are classified into end-to-end and pre-training types based on the attack phase . Existing backdoor attacks are based upon perplexity, fine-pruning, and maxEntropy. |
| Approach: | They propose an entropy-based poisoning filter that mitigates backdoor attacks . they propose an invisible and universal task-agnostic backdoor attack via syntactic transfer . |
| Outcome: | The proposed attack can transfer backdoors to various downstream tasks while preserving pre-trained language models' pre-training capabilities. |
ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models (2023.emnlp-demo)
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Chenliang Li, He Chen, Ming Yan, Weizhou Shen, Haiyang Xu, Zhikai Wu, Zhicheng Zhang, Wenmeng Zhou, Yingda Chen, Chen Cheng, Hongzhu Shi, Ji Zhang, Fei Huang, Jingren Zhou
| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities to comprehend human intentions, engage in reasoning, and design planning-like behavior. |
| Approach: | They propose a framework that equips large language models with tool-use capabilities . they propose LLaMA and Chat-GLM as controllers, and a model-based agent framework . |
| Outcome: | The proposed framework equips open-source LLMs with tool-use capabilities . it provides a user-friendly system library with a customizable engine design . |
SoMeLVLM: A Large Vision Language Model for Social Media Processing (2024.findings-acl)
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Xinnong Zhang, Haoyu Kuang, Xinyi Mou, Hanjia Lyu, Kun Wu, Siming Chen, Jiebo Luo, Xuanjing Huang, Zhongyu Wei
| Challenge: | Genereal domain large models lack nuanced multimodal understanding of social media . general domain models focus more on text than other modalities, which is not consistent with real-world user habits. |
| Approach: | They propose a Large Vision Language Model for Social Media Processing that combines five key capabilities to understand and generate real social media behavior. |
| Outcome: | The proposed model achieves state-of-the-art performance in multiple social media tasks. |
Ambiguity-aware Multi-level Incongruity Fusion Network for Multi-Modal Sarcasm Detection (2025.coling-main)
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| Challenge: | Existing methods for sarcasm detection focus on fusing text and image information to establish cross-modal correlations, overlooking the significance of original unimodal incongruity information. |
| Approach: | They propose a multi-modal incongruity learning module to capture inconcluity information simultaneously at the text-level, image-level and cross-modal-level. |
| Outcome: | The proposed model outperforms state-of-the-art methods on a publicly available dataset. |
NAST: A Non-Autoregressive Generator with Word Alignment for Unsupervised Text Style Transfer (2021.findings-acl)
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| Challenge: | Autoregressive text style transfer models often ignore part of the source sentence and generate some irrelevant words with strong styles. |
| Approach: | They propose a non-autoregressive generator for unsupervised text style transfer which explicitly models word alignments to suppress irrelevant words. |
| Outcome: | The proposed generator significantly improves performance and provides explainable word alignments. |
PhiloGPT: A Philology-Oriented Large Language Model for Ancient Chinese Manuscripts with Dunhuang as Case Study (2024.emnlp-main)
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Yuqing Zhang, Baoyi He, Yihan Chen, Hangqi Li, Han Yue, Shengyu Zhang, Huaiyong Dou, Junchi Yan, Zemin Liu, Yongquan Zhang, Fei Wu
| Challenge: | philology requires years of professional training in extensive knowledge memorization and manual textual retrieval. |
| Approach: | They curated the PhiloCorpus-ZH, a rich collec-tion of ancient Chinese texts spanning a millennium with 30 diverse topics, including firsthand folk copies. |
| Outcome: | The PhiloCorpus-ZH corpus facilitated the development of the first LLM tailored for discovering ancient Chinese manuscripts. |
D2R: Dual-Branch Dynamic Routing Network for Multimodal Sentiment Detection (2024.emnlp-main)
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| Challenge: | Existing methods for multimodal sentiment detection use the same fixed framework to classify the sentiment polarity of image-text pairs. |
| Approach: | They propose a multimodal dynamic interaction model that uses a fixed framework to classify the sentiment polarity of a given imagetext pair. |
| Outcome: | The proposed model outperforms state-of-the-art models on three publicly available datasets. |
Measuring Your ASTE Models in The Wild: A Diversified Multi-domain Dataset For Aspect Sentiment Triplet Extraction (2023.findings-acl)
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| Challenge: | Existing ASTE datasets are limited in their ability to represent real-world scenarios, hindering progress in this area. |
| Approach: | They propose a new ASTE dataset that is manually annotated to better fit real-world scenarios by providing more diverse and realistic reviews. |
| Outcome: | The proposed dataset is manually annotated to better fit real-world scenarios. |
Mitigating Structural Knowledge Collapse in Domain-Specific LLMs via Morpheme-Aware KV-Aggregation (2026.acl-long)
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| Challenge: | Existing tokenizers over-fragment domain terms, disrupting morpheme semantics. |
| Approach: | They propose a lightweight tokenizer that dynamically consolidates fragments without tokenizer changes. |
| Outcome: | The proposed adapter outperforms vocabulary adaptation baselines on medical and legal terms by 3.2–4.6% and 7.9% on high-fragmentation terms. |
VIRT: Improving Representation-based Text Matching via Virtual Interaction (2022.emnlp-main)
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| Challenge: | Experimental results show that representation-based text matching methods suffer from performance degradation due to the lack of interactions between the pair of texts. |
| Approach: | They propose a virtual interaction mechanism that enables deep interaction between texts . they propose 'inteRacTion mechanism' that can be integrated into existing methods as plugins . |
| Outcome: | The proposed method outperforms state-of-the-art models on six text matching benchmarks. |
ForceReader: a BERT-based Interactive Machine Reading Comprehension Model with Attention Separation (2020.coling-main)
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| Challenge: | Various BERT-based reading comprehension models have been proposed, however, these models employ the combined input method without further modification for reading comprehension. |
| Approach: | They propose a BERT-based interactive machine reading comprehension model that uses BERT's combined input method without further modification for reading comprehension. |
| Outcome: | The proposed model improves reading comprehension tasks compared to BERT-based models. |
Pre3: Enabling Deterministic Pushdown Automata for Faster Structured LLM Generation (2025.acl-long)
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Junyi Chen, Shihao Bai, Zaijun Wang, Siyu Wu, Chuheng Du, Hailong Yang, Ruihao Gong, Shengzhong Liu, Fan Wu, Guihai Chen
| Challenge: | Existing methods for structured generation of outputs are inefficient under large inference batches. |
| Approach: | They propose a new LLM-based method that parses LR(1) grammars into a pushdown automaton and exploits deterministic pushdown automation to optimize the constrained LLM decoding efficiency. |
| Outcome: | The proposed method improves time per output token (TPOT) by 40% and throughput by 36% . |
M3AV: A Multimodal, Multigenre, and Multipurpose Audio-Visual Academic Lecture Dataset (2024.acl-long)
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Zhe Chen, Heyang Liu, Wenyi Yu, Guangzhi Sun, Hongcheng Liu, Ji Wu, Chao Zhang, Yu Wang, Yanfeng Wang
| Challenge: | Publishing open-source academic video recordings is an emerging approach to sharing knowledge online. |
| Approach: | They propose a multimodal, multigenre, and multipurpose audio-visual academic lecture dataset with human annotations for multimodal content recognition and understanding tasks. |
| Outcome: | The proposed dataset can be used for multiple audio-visual recognition and understanding tasks. |
Zero-Shot Text Classification via Self-Supervised Tuning (2023.findings-acl)
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| Challenge: | Existing solutions to zero-shot text classification use pre-trained language models or large-scale annotated data. |
| Approach: | They propose a self-supervised learning paradigm to solve zero-shot text classification tasks by tuning the language models with unlabeled data. |
| Outcome: | The proposed model outperforms the state-of-the-art models on 7 out of 10 tasks and is less sensitive to prompt design. |
RECAL: Sample-Relation Guided Confidence Calibration over Tabular Data (2023.findings-emnlp)
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| Challenge: | Various machine learning methods for tabular data lack accurate confidence estimation, which is needed for high-risk sensitive applications such as credit modeling and financial fraud detection. |
| Approach: | They propose a general post-training confidence calibration framework to calibrate the confidence of current machine learning models by employing graph neural networks to model the relationships between different samples. |
| Outcome: | The proposed framework improves the confidence estimation on tabular datasets by using graph neural networks to model the relationships between different samples. |
Reasoning Aware Self-Consistency: Leveraging Reasoning Paths for Efficient LLM Sampling (2025.naacl-long)
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| Challenge: | Large Language Models (LLMs) generate reasoning paths before answers, but lack a systematic approach to determine optimal number of samples or select the most faithful rationale. |
| Approach: | They propose a framework that evaluates the quality of reasoning and consistency of answers for each generated sample and uses criteria-based stopping and weighted majority voting to guide early stopping decisions and rationale selection. |
| Outcome: | The proposed framework outperforms existing methods while maintaining accuracy. |
GTA: Generating Long-horizon Tasks for Web Agents at Scale (2026.acl-long)
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Tenghao Huang, Kung-Hsiang Huang, Prafulla Kumar Choubey, Yilun Zhou, Muhao Chen, Jonathan May, Chien-Sheng Wu
| Challenge: | Existing benchmarks provide only coarse start–goal annotations without intermediate trajectories . Existing frameworks provide no supervision over the agent's latent decision process . |
| Approach: | They propose a framework that integrates crawling, retrieval-based seeding, in-context generation and automated quality control to produce realistic tasks paired with executable trajectories. |
| Outcome: | The proposed framework decouples crawling from generation for greater efficiency and ensures dense supervision through deterministic replays and systematic validation. |
Guiding Variational Response Generator to Exploit Persona (2020.acl-main)
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Bowen Wu, MengYuan Li, Zongsheng Wang, Yifu Chen, Derek F. Wong, Qihang Feng, Junhong Huang, Baoxun Wang
| Challenge: | Neural Response Generators (NRGs) use persona information of users to perform personalized conversations . current studies focus on incorporating explicit meta-data of user profiles or character descriptions to generate persona-aware responses. |
| Approach: | They propose to use persona information of users in Neural Response Generators to perform personalized conversations. |
| Outcome: | The proposed method improves persona-aware response generation and the metrics are reasonable to evaluate them. |
KG-RAG: Enhancing GUI Agent Decision-Making via Knowledge Graph-Driven Retrieval-Augmented Generation (2025.emnlp-main)
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Ziyi Guan, Jason Chun Lok Li, Zhijian Hou, Pingping Zhang, Donglai Xu, Yuzhi Zhao, Mengyang Wu, Jinpeng Chen, Thanh-Toan Nguyen, Pengfei Xian, Wenao Ma, Shengchao Qin, Graziano Chesi, Ngai Wong
| Challenge: | Recent advances in GUI agents have limited app-specific knowledge of complex mobile tasks. |
| Approach: | They propose a Knowledge Graph-driven Retrieval-Augmented Generation framework that transforms fragmented UTGs into structured vector databases for efficient real-time retrieval. |
| Outcome: | The proposed framework outperforms existing methods in a 75.8% success rate and 84.6% decision accuracy test across mobile apps. |
On the Impact of Cross-Domain Data on German Language Models (2023.findings-emnlp)
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Amin Dada, Aokun Chen, Cheng Peng, Kaleb Smith, Ahmad Idrissi-Yaghir, Constantin Seibold, Jianning Li, Lars Heiliger, Christoph Friedrich, Daniel Truhn, Jan Egger, Jiang Bian, Jens Kleesiek, Yonghui Wu
| Challenge: | Traditionally, large language models have been trained on general web crawls or domain-specific data. |
| Approach: | They present a German dataset and a dataset aimed at containing high-quality data to examine the importance of data diversity over quality. |
| Outcome: | The proposed model outperforms models trained on quality data on multiple downstream tasks. |
Smart-Searcher: Incentivizing the Dynamic Knowledge Acquisition of LLMs via Reinforcement Learning (2025.findings-emnlp)
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Huatong Song, Jinhao Jiang, Wenqing Tian, Zhipeng Chen, Yuhuan Wu, Jiahao Zhao, Yingqian Min, Xin Zhao, Lei Fang, Ji-Rong Wen
| Challenge: | Large Language Models (LLMs) are powerful but prone to hallucinations due to static knowledge. Retrieval-augmented generation (RAG) helps by injecting external information, but current methods are costly, generalize poorly, or ignore the model’s internal knowledge. |
| Approach: | They propose a framework to train large language models to leverage both internal and external knowledge sources. |
| Outcome: | The proposed framework outperforms existing methods and achieves efficient retrieval-augmented reasoning. |
XPrompt: Exploring the Extreme of Prompt Tuning (2022.emnlp-main)
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| Challenge: | Prompt tuning learns soft prompts to condition pre-trained Language Models for performing downstream tasks in a parameter-efficient manner. |
| Approach: | They propose a Prompt tuning model with an eXtremely small scale that learns soft prompts to condition the frozen Pre-trained Language Models for performing downstream tasks in a parameter-efficient manner. |
| Outcome: | The proposed model outperforms the vanilla Prompt-Tuning and can significantly improve across tasks and model scales. |
Large Language Models Meet NL2Code: A Survey (2023.acl-long)
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Daoguang Zan, Bei Chen, Fengji Zhang, Dianjie Lu, Bingchao Wu, Bei Guan, Wang Yongji, Jian-Guang Lou
| Challenge: | generating code from a natural language description is a pressing and significant challenge in code intelligence. |
| Approach: | They propose to survey 27 existing large language models for NL2Code and compare them to humanEval benchmarks. |
| Outcome: | The proposed model is compared with existing models on the HumanEval benchmark. |
CARER: Contextualized Affect Representations for Emotion Recognition (D18-1)
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| Challenge: | Existing methods to model emotion-relevant content are based on rule-based and statistics-based approaches. |
| Approach: | They propose a semi-supervised graph-based algorithm to produce rich structural descriptors . they use word embeddings to evaluate the algorithm on emotion recognition tasks . |
| Outcome: | The proposed method outperforms state-of-the-art methods on emotion recognition tasks. |
LiveLongBench: Tackling Long-Context Understanding for Spoken Texts from Live Streams (2026.findings-acl)
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| Challenge: | Existing studies show that spoken text exhibits unique linguistic properties, such as high redundancy and repetitive phrases. |
| Approach: | They propose a long-text dataset that better handles redundancy in spoken text . their results highlight key limitations of current methods and suggest future directions . |
| Outcome: | The proposed benchmark improves existing methods and improves on redundancy in spoken text. |
Improving Copy-oriented Text Generation via EDU Copy Mechanism (2024.lrec-main)
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| Challenge: | Existing extractive models generate texts through word-by-word decoding, causing factual inconsistencies and slow inference. |
| Approach: | They propose a framework that integrates the behavior of copying EDUs into generative models. |
| Outcome: | The proposed framework reduces the number of generated tokens significantly. |
V-GameGym: Visual Game Generation for Code Large Language Models (2026.findings-acl)
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Wei Zhang, Jian Yang, Renshuai Tao, Linzheng Chai, Shuyue Guo, Jiajun Wu, Xiaoming Chen, Ganqu Cui, Ning Ding, Xander Xu, HU Wei, Bowen Zhou
| Challenge: | Existing code-related benchmarks focus on single modality rather than visual game development. |
| Approach: | They propose a multimodal benchmark for evaluating code large language models in visual game generation that integrates a clustering-based curation methodology and a pipeline for visual code synthesis. |
| Outcome: | The proposed framework assesses code generation and visual game generation using a sandbox environment. |
C-ReD: A Comprehensive Chinese Benchmark for AI-Generated Text Detection Derived from Real-World Prompts (2026.findings-acl)
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| Challenge: | Recent efforts to develop algorithms for large language models (LLMs) have limited model diversity and data homogeneity in the Chinese corpora. |
| Approach: | They propose a Chinese Real-prompt AI-generated text Detection benchmark that can be generalized to unseen LLMs and external Chinese datasets. |
| Outcome: | The proposed benchmarks address critical gaps in model diversity, domain coverage, and prompt realism that have limited prior Chinese detection benchmarks. |
Multi-Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question Answering (P18-1)
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| Challenge: | Existing approaches to read comprehension style question answering are limited by the volume of annotated datasets. |
| Approach: | They propose a hierarchical attention network for reading comprehension style question answering . they first encode the question and paragraph with fine-grained language embeddings . then propose fusion approach to fuse information from both global and attended representations based on the hierarchic attention network . |
| Outcome: | The proposed method achieves state-of-the-art on the SQuAD and TriviaQA Wiki leaderboards and two adversarial SQu AD datasets. |
Multimodal Chemical Structure-Text Coreference in Intellectual Property via Rule-guided Reinforcement Learning (2026.findings-acl)
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| Challenge: | Existing tools for identifying chemical structures and textual referents are inadequate for this multimodal task. |
| Approach: | They propose a RULE-guided multimodal Reinforcement learning framework for chemical structure-text coreference . RULER is a rule-driven reinforcement learning framework that uses rule-based reward functions to obtain the correct domain knowledge. |
| Outcome: | The proposed framework improves on the baseline framework and shows superior efficacy. |
Mirror: A Universal Framework for Various Information Extraction Tasks (2023.emnlp-main)
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Tong Zhu, Junfei Ren, Zijian Yu, Mengsong Wu, Guoliang Zhang, Xiaoye Qu, Wenliang Chen, Zhefeng Wang, Baoxing Huai, Min Zhang
| Challenge: | Recent studies often formulate IE tasks as a triplet extraction problem, but this paradigm does not support multi-span and n-ary extraction, leading to weak versatility. |
| Approach: | They propose a multi-span cyclic graph extraction problem and a non-autoregressive graph decoding algorithm to extract all spans in a single step. |
| Outcome: | The proposed model outperforms or reaches competitive performance with SOTA systems under few-shot and zero-shot settings and it is compatible with 57 datasets. |
FGDGNN: Fine-Grained Dynamic Graph Neural Network for Rumor Detection on Social Media (2025.findings-acl)
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| Challenge: | Existing methods for detecting rumors on social media focus on coarse-grained temporal information and ignore fine-grain temporal dynamics. |
| Approach: | They propose a fine-grained dynamic graph neural network model which incorporates fine-grain temporal information into a unified framework for rumor detection. |
| Outcome: | The proposed model improves on three public real-world datasets. |
Can Intelligent Agents Revolutionize Scale Generation? (2026.findings-acl)
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| Challenge: | Existing measurement scales require extensive manual labor and require extensive validation and validation. |
| Approach: | They propose a multi-agent framework that automates scale development by leveraging collaborative AI agents. |
| Outcome: | The proposed framework automates scale development while maintaining rigorous quality standards. |
Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation (2025.acl-industry)
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Zixuan Wang, Jinghao Shi, Hanzhong Liang, Xiang Shen, Vera Wen, Zhiqian Chen, Yifan Wu, Zhixin Zhang, Hongyu Xiong
| Challenge: | Effective content moderation is essential for video platforms to safeguard user experience and uphold community standards. |
| Approach: | They propose a method to transform a generative MLLM into a multimodal classifier using minimal discriminative training data. |
| Outcome: | The proposed method improves F1 score by 66.50% over traditional classifiers while requiring only 2% of the fine-tuning data. |
ClaimGen-CN: A Large-scale Chinese Dataset for Legal Claim Generation (2025.findings-emnlp)
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| Challenge: | Currently, legal claims are not being used by non-professionals. |
| Approach: | They construct a dataset for Chinese legal claim generation task and then use it to evaluate the generated claims. |
| Outcome: | The proposed dataset is the first for the Chinese legal claim generation task and will be made publicly available. |
Large Language Model Agents in Finance: A Survey Bridging Research, Practice, and Real-World Deployment (2025.findings-emnlp)
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Yifei Dong, Fengyi Wu, Kunlin Zhang, Yilong Dai, Sanjian Zhang, Wanghao Ye, Sihan Chen, Zhi-Qi Cheng
| Challenge: | a systematic review of large language models (LLMs) is conducted to better align their capabilities with real-world demands. |
| Approach: | They propose a functional taxonomy mapping financial domains to tasks, datasets, and institutional constraints. they catalog over 30 financial benchmarks and 20 representative models. |
| Outcome: | The proposed model frameworks are bridging financial practice and LLM research. |
TokenSelect: Efficient Long-Context Inference and Length Extrapolation for LLMs via Dynamic Token-Level KV Cache Selection (2025.emnlp-main)
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| Challenge: | Rapid advances in Large Language Models have spurred demand for processing extended context sequences . however, performance degradation due to sequence lengths out-of-distribution and excessively long inference times are limiting LLMs in long-context scenarios. |
| Approach: | They propose a training-free method for efficient and accurate long-context inference . they selectively involves a few critical KV cache tokens in attention calculation . |
| Outcome: | The proposed method speeds up attention computation and accelerates inference time while reducing selection overhead. |
Jailbreaking? One Step Is Enough! (2025.acl-long)
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Weixiong Zheng, Peijian Zeng, YiWei Li, Hongyan Wu, Nankai Lin, Junhao Chen, Aimin Yang, Yongmei Zhou
| Challenge: | Large language models (LLMs) excel in various tasks but remain vulnerable to jailbreak attacks, where adversaries manipulate prompts to generate harmful outputs. |
| Approach: | They propose a Reverse Embedded Defense Attack mechanism that disguises the attack intention as the "defense" intention against harmful content. |
| Outcome: | The proposed method outperforms existing methods on open-source and closed-source models and enables successful jailbreak in one iteration. |
BLSP-Emo: Towards Empathetic Large Speech-Language Models (2024.emnlp-main)
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| Challenge: | BLSP-Emo model understands both semantics and emotions in speech and generates empathetic responses. |
| Approach: | They propose a language-speech pretraining with emotion support that utilizes existing speech and emotion recognition datasets to create an end-to-end speech-language model. |
| Outcome: | The proposed model can understand both semantics and emotions in speech and generate empathetic responses. |
Towards Enhancing Relational Rules for Knowledge Graph Link Prediction (2023.findings-emnlp)
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| Challenge: | Existing knowledge graph reasoning methods are inadequate for missing knowledge . Various methods are explored to facilitate reasoning for missing information . |
| Approach: | They propose a novel knowledge graph reasoning approach that uses a query-related fusion gate unit to model the sequentiality of relation composition and a buffering update mechanism to alleviate lagged entity information propagation. |
| Outcome: | Experimental results show that the proposed approach is superior on both transductive and inductive link prediction tasks. |
Skill Discovery for Software Scripting Automation via Offline Simulations with LLMs (2026.findings-eacl)
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Paiheng Xu, Gang Wu, Xiang Chen, Tong Yu, Chang Xiao, Franck Dernoncourt, Tianyi Zhou, Wei Ai, Viswanathan Swaminathan
| Challenge: | Large Language Models (LLMs) can generate code from natural language queries, but runtime code generation is limited due to unverified code, security risks, longer response times, and higher computational costs. |
| Approach: | They propose an offline simulation framework to curate a software-specific skillset by exploiting large language models and publicly available scripting guides. |
| Outcome: | The proposed framework significantly improves automation success rates, reduces response time, and saves runtime token costs compared to traditional runtime code generation. |
MMCLIP: Cross-Modal Attention Masked Modelling for Medical Language-Image Pre-Training (2026.acl-long)
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| Challenge: | Existing vision-and-language pretraining methods face challenges in reconstructing pathological features due to limited data. |
| Approach: | They propose a method that uses masked modeling to enhance visual and linguistic learning. |
| Outcome: | MMCLIP integrates unpaired data through disease-kind prompts to achieve state-of-the-art performance in zero-shot and fine-tuning across five benchmarks. |
GLGE: A New General Language Generation Evaluation Benchmark (2021.findings-acl)
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Dayiheng Liu, Yu Yan, Yeyun Gong, Weizhen Qi, Hang Zhang, Jian Jiao, Weizhu Chen, Jie Fu, Linjun Shou, Ming Gong, Pengcheng Wang, Jiusheng Chen, Daxin Jiang, Jiancheng Lv, Ruofei Zhang, Winnie Wu, Ming Zhou, Nan Duan
| Challenge: | Multi-task benchmarks focus on a range of Natural Language Understanding (NLU) tasks without considering the Natural Language Generation (NLG) models. |
| Approach: | They propose a multi-task benchmark for evaluating the generalization capabilities of NLG models across eight language generation tasks. |
| Outcome: | The proposed benchmarks are based on GLUE and Su-perGLUE for English and several other languages. |
Reflective RAG: Self-Evaluation Driven Strategy Optimization in Agentic Retrieval-Augmented Generation (2026.findings-acl)
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| Challenge: | Recent agentic RAG systems lack the capacity to evaluate the utility of retrieved information, leading to brittle reasoning and suboptimal decision-making. |
| Approach: | They propose a framework that integrates self-evaluation to dynamically optimize retrieval and generation strategy. |
| Outcome: | The proposed framework outperforms strong agentic baselines on five knowledge-intensive QA benchmarks and improves training stability and generalization to multi-hop reasoning tasks. |
DMSD: Dual-Modal Semantic Disentanglement for Compositional Zero-Shot Learning (2026.findings-acl)
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| Challenge: | Compositional Zero-Shot Learning (CZSL) is a new research paradigm that learns sub-concepts from seen compositions and recognizes unseen novel combinations. |
| Approach: | They propose a Dual-Modal Semantic Disentanglement framework that integrates visual and textual information to achieve effective sub-concept disentangling. |
| Outcome: | The proposed framework achieves state-of-the-art performance on three benchmark datasets . it integrates a class-centroid bridge module to guide class centroids toward the textual space . |
Act as you think: Reinforcing Consistent Reasoning in Medical Visual Question Answering (2026.acl-long)
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Songtao Jiang, Yuan Wang, Ruizhe Chen, Yan Zhang, Ruilin Luo, Bohan Lei, Yeying Jin, Sibo Song, ZhiBo Yang, Jimeng Sun, Jian Wu, Zuozhu Liu
| Challenge: | Recent advances have improved the accuracy of medical visual question answering (Med-VQA) however, the high stakes nature of the medical domain has precipitated a shift towards interpretability and transparency of reasoning processes. |
| Approach: | They propose a reinforcement learning from verifiable rewards framework that rewards internal consistency and logical coherence. |
| Outcome: | The proposed framework rewards internal consistency and logical coherence, and is highly versatile, the authors show. |
QiMeng-Attention: SOTA Attention Operator is generated by SOTA Attention Algorithm (2025.findings-acl)
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Qirui Zhou, Shaohui Peng, Weiqiang Xiong, Haixin Chen, Yuanbo Wen, Haochen Li, Ling Li, Qi Guo, Yongwei Zhao, Ke Gao, Ruizhi Chen, Yanjun Wu, Zhao Chen, Yunji Chen
| Challenge: | Existing LLMs cannot comprehend the complex data flow and computation process of the attention operator and utilize low-level primitive to exploit GPU performance. |
| Approach: | They propose an LLM-friendly Thinking Language (LLM-TL) that can decouple the generation of high-level optimization logic and low-level implementation on GPU and enhance LLMs’ understanding of attention operator. |
| Outcome: | The proposed method outshines existing LLMs on A100, RTX8000, and T4 GPUs, achieving a speed-up of up to 35.16. |
OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis (2025.acl-long)
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Qiushi Sun, Kanzhi Cheng, Zichen Ding, Chuanyang Jin, Yian Wang, Fangzhi Xu, Zhenyu Wu, Chengyou Jia, Liheng Chen, Zhoumianze Liu, Ben Kao, Guohao Li, Junxian He, Yu Qiao, Zhiyong Wu
| Challenge: | Graphical User Interface (GUI) agents powered by Vision-Language Models (VLMs) have demonstrated human-like computer control capability. |
| Approach: | They propose a GUI data synthesis pipeline that reverse engineers GUI trajectory construction process by executing pre-defined tasks. |
| Outcome: | The proposed GUI data synthesis pipeline overcomes the bottlenecks of previous methods that rely on pre-defined tasks and limited data diversity. |
Dense Passage Retrieval for Open-Domain Question Answering (2020.emnlp-main)
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Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih
| Challenge: | Open-domain question answering relies on efficient passage retrieval to select candidate contexts. |
| Approach: | They propose a dual-encoder framework that can be implemented to retrieve passages from a small number of questions and passages. |
| Outcome: | The proposed system outperforms a strong Lucene-BM25 system in top-20 passage retrieval accuracy on multiple open-domain QA benchmarks. |
Learning What Matters: Dynamic Dimension Selection and Aggregation for Interpretable Vision-Language Reward Modeling (2026.acl-long)
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Qiyuan Chen, Hongsen Huang, Jiahe Chen, Qian Shao, Jintai Chen, Hongxia Xu, Renjie Hua, Ren Chuan, Jian Wu
| Challenge: | Existing multimodal reward models are interpretable but slow, while discriminative ones are opaque "black boxes." |
| Approach: | They propose a framework that dynamically decomposes evaluation into granular, interpretable dimensions. |
| Outcome: | The proposed framework outperforms open-source reward models on benchmarks like VL-RewardBench. |
Detecting AI-Generated Video: A Vision–Language Dual-View Survey (2026.findings-acl)
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| Challenge: | realism of AI-generated Videos (AIGC-V) rendering artifact-centric detection insufficient, authors argue . a vision–language dual-view taxonomy is proposed to systematize this rapidly evolving field . |
| Approach: | They propose a Vision–Language Dual-View taxonomy to systematize AIGC-V detection . they propose realism of AI-generated Videos is rendering traditional inspection insufficient . |
| Outcome: | The proposed model aims to show that the existing methods are consistent with real-world facts. |
Cost-Optimal Grouped-Query Attention for Long-Context Modeling (2025.emnlp-main)
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Yingfa Chen, Yutong Wu, Chenyang Song, Zhen Leng Thai, Xingyu Shen, Xu Han, Zhiyuan Liu, Maosong Sun
| Challenge: | Current GQA configurations overlook how context length influences inference cost . |
| Approach: | They propose a recipe for deriving cost-optimal GQA configurations that decouple the total head size from the hidden size and allow more flexible control over attention FLOPs. |
| Outcome: | The proposed configurations reduce memory usage and FLOPs by more than 50% compared to Llama-3's GQA, with *no degradation in model capabilities*. |
ACEBench: A Comprehensive Evaluation of LLM Tool Usage (2025.findings-emnlp)
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Chen Chen, Xinlong Hao, Weiwen Liu, Xu Huang, Xingshan Zeng, Shuai Yu, Dexun Li, Yuefeng Huang, Xiangcheng Liu, Wang Xinzhi, Wu Liu
| Challenge: | Existing benchmarks for evaluating LLMs’ tool usage face several limitations: limited evaluation scenarios, lacking assessments in real multi-turn dialogue contexts; narrow evaluation dimensions, with insufficient detailed assessments of how LLM use tools; and reliance on LLM or real API executions for evaluation, which introduces significant overhead. |
| Approach: | ACEBench is a benchmark for evaluating tool usage in Large Language Models . it categorizes data into three primary types based on evaluation methodology: Normal, Special, and Agent. |
| Outcome: | ACEBench categorizes data into three primary types based on evaluation methodology: Normal, Special, and Agent. |
Let Me Speak Freely? A Study On The Impact Of Format Restrictions On Large Language Model Performance. (2024.emnlp-industry)
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| Challenge: | Structured generation is used to extract key output information from large language models (LLMs). |
| Approach: | They examine whether constraints on generation space impact LLMs’ abilities, including reasoning and domain knowledge comprehension. |
| Outcome: | The proposed model is based on a few-shot in-context learning and instruction-following capabilities. |
Rapid Diffusion: Building Domain-Specific Text-to-Image Synthesizers with Fast Inference Speed (2023.acl-industry)
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Bingyan Liu, Weifeng Lin, Zhongjie Duan, Chengyu Wang, Wu Ziheng, Zhang Zipeng, Kui Jia, Lianwen Jin, Cen Chen, Jun Huang
| Challenge: | Text-to-Image Synthesis (TIS) aims to generate images based on textual inputs . but, current diffusion-based models lack entity knowledge and low inference speed . |
| Approach: | They propose a framework for training and deploying latent diffusion models with rich entity knowledge injected and optimized networks. |
| Outcome: | The proposed framework improves image quality and inference speed and can be used in industrial applications. |
Knowledge Graph Enhanced Large Language Model Editing (2024.emnlp-main)
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| Challenge: | Existing methods for editing large language models struggle to track and incorporate changes in knowledge associated with edits, which limits the generalization ability of post-edit LLMs in processing edited knowledge. |
| Approach: | They propose a model editing method that leverages knowledge graphs to enhance LLM editing by capturing changes in associated knowledge by constructing an external graph. |
| Outcome: | The proposed method improves the generalization ability of LLMs in processing edited knowledge. |
SgSum:Transforming Multi-document Summarization into Sub-graph Selection (2021.emnlp-main)
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| Challenge: | Existing extractive multi-document summarization methods score each sentence individually and extract salient sentences one by one. |
| Approach: | They propose a novel framework for extractive multi-document summarization that selects a sub-graph as the summary instead of selecting salient sentences. |
| Outcome: | The proposed framework improves on existing methods on multi-document datasets and human evaluations show it produces more coherent and informative summaries. |
ViDoRAG: Visual Document Retrieval-Augmented Generation via Dynamic Iterative Reasoning Agents (2025.emnlp-main)
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| Challenge: | Existing benchmarks focus on image-based question answering (QA) but ignore the fundamental challenges of efficient retrieval, comprehension, and reasoning within dense visual documents. |
| Approach: | They propose a novel multi-agent RAG framework tailored for complex reasoning across visual documents that employs a Gaussian Mixture Model (GMM)-based hybrid strategy to handle multi-modal retrieval. |
| Outcome: | The proposed framework outperforms existing methods by over 10% on the competitive ViDoSeek benchmark. |
Minimal Distillation Schedule for Extreme Language Model Compression (2024.findings-eacl)
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| Challenge: | Existing methods for teacher assistant-based distillation require multiple trials to find the optimal teacher assistant. |
| Approach: | They propose a method that allows scheduling of an optimal teacher assistant in just one trial . they show that student performance is positively correlated with the scale-performance tradeoff . |
| Outcome: | The proposed method can select the optimal teacher assistant in just one trial . it can be used to compare performance of student and teacher assistants on GLUE benchmarks. |
Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERT (2020.acl-main)
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| Challenge: | Recent pre-trained language models achieve state-of-the-art performance for downstream NLP tasks. |
| Approach: | They propose a parameter-free probing technique for analyzing pre-trained language models . their method does not require direct supervision from probing tasks . |
| Outcome: | The proposed method improves on linguistically-uninformed baselines on pre-trained language models. |
LocAgent: Graph-Guided LLM Agents for Code Localization (2025.acl-long)
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Zhaoling Chen, Robert Tang, Gangda Deng, Fang Wu, Jialong Wu, Zhiwei Jiang, Viktor Prasanna, Arman Cohan, Xingyao Wang
| Challenge: | Existing approaches struggle to efficiently navigate complex codebases when identifying relevant code snippets. |
| Approach: | They propose a graph-guided agent framework that addresses code localization through a distributed graph-based agent. |
| Outcome: | The proposed framework improves accuracy on real-world benchmarks and can be used to locate code snippets at a cost of 86%. |
Think before Go: Hierarchical Reasoning for Image-goal Navigation (2026.acl-long)
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| Challenge: | Existing methods for image-goal navigation fail to extract informative visual cues, leading agents to wander around. |
| Approach: | They propose a framework that decomposes image-goal navigation into high-level planning and low-level execution. |
| Outcome: | The proposed method is superior to existing methods in both simulation and real-world environments. |
Preserving Commonsense Knowledge from Pre-trained Language Models via Causal Inference (2023.acl-long)
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Junhao Zheng, Qianli Ma, Shengjie Qiu, Yue Wu, Peitian Ma, Junlong Liu, Huawen Feng, Xichen Shang, Haibin Chen
| Challenge: | Existing studies attribute catastrophic forgetting to fine-tuning, and they retain pre-trained knowledge indiscriminately without identifying what knowledge is transferable. |
| Approach: | They propose a unified objective for fine-tuning to retrieve the causality back from pre-trained data and use it to mitigate negative transfer while preserving knowledge. |
| Outcome: | The proposed method outperforms state-of-the-art fine-tuning methods on commonsense QA datasets and can be implemented as a plug-in module to inflate the performance of existing QA models. |
Beyond Prompt Engineering: A Systematic Analysis of Prompt Lexical Sensitivity and Its Impacts on Quality (2026.findings-acl)
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Qipeng Xie, Zi Liang, Jiafei Wu, Yufei Chen, Weizheng Wang, Wenao Ma, Zhong Ming, Haiqin Yang, Kaishun Wu
| Challenge: | Existing studies on prompt engineering have focused on optimizing models for performance under stylistic perturbations. |
| Approach: | They conduct the first analysis of n-gram token-level mechanisms . they find that higher average performance is inherently associated with lower variance and greater stability. |
| Outcome: | The proposed model reduces the variance of the generated code by 40% . the proposed model is based on a large-scale dataset of 132,000 prompt variants . |
Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis (2022.findings-acl)
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| Challenge: | Aspect-based sentiment analysis (ABSA) predicts sentiment polarity towards a specific aspect in a sentence. |
| Approach: | They propose to use a dynamic aspect-oriented semantics-based method to learn ABSA. |
| Outcome: | The proposed method can learn dynamic aspect-oriented semantics for ABSA on three benchmark datasets. |
Truth or Sophistry? LoFa: A Benchmark for LLM Robustness Against Logical Fallacies (2026.acl-long)
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| Challenge: | Prior work has focused on the ability of Large Language Models to **identify** or **classify** fallacies, but their robustness against these fallacias in persuasive contexts remains largely unexplored. |
| Approach: | They propose a new metric to assess LLM robustness against fallacies by pairing factual questions with fallacious arguments and developing a multi-round debate framework to assess model resilience. |
| Outcome: | The proposed metric disentangles robustness from a model’s knowledge limitations and demonstrates unique vulnerability profiles across models. |
LPO: Towards Accurate GUI Agent Interaction via Location Preference Optimization (2026.findings-acl)
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Jiaqi Tang, Yu Xia, Yi-Feng Wu, Yuwei Hu, Chen Yuhui, Qing-Guo Chen, Xiaogang Xu, Xiangyu Wu, Hao LU, Yanqing Ma, Shiyin Lu, Qifeng Chen
| Challenge: | Existing strategies for spatial localization are limited due to their limited capacity to perceive positional data. |
| Approach: | They propose a location-based approach that leverages locational data to optimize interaction preferences. |
| Outcome: | The proposed approach achieves SOTA results across offline benchmarks and real-world evaluations. |
Improving Translation Quality Estimation with Bias Mitigation (2023.acl-long)
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| Challenge: | State-of-the-art translation Quality Estimation models are biased, relying on monolingual features while ignoring the bilingual semantic alignment. |
| Approach: | They propose a method to mitigate the bias of translation quality estimation models by contrastive learning between clean and noisy sentence pairs. |
| Outcome: | The proposed method improves the estimation performance while mitigating the bias. |
INTELMO: Enhancing Models’ Adoption of Interactive Interfaces (2023.emnlp-demo)
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| Challenge: | INTELMO is an easy-to-use library to help model developers adopt user-faced interactive interfaces for their language models. |
| Approach: | They propose a library to help model developers adopt user-faced interactive interfaces and articles from real-time RSS sources for their language models. |
| Outcome: | The proposed library categorizes common NLP tasks and provides default style patterns . it provides developers with fine-grained and flexible control over user interfaces . |
BFS-Prover: Scalable Best-First Tree Search for LLM-based Automatic Theorem Proving (2025.acl-long)
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| Challenge: | Existing approaches to theorem proving in large language models rely on value functions and/or Monte Carlo Tree Search (MCTS), but the potential of simpler methods like Best-First Tree Search remains underexplored. |
| Approach: | They propose a scalable expert iteration framework that implements strategic data filtering at each expert iteration round, excluding problems solvable via beam search node expansion to focus on harder cases. |
| Outcome: | The proposed framework achieves a state-of-the-art score of 72.95 on the MiniF2F test set and challenges the perceived necessity of complex tree search methods. |
RankCSE: Unsupervised Sentence Representations Learning via Learning to Rank (2023.acl-long)
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Jiduan Liu, Jiahao Liu, Qifan Wang, Jingang Wang, Wei Wu, Yunsen Xian, Dongyan Zhao, Kai Chen, Rui Yan
| Challenge: | Unsupervised sentence representation learning is one of the fundamental problems in natural language processing . contrastive learning methods fail to capture fine-grained ranking information among the sentences . |
| Approach: | They propose a novel approach for unsupervised sentence representation learning that integrates ranking consistency and ranking distillation with contrastive learning into a unified framework. |
| Outcome: | The proposed approach performs better over state-of-the-art models on STS and TR tasks. |
Keyphrase Generation with Correlation Constraints (D18-1)
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| Challenge: | Existing methods for keyphrase generation ignore correlation among keyphrases, resulting in duplication and coverage issues. |
| Approach: | They propose a new sequence-to-sequence architecture for keyphrase generation that captures correlation among keyphrases by preceding phrases to eliminate duplicate phrases and improve result coherence. |
| Outcome: | The proposed model outperforms the state-of-the-art method on benchmark datasets in terms of accuracy and diversity. |
Reinforcement Learning–Guided Adaptive Tuning for Out-of-Distribution Harmful Text Detection (2026.acl-long)
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| Challenge: | Existing methods for testing harmful information on social media rely on fixed parameters that fail to handle substantial semantic discrepancies . RLAT can be used to adapt to semantic variations while preventing overfitting from continuous tuning. |
| Approach: | They propose a reinforcement learning-guided adaptive tuning method for harmful text detection that optimizes consistency loss and applies word-level attention constraints to reduce over-reliance on local words. |
| Outcome: | The proposed method outperforms state-of-the-art models in cross-platform and cross-temporal scenarios across multiple public datasets. |
Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study (2025.coling-main)
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Bowen Li, Wenhan Wu, Ziwei Tang, Lin Shi, John Yang, Jinyang Li, Shunyu Yao, Chen Qian, Binyuan Hui, Qicheng Zhang, Zhiyin Yu, He Du, Ping Yang, Dahua Lin, Chao Peng, Kai Chen
| Challenge: | Existing benchmarks focused on simplified or isolated aspects of coding, ignoring the full spectrum of programming challenges. |
| Approach: | They propose a case study that examines the performance of large language models across the entire software development lifecycle with four programming languages, multiple domains, and carefully designed and verified metrics for each task. |
| Outcome: | The proposed model performs across the entire software development lifecycle, including design, environment setup, implementation, acceptance testing, and unit testing. |
Do Current Video LLMs Have Strong OCR Abilities? A Preliminary Study (2025.coling-main)
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| Challenge: | a new benchmark evaluates video-based optical character recognition (Video OCR) performance of multi-modal models in videos . the benchmark aims to improve video LLMs' ability to extract text from video content . previous benchmarks have focused on video QA, but not video-related QA. |
| Approach: | They propose to evaluate the video OCR performance of multi-modal models in videos . they use a semi-automated approach that integrates the OCR ability of image LLMs with manual refinement . |
| Outcome: | The proposed benchmark includes 1,028 videos and 2,961 question-answer pairs . it integrates the OCR ability of image LLMs with manual refinement . |
LegalAgentBench: Evaluating LLM Agents in Legal Domain (2025.acl-long)
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Haitao Li, Junjie Chen, Jingli Yang, Qingyao Ai, Wei Jia, Youfeng Liu, Kai Lin, Yueyue Wu, Guozhi Yuan, Yiran Hu, Wuyue Wang, Yiqun Liu, Minlie Huang
| Challenge: | Existing general-domain benchmarks do not capture complexity of real-world judicial cognition and decision-making. |
| Approach: | They propose a benchmark specifically designed to evaluate LLM Agents in the legal domain. |
| Outcome: | The proposed benchmark includes 17 corpora from real-world legal scenarios and provides 37 tools for interacting with external knowledge. |
CoMave: Contrastive Pre-training with Multi-scale Masking for Attribute Value Extraction (2023.findings-acl)
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Xinnan Guo, Wentao Deng, Yongrui Chen, Yang Li, Mengdi Zhou, Guilin Qi, Tianxing Wu, Dong Yang, Liubin Wang, Yong Pan
| Challenge: | Existing methods to extract product features from unstructured text still suffer from problems . e-commerce platforms are focusing on multi-scale values, which can be confusing . |
| Approach: | They propose a pre-training technique to automatically obtain attribute value pairs from product descriptions to aid e-commerce. |
| Outcome: | The proposed method improves on the existing token-level masking strategy and achieves state-of-the-art on four benchmarks. |
CoTKR: Chain-of-Thought Enhanced Knowledge Rewriting for Complex Knowledge Graph Question Answering (2024.emnlp-main)
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| Challenge: | Existing knowledge rewriting methods may include irrelevant information, omit crucial details, or fail to align with the question’s semantics. |
| Approach: | They propose a new rewriting method CoTKR for generating reasoning traces and corresponding knowledge in an interleaved manner, thereby mitigating the limitations of single-step knowledge rewrite. |
| Outcome: | The proposed method mitigates the limitations of single-step knowledge rewriting and bridges the preference gap between the knowledge reactor and the question answering (QA) model. |
FanChuan: A Multilingual and Graph-Structured Benchmark For Parody Detection and Analysis (2025.findings-acl)
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Yilun Zheng, Sha Li, Fangkun Wu, Yang Ziyi, Lin Hongchao, Zhichao Hu, Cai Xinjun, Ziming Wang, Jinxuan Chen, Sitao Luan, Jiahao Xu, Lihui Chen
| Challenge: | Parody is an emerging phenomenon on social media, where individuals imitate a role or position opposite to their own . limited available data and deficient diversity in current datasets hinder study of parody . |
| Approach: | They build a dataset of parody users and annotated comments from both English and Chinese corpora to test parody detection and comment sentiment analysis. |
| Outcome: | The proposed datasets provide richer contextual information, which is lacking in existing datasets. |
AMPO: Automatic Multi-Branched Prompt Optimization (2024.emnlp-main)
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Sheng Yang, Yurong Wu, Yan Gao, Zineng Zhou, Bin Zhu, Xiaodi Sun, Jian-Guang Lou, Zhiming Ding, Anbang Hu, Yuan Fang, Yunsong Li, Junyan Chen, Linjun Yang
| Challenge: | Existing prompt engineering techniques are limited to producing single flow instructions, struggling with handling diverse patterns. |
| Approach: | They propose an automatic prompt optimization method that iteratively develops a multi-branched prompt using failure cases as feedback. |
| Outcome: | The proposed method achieves the best results across five tasks and demonstrates significant optimization efficiency due to adoption of a minimal search strategy. |
When Safety Alignment Fails to Generalize: Probing with Language Game Jailbreaks (2026.findings-acl)
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| Challenge: | Existing safety alignment methods rely on fixed or narrow transformation schemes to generalize . existing methods based on fixed and narrow transformations are often inadequate . |
| Approach: | They propose a framework for discovering and refining language game-based jailbreaks to probe alignment generalization. |
| Outcome: | The proposed framework allows controlled exploration of alignment behavior across closely related linguistic variants. |
Personal Travel Solver: A Preference-Driven LLM-Solver System for Travel Planning (2025.acl-long)
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| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated remarkable capabilities in understanding complex instructions and reasoning across diverse domains. |
| Approach: | They propose to integrate user’s implicit preference into the progress of travel planning by integrating real user reviews and point-of-interest metadata from Google Local into RealTravel. |
| Outcome: | The proposed system achieves better performance than baseline methods and improves the level of personalization. |
Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA (2024.emnlp-main)
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Minzheng Wang, Longze Chen, Fu Cheng, Shengyi Liao, Xinghua Zhang, Bingli Wu, Haiyang Yu, Nan Xu, Lei Zhang, Run Luo, Yunshui Li, Min Yang, Fei Huang, Yongbin Li
| Challenge: | Existing benchmarks for evaluating long-context language models employ irrelevant noise texts to artificially extend the length of test cases, diverging from the real-world scenarios of long-constituency applications. |
| Approach: | They propose a long-context benchmark, Loong, aligning with realistic scenarios through extended multi-document question answering (QA) . |
| Outcome: | The proposed model can scale up the context window of large language models to perform in-depth analysis of multiple long documents. |
Scaling Context, Not Parameters: Training a Compact 7B Language Model for Efficient Long-Context Processing (2025.acl-industry)
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| Challenge: | a new language model that supports 512K context lengths addresses practical limitations in long-context training . a competitive 35% score on 512k-token BABILong tasks without RAG or task-specific tuning is achieved . |
| Approach: | They present a language model that supports 512K-token context length . they evaluated its long-context learning performance on three benchmarks . |
| Outcome: | The model outperforms open-source models on three long-context benchmarks . it achieves a competitive 35% score on 512K-token BABILong tasks without RAG or fine-tuning . |
Text Style Transfer Back-Translation (2023.acl-long)
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Daimeng Wei, Zhanglin Wu, Hengchao Shang, Zongyao Li, Minghan Wang, Jiaxin Guo, Xiaoyu Chen, Zhengzhe Yu, Hao Yang
| Challenge: | Current methods require large amount of bilingual training data, which is challenging and sometimes impossible task. |
| Approach: | They propose a method to modify the style of inputs by modifying the source side of BT data. |
| Outcome: | The proposed method significantly improves translation quality against popular BT benchmarks on high-resource and low-resourced language pairs. |
UniKeyphrase: A Unified Extraction and Generation Framework for Keyphrase Prediction (2021.findings-acl)
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| Challenge: | Mainstream methods that ignore the diversity among keyphrases or weakly capture the relation between tasks implicitly ignore keyphrase diversity. |
| Approach: | They propose a novel end-to-end learning framework that jointly learns to extract and generate keyphrases by exploiting latent semantic relation between extraction and generation. |
| Outcome: | The proposed approach outperforms mainstream methods on a benchmarked document on keyphrase prediction. |
Large Language Models Help Humans Verify Truthfulness – Except When They Are Convincingly Wrong (2024.naacl-long)
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| Challenge: | Large Language Models (LLMs) are increasingly used for accessing information on the web. |
| Approach: | They conduct experiments with 80 crowdworkers to compare LLMs with search engines . they ask LLM to provide contrastive information to reduce over-reliance on LLM . |
| Outcome: | The results show that LLMs can outperform search engines but not LLM explanations . the study shows that LMS explanations are not reliable replacements for reading retrieved passages compared to search engines alone. |
BERT4GCN: Using BERT Intermediate Layers to Augment GCN for Aspect-based Sentiment Classification (2021.emnlp-main)
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| Challenge: | Existing approaches to Aspect-based sentiment classification ignore sequential features of context and lack syntactic knowledge of sentences. |
| Approach: | They propose a model which integrates sequential grammatical features from context and syntactic knowledge from dependency graphs to augment GCN to better encode dependency graph outputs. |
| Outcome: | The proposed model outperforms state-of-the-art models when equipped with contextual word embedding from pre-training language models. |
MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization (2023.findings-emnlp)
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Yuyan Chen, Zhihao Wen, Ge Fan, Zhengyu Chen, Wei Wu, Dayiheng Liu, Zhixu Li, Bang Liu, Yanghua Xiao
| Challenge: | Existing research emphasizes the importance of adapting prompts to specific tasks, rather than specific LLMs. |
| Approach: | They propose a model-adaptive prompt optimizer method that optimizes original prompts for each LLM in downstream tasks. |
| Outcome: | The proposed method can optimize prompts for an LLM in downstream tasks. |
RouterEval: A Comprehensive Benchmark for Routing LLMs to Explore Model-level Scaling Up in LLMs (2025.findings-emnlp)
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| Challenge: | a lack of comprehensive benchmarks for Routing large language models has hindered the development of routers. |
| Approach: | They propose a router-based benchmark to evaluate Routing large language models . the benchmark includes performance records for 12 popular LLM evaluations . |
| Outcome: | The proposed model-level scaling up phenomenon can surpass the best single model in the pool and many existing strong LLMs. |
Probabilistic Graph Reasoning for Natural Proof Generation (2021.findings-acl)
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| Challenge: | Existing approaches to reasoning over formal representations do not explicitly consider inter-dependency between answers and proofs. |
| Approach: | They propose a novel approach for joint answer prediction and proof generation using an induced graphical model. |
| Outcome: | The proposed approach achieves 10%-30% improvement on QA accuracy in evaluations under diverse conditions. |
LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding (2024.acl-long)
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Mostafa Elhoushi, Akshat Shrivastava, Diana Liskovich, Basil Hosmer, Bram Wasti, Liangzhen Lai, Anas Mahmoud, Bilge Acun, Saurabh Agarwal, Ahmed Roman, Ahmed Aly, Beidi Chen, Carole-Jean Wu
| Challenge: | Large Language Models (LLMs) have been deployed to many applications, yet their high compute and memory requirements lead to high financial and energy costs when deployed to GPU servers. |
| Approach: | They propose an end-to-end solution to speed-up inference of large language models . they apply layer dropout, and show that it increases the accuracy of early exit at earlier layers without adding any auxiliary layers or modules to the model. |
| Outcome: | The proposed method shows speedups of up to 2.16x on summarization for CNN/DM documents, 1.82x on coding, and 2.0x on TOPv2 semantic parsing task. |
PuzzleClone: A DSL-Powered Framework for Synthesizing Verifiable Data (2026.findings-acl)
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| 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. |
XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation (2020.emnlp-main)
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Yaobo Liang, Nan Duan, Yeyun Gong, Ning Wu, Fenfei Guo, Weizhen Qi, Ming Gong, Linjun Shou, Daxin Jiang, Guihong Cao, Xiaodong Fan, Ruofei Zhang, Rahul Agrawal, Edward Cui, Sining Wei, Taroon Bharti, Ying Qiao, Jiun-Hung Chen, Winnie Wu, Shuguang Liu, Fan Yang, Daniel Campos, Rangan Majumder, Ming Zhou
| Challenge: | XGLUE provides a benchmark dataset to train large-scale cross-lingual pre-trained models . XCLUE provides 11 diversified tasks that cover both understanding and generation scenarios . |
| Approach: | They introduce a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora. |
| Outcome: | The proposed dataset is labeled in English and includes only natural language understanding tasks. |
Benchmarking Contextual and Paralinguistic Reasoning in Speech-LLMs: A Case Study with In-the-Wild Data (2025.findings-emnlp)
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Qiongqiong Wang, Hardik Bhupendra Sailor, Tianchi Liu, Wenyu Zhang, Muhammad Huzaifah, Nattadaporn Lertcheva, Shuo Sun, Nancy F. Chen, Jinyang Wu, AiTi Aw
| Challenge: | Recent speech-LLMs have shown impressive performance in tasks like transcription and translation, yet they remain limited in understanding the paralinguistic aspects of speech crucial for social and emotional intelligence. |
| Approach: | They propose a benchmark for evaluating speech-LLMs on contextual paralinguistic reasoning . the benchmark includes curated question answering datasets requiring both linguistic and empathetic understanding . |
| Outcome: | The proposed benchmark reveals a key gap in existing evaluations and offers insights into building more context-aware and emotionally intelligent LLMs. |
Navigating the Nuances: A Fine-grained Evaluation of Vision-Language Navigation (2024.findings-emnlp)
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| Challenge: | a new evaluation framework for vision-language navigation is proposed . current evaluation standards hinge on endpoint success rates and path alignment metrics . |
| Approach: | They propose a semi-automatic method for CFG construction with Large-Language Models . they induct data spanning five principal instruction categories and analyze them . |
| Outcome: | The proposed framework diagnoses current models for the Vision-Language Navigation task at a finer-grained level. |
Playing 20 Question Game with Policy-Based Reinforcement Learning (D18-1)
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| Challenge: | The 20 Questions (Q20) game encourages deductive reasoning and creativity. |
| Approach: | They propose a policy-based Reinforcement Learning method which learns optimal question selection . the method is robust to noisy answers and uses a reward network to estimate the more informative reward . |
| Outcome: | The proposed method outperforms an entropy-based engineering system and has competitive performance in noisy-free simulation environment. |
METRO: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialogues (2026.acl-long)
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| Challenge: | Developing non-collaborative dialogue agents traditionally requires manual codification of expert strategies. |
| Approach: | They propose a method that formalizes expert knowledge into a Strategy Forest from raw transcripts. |
| Outcome: | The proposed method outperforms existing methods by 9%-10% in two benchmarks. |
IceBreaker for Conversational Agents: Breaking the First-Message Barrier with Personalized Starters (2026.acl-industry)
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Hongwei Zheng, Weiqi Wu, Zhengjia Wang, Guanyu Jiang, Haoming Li, Tianyu Wu, Yongchun Zhu, Jingwu Chen, Feng Zhang
| Challenge: | Existing efforts focus on activation within ongoing dialogues, while overlooking a key real-world bottleneck. |
| Approach: | They propose a conversation starter generation system that generates personalized starters to guide users into conversation without explicit user intent. |
| Outcome: | The proposed system improves user active days by +1.84 and click-through rate by +94.25 and has been deployed in production. |
PCBERT: Parent and Child BERT for Chinese Few-shot NER (2022.coling-1)
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| Challenge: | Existing approaches to improve model performance on few-shot or zero-shot datasets are not effective for Chinese few- shot NER. |
| Approach: | They propose a prompt-based Parent and Child BERT for Chinese few-shot NER to train an annotating model on high-resource datasets and then discover more implicit labels on low-resourced datasets. |
| Outcome: | The proposed model can be used on Weibo and other Chinese NER datasets and it is shown to be effective in few-shot learning. |
MoSEs: Uncertainty-Aware AI-Generated Text Detection via Mixture of Stylistics Experts with Conditional Thresholds (2025.emnlp-main)
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| Challenge: | Existing methods neglect stylistic modeling and rely on static thresholds, which greatly limits the detection performance. |
| Approach: | They propose a framework that enables stylistics-aware uncertainty quantification through conditional threshold estimation. |
| Outcome: | The proposed framework achieves an average improvement 11.34% in detection performance compared to baselines. |
Fidelity-Enriched Contrastive Search: Reconciling the Faithfulness-Diversity Trade-Off in Text Generation (2023.emnlp-main)
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| Challenge: | Language models often generate fluent and convincing content but can lack consistency with the provided source, resulting in potential inaccuracies. |
| Approach: | They propose a new decoding method that augments the contrastive search framework with context-aware regularization terms to promote tokens that are semantically similar to the provided source while penalizing repetitiveness in the generated text. |
| Outcome: | The proposed method improves faithfulness across various language models while maintaining output diversity comparable to well-performing decoding algorithms. |
Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization (2024.findings-naacl)
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Yixin Liu, Alexander Fabbri, Jiawen Chen, Yilun Zhao, Simeng Han, Shafiq Joty, Pengfei Liu, Dragomir Radev, Chien-Sheng Wu, Arman Cohan
| Challenge: | Recent studies have found that large language models (LLMs) can achieve state-of-the-art performance on generic summarization benchmarks, but their performance on more complex summarizing task settings is less studied. |
| Approach: | They benchmark large language models on instruction controllable text summarization . they use 4 evaluation protocols and 11 LLMs to evaluate their performance . |
| Outcome: | The proposed model performs well on instruction controllable text summarization tasks with 4 evaluation protocols and 11 LLMs. |
No One Fits All: From Fixed Prompting to Learned Routing in Multilingual LLMs (2026.findings-acl)
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| Challenge: | Existing studies show that translation-based prompting is not universally optimal for multilingual LLMs. |
| Approach: | They evaluate translation-based prompting across ten languages and four benchmarks . they propose a lightweight classifier that predicts whether native or translation- based prompts are optimal . |
| Outcome: | The proposed classifiers achieve statistically significant improvements over fixed prompting strategies across ten languages and four benchmarks. |
Efficient Citer: Tuning Large Language Models for Enhanced Answer Quality and Verification (2024.findings-naacl)
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Marzieh Tahaei, Aref Jafari, Ahmad Rashid, David Alfonso-Hermelo, Khalil Bibi, Yimeng Wu, Ali Ghodsi, Boxing Chen, Mehdi Rezagholizadeh
| Challenge: | Existing models with explicit citations lack the ability to verify information generated by these models. |
| Approach: | They construct a citation training dataset and fine-tune two models to address the challenge of explicit citations efficiently. |
| Outcome: | The proposed models surpass ChatGPT and exhibit exceptional out-of-domain generalization in both human and automatic evaluation. |
Re-embedding Difficult Samples via Mutual Information Constrained Semantically Oversampling for Imbalanced Text Classification (2021.emnlp-main)
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| Challenge: | Existing frameworks for imbalanced text classification can generate anchor instances for difficult samples . difficult samples are hard to classify as they are embedded into an overlapping semantic region with the majority class. |
| Approach: | They propose a Mutual Information constrained Semantically Oversampling framework that generates anchor instances for difficult samples to help the backbone network determine the re-embedding position of a non-overlapping representation. |
| Outcome: | The proposed framework can generate anchor instances to help classifiers achieve significant improvements over baselines on a variety of imbalanced text classification tasks. |
On the Generation of Medical Dialogs for COVID-19 (2021.acl-short)
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Meng Zhou, Zechen Li, Bowen Tan, Guangtao Zeng, Wenmian Yang, Xuehai He, Zeqian Ju, Subrato Chakravorty, Shu Chen, Xingyi Yang, Yichen Zhang, Qingyang Wu, Zhou Yu, Kun Xu, Eric Xing, Pengtao Xie
| Challenge: | under the pandemic of COVID-19, people experiencing COVI D19-related symptoms have a pressing need to consult doctors. |
| Approach: | They develop a medical dialog system that can provide COVID19-related consultations . they use two dialog datasets containing conversations between doctors and patients . |
| Outcome: | The proposed system can provide COVID19-related consultations, but is too small compared with general-domain dialog datasets. |
RLHFPoison: Reward Poisoning Attack for Reinforcement Learning with Human Feedback in Large Language Models (2024.acl-long)
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| Challenge: | Recent advances in Large Language Models (LLMs) have significantly enhanced the capabilities in natural language processing. |
| Approach: | They propose a method to poison large language models by using annotators to rank a set of collected responses to generate longer tokens. |
| Outcome: | The proposed method can generate longer tokens without harming the original safety alignment performance. |
DRA-GRPO: Your GRPO Needs to Know Diverse Reasoning Paths for Mathematical Reasoning (2026.findings-acl)
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Xiwen Chen, Wenhui Zhu, Peijie Qiu, Xuanzhao Dong, Hao Wang, Haiyu Wu, Huayu Li, Aris Sotiras, Yalin Wang, Abolfazl Razi
| Challenge: | Existing methods for group-relative policy optimization rely on scalar correctness rewards that are often non-injective with respect to semantic content. |
| Approach: | They propose a framework that calibrates the reward signal using the semantic density of sampled groups. |
| Outcome: | The proposed framework outperforms strong baselines on five math benchmarks with 7,000 samples and 55 cost. |
Boosting LLM’s Molecular Structure Elucidation with Knowledge Enhanced Tree Search Reasoning (2025.acl-long)
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Xiang Zhuang, Bin Wu, Jiyu Cui, Kehua Feng, Xiaotong Li, Huabin Xing, Keyan Ding, Qiang Zhang, Huajun Chen
| Challenge: | Molecular structure elucidation involves deducing a molecule’s structure from various types of spectral data, which is crucial in chemical experimental analysis. |
| Approach: | They propose a Knowledge-enhanced reasoning framework for Molecular Structure Elucidation that leverages Monte Carlo Tree Search for test-time scaling as a plugin to extend the LLMs’ coverage of the chemical structure space. |
| Outcome: | The proposed framework significantly improves on both GPT-4o-mini and GPT4o, and a specialized molecule-spectrum scorer improves performance. |
Mind’s Mirror: Distilling Self-Evaluation Capability and Comprehensive Thinking from Large Language Models (2024.naacl-long)
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| Challenge: | Large language models (LLMs) have achieved significant advances in natural language processing, but their scale and computational demands pose challenges to their practical application. |
| Approach: | They propose a method for distilling the self-evaluation capability from LLMs into SLMs and advocate for more comprehensive thinking by incorporating multiple distinct CoTs and self-estimation outputs. |
| Outcome: | The proposed method significantly improves the performance of distilled SLMs on three NLP benchmarks. |
A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions (2026.acl-long)
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Zhiyin Yu, Yuchen Mou, Juncheng Yan, Junyu Luo, Chunchun Chen, Xing Wei, Yunhui Liu, Hongru Sun, Yuxing Zhang, Jun Xu, Yatao Bian, Ming Zhang, Wei Ye, Tieke He, Jie Yang, Guanjie Zheng, Zhonghai Wu, Bo Zhang, Lei Bai, Xiao Luo
| Challenge: | Existing research on reinforcement learning for LLMs under data scarcity has not been unified. |
| Approach: | They propose a top-up hierarchical framework built around three complementary perspectives: data-centric, training-centric and framework-centric. |
| Outcome: | The proposed framework provides a clear conceptual foundation for understanding the design space of data-efficient RL for large language models and to guide researchers working in this emerging area. |
Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation (2020.acl-main)
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Aditya Siddhant, Ankur Bapna, Yuan Cao, Orhan Firat, Mia Chen, Sneha Kudugunta, Naveen Arivazhagan, Yonghui Wu
| Challenge: | Existing multilingual NMT approaches do not utilize the abundance of monolingual data, especially in low-resource languages. |
| Approach: | They propose to combine monolingual data with self-supervision to pre-train translation models and fine-tune on small amounts of supervised data. |
| Outcome: | The proposed approach improves translation quality of low-resource languages and zero-shot translation quality. |
CRAB: A Benchmark for Evaluating Curation of Retrieval-Augmented LLMs in Biomedicine (2025.emnlp-industry)
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| Challenge: | Recent development in Retrieval-Augmented Large Language Models (LLMs) have shown great promise in biomedical applications. |
| Approach: | They propose a multilingual benchmark to evaluate retrieval-augmented large language models' curation ability. |
| Outcome: | The proposed benchmark is available in English, French, German and Chinese. |
A Survey of Multimodal Mathematical Reasoning: From Perception, Alignment to Reasoning (2026.acl-long)
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Tianyu Yang, Sihong Wu, Yilun Zhao, Zhenwen Liang, Lisen Dai, Chen Zhao, Minhao Cheng, Arman Cohan, Xiangliang Zhang
| Challenge: | Multimodal mathematical Reasoning (MMR) has attracted increasing attention for its ability to solve mathematical problems involving both textual and visual modalities. |
| Approach: | They review the theoretical frameworks of multimodal reasoning and examine the challenges they face in visual math tasks. |
| Outcome: | The proposed models can solve problems involving both textual and visual modalities. |
LLMs Can Simulate Standardized Patients via Agent Coevolution (2025.acl-long)
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Zhuoyun Du, LujieZheng LujieZheng, Renjun Hu, Yuyang Xu, Xiawei Li, Ying Sun, Wei Chen, Jian Wu, Haolei Cai, Haochao Ying
| Challenge: | Training medical personnel using standardized patients (SPs) remains a complex challenge, necessitating extensive domain expertise and role-specific practice. |
| Approach: | They propose a simulated patient framework that allows patient agents to simulate diagnostic process through multi-turn dialogues. |
| Outcome: | The proposed framework improves over existing reasoning methods by more than 10% in requirement alignment and better human preference after evolving over 200 cases for 10 hours with excellent generalizability. |
Exploring Question Guidance and Answer Calibration for Visually Grounded Video Question Answering (2024.findings-emnlp)
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| Challenge: | Existing methods for videoQA lack temporal localization labels, leading to inaccurate localization. |
| Approach: | They propose a Question-Guided and Answer-Calibrated TRansformer which guides and calibrates localization using question and option texts without localization labels. |
| Outcome: | The proposed model achieves comparable accuracy to large-scale pretrained models and leads in localization aspects. |
SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval (2024.findings-acl)
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Siwei Wu, Yizhi Li, Kang Zhu, Ge Zhang, Yiming Liang, Kaijing Ma, Chenghao Xiao, Haoran Zhang, Bohao Yang, Wenhu Chen, Wenhao Huang, Noura Al Moubayed, Jie Fu, Chenghua Lin
| Challenge: | Multi-modal information retrieval (MMIR) is a rapidly evolving field . current benchmarks for image-text pairings overlook the scientific domain . |
| Approach: | They develop a scientific domain-specific MMIR benchmark to evaluate image-text pairings using open-access research paper corpora. |
| Outcome: | The proposed benchmarks are based on 530K image-text pairs extracted from scientific documents with detailed captions. |
ControlMath: Controllable Data Generation Promotes Math Generalist Models (2024.emnlp-main)
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| Challenge: | Currently, mathematical reasoning is one of the most challenging areas for closed-source LLMs. |
| Approach: | They propose an iterative method involving an equation-generator module and two LLM-based agents that generate diverse equations and transform them into math word problems. |
| Outcome: | The proposed method enables the generation of diverse math problems, not limited to specific domains or distributions. |
Implicit Cross-Lingual Rewarding for Efficient Multilingual Preference Alignment (2025.findings-acl)
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| Challenge: | Existing approaches to align English LLMs with human preferences rely on expensive human annotations or advanced multilingual preference alignment models. |
| Approach: | They propose a method that captures learned preferences from English models by implicit rewards . they annotate preference relations in cross-lingual instruction-following pairs using English . |
| Outcome: | The proposed approach captures learned preferences from well-aligned English models by implicit rewards and transfers them to other languages through iterative training. |
DAST: Context-Aware Compression in LLMs via Dynamic Allocation of Soft Tokens (2025.findings-acl)
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Shaoshen Chen, Yangning Li, Zishan Xu, Yongqin Zeng, Shunlong Wu, Xinshuo Hu, Zifei Shan, Xin Su, Jiwei Tang, Yinghui Li, Hai-Tao Zheng
| Challenge: | Existing semantic vector-based compression methods do not account for the intrinsic information density variations between context chunks, instead allocating soft tokens uniformly across context chunk. |
| Approach: | They propose a method that leverages the LLM's intrinsic understanding of contextual relevance to guide compression. |
| Outcome: | The proposed method surpasses state-of-the-art methods on long context tasks. |
NesTools: A Dataset for Evaluating Nested Tool Learning Abilities of Large Language Models (2025.coling-main)
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| Challenge: | Existing benchmarks on nested tool learning are lacking relevant data instances. |
| Approach: | They propose a method to construct large-scale nested tool calls with different nesting structures using a large-quality dataset. |
| Outcome: | The proposed method can be used to evaluate the nested tool learning abilities of large language models (LLMs) in real-world applications. |
Co-Eval: Augmenting LLM-based Evaluation with Machine Metrics (2025.emnlp-main)
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| 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. |
CERD: A Comprehensive Chinese Rhetoric Dataset for Rhetorical Understanding and Generation in Essays (2024.findings-emnlp)
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Nuowei Liu, Xinhao Chen, Hongyi Wu, Changzhi Sun, Man Lan, Yuanbin Wu, Xiaopeng Bai, Shaoguang Mao, Yan Xia
| Challenge: | Existing rhetorical understanding and generation datasets focus on single coarse-grained categories or fine-grain categories, neglecting the intrinsic connections between different rhetorical devices. |
| Approach: | They propose a Chinese Essay Rhetoric Dataset with four coarse-grained categories . they propose to treat these categories as separate sub-tasks, thereby improving writing skills . |
| Outcome: | The proposed dataset improves the author's writing proficiency and language usage skills by recognizing and generating rhetorical sentences under given conditions. |
Attending via both Fine-tuning and Compressing (2021.findings-acl)
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| Challenge: | Existing studies show that attention mechanisms can improve models' interpretation, but they are not explicable. |
| Approach: | They propose a framework consisting of a learner and a compressor to purify attention scores . they propose to fine-tune and compress the attention mechanism to obtain a more faithful explanation . |
| Outcome: | The proposed framework improves performance and interpretability on eight benchmark datasets. |
Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations (2024.findings-emnlp)
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| Challenge: | Existing research focuses on developing powerful large language models for mathematical reasoning within monolingual languages. |
| Approach: | They propose to use translation to build powerful multilingual math reasoning models . they propose different training strategies to build xMR LLMs that outperform open-source LLM . |
| Outcome: | The proposed model outperforms open-source LLMs and surpasses ChatGPT in few-shot scenarios. |
CoMMIT: Coordinated Multimodal Instruction Tuning (2025.emnlp-main)
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Xintong Li, Junda Wu, Tong Yu, Rui Wang, Yu Wang, Xiang Chen, Jiuxiang Gu, Lina Yao, Julian McAuley, Jingbo Shang
| Challenge: | et al., 2024) show that multimodal instruction tuning is more effective than baselines. |
| Approach: | They propose a multimodal balance coefficient that enables quantitative measurement of the balance of learning . they propose auxiliary regularization on the gradient to promote updating with larger step sizes . |
| Outcome: | The proposed method is more effective than baselines in MLLM instruction tuning. |
DiMo-GUI: Advancing Test-time Scaling in GUI Grounding via Modality-Aware Visual Reasoning (2025.emnlp-main)
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| Challenge: | DiMo-GUI is a training-free framework for GUI grounding that splits input into textual elements and iconic elements, allowing the model to reason over each modality independently using general-purpose vision-language models. |
| Approach: | They propose a training-free framework for GUI grounding that leverages two core strategies: dynamic visual grounding and modality-aware optimization. |
| Outcome: | The proposed framework splits the input into textual elements and iconic elements, allowing the model to reason over each modality independently using general-purpose vision-language models. |
Identifying and Mitigating Social Bias Knowledge in Language Models (2025.findings-naacl)
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| Challenge: | Existing methods for debiasing may generate incorrect or nonsensical predictions but leave aside individual commonsense facts, resulting in modified knowledge that elicits unreasonable or undesired predictions. |
| Approach: | They propose a framework that identifies encoding locations of biases within language models and then applies the Fairness-Stamp (FAST) they also propose 'BiaScope' to evaluate the retention of commonsense knowledge and generalization across paraphrased social biase. |
| Outcome: | The proposed framework surpasses state-of-the-art baselines with superior debiasing performance while not compromising the overall model capability for knowledge retention and prediction. |
RECAP: An End-to-End Platform for Capturing, Replaying, and Analyzing AI-Assisted Programming Interactions (2026.acl-demo)
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| Challenge: | Deployed in a university software engineering course, RECAP captured 2,034 prompts and 8,239 code edits from 41 students across a multi-week project. |
| Approach: | They propose an open-source platform that passively records AI chat sessions and fine-grained code edits inside VS Code without disrupting the developer’s workflow. |
| Outcome: | The open-source platform captures 2,034 prompts and 8,239 code edits from 41 students across a multi-week project. |
Evaluating the Expressive Appropriateness of Speech in Rich Contexts (2026.acl-long)
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Tianrui Wang, Ziyang Ma, Yizhou Peng, Haoyu Wang, Zhikang Niu, Zikang Huang, Yihao Wu, Yi-Wen Chao, Yu Jiang, Yuheng Lu, Guanrou Yang, Xuanchen Li, Hexin Liu, Chunyu Qiang, Cheng Gong, Yifan Yang, Tianchi Liu, Junyu Wang, Nana Hou, Meng Ge, Fuming You, Yang Wei, Zhongqian Sun, Hu Haifeng, Xiaobao Wang, Eng Siong Chng, Xie Chen, Longbiao Wang, Jianwu Dang
| Challenge: | Existing methods for evaluating expressive speech focus on word accuracy, naturalness, signal quality, or emotional intensity at the utterance level. |
| Approach: | They propose a framework for Evaluating Expressive Appropriateness in speech that assesses whether a speech sample aligns with the underlying communicative intent implied by its discourse-level narrative context. |
| Outcome: | The proposed framework outperforms existing speech evaluation and analysis systems on a human-annotated test set. |
Long-Chain Reasoning Distillation via Adaptive Prefix Alignment (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities, especially in solving complex mathematical problems. |
| Approach: | They propose a framework that exploits teacher CoTs for distillation through adaptive prefix alignment. |
| Outcome: | The proposed framework outperforms baseline models on multiple mathematical reasoning benchmarks by over 3%. |
The Impact of Large Language Models in Academia: from Writing to Speaking (2025.findings-acl)
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| Challenge: | Large language models (LLMs) are impacting human society, especially in textual information. |
| Approach: | They propose to build an automated monitoring platform to track the impact of large language models on human expression. |
| Outcome: | The results show that LLM-style words such as significant are used more frequently in abstracts and oral presentations. |
Benchmarking for Domain-Specific LLMs: A Case Study on Academia and Beyond (2025.findings-emnlp)
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| Challenge: | Comp-Comp is an iterative benchmarking framework grounded in the principles of comprehensiveness and compactness. |
| Approach: | They propose a benchmark framework that incorporates the principle of comprehensiveness and compactness. |
| Outcome: | The proposed framework is domain-agnostic and adaptable to a wide range of specialized fields. |
LLM as a metric critic for low resource relation identification (2024.findings-emnlp)
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| Challenge: | Existing studies show that small language models (SLMs) overfit in low resource situations . however, the gap between pre-training and fine-tuning leads to performance decay . |
| Approach: | They propose to combine large language models and LLM for relation identification by co-evolution . they propose to use a masked language model prompt to generate a relation identification task . |
| Outcome: | The proposed model can handle low resource relation identification tasks with minimal overfitting . the proposed model provides essential background knowledge to assist training process . |
GAIA: A Fine-grained Multimedia Knowledge Extraction System (2020.acl-demos)
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Manling Li, Alireza Zareian, Ying Lin, Xiaoman Pan, Spencer Whitehead, Brian Chen, Bo Wu, Heng Ji, Shih-Fu Chang, Clare Voss, Daniel Napierski, Marjorie Freedman
| Challenge: | Open source knowledge extraction tools are used for many real-world applications, but there is no comprehensive system for KE. |
| Approach: | They propose a multimedia knowledge extraction system that takes multimedia data from various sources and languages as input and creates a coherent, structured knowledge base. |
| Outcome: | The system achieves top performance at the recent NIST TAC SM-KBP2019 evaluation. |
SafeEraser: Enhancing Safety in Multimodal Large Language Models through Multimodal Machine Unlearning (2025.findings-acl)
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Junkai Chen, Zhijie Deng, Kening Zheng, Yibo Yan, Shuliang Liu, PeiJun Wu, Peijie Jiang, Jia Liu, Xuming Hu
| Challenge: | Existing methods for MU forget quality and model utility are not fully explored for safety in MLLMs. |
| Approach: | They propose a safety unlearning benchmark for MLLMs to measure over-forgetting . they propose MU methods to forget quality and model utility . |
| Outcome: | The proposed method reduces over-forgetting by 79.5% while maintaining forget quality and model utility. |
I Need Help! Evaluating LLM’s Ability to Ask for Users’ Support: A Case Study on Text-to-SQL Generation (2024.emnlp-main)
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| Challenge: | a new study examines the proactive ability of large language models to seek user support . without external feedback, many LLMs struggle to recognize their need for user support. |
| Approach: | They propose metrics to evaluate the trade-off between performance improvements and user burden . they also investigate whether LLMs can determine when to request user support . |
| Outcome: | The proposed metrics show that without external feedback, many LLMs struggle to recognize their need for user support. |
Beyond Length Scaling: Synergizing Breadth and Depth for Generative Reward Models (2026.findings-acl)
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| Challenge: | Recent advances in Generative Reward Models have demonstrated that scaling the length of Chain-of-Thought reasoning enhances reliability of evaluation. |
| Approach: | They propose a framework that reconfigures raw rationales into structured Breadth-CoT and Depth-Co T through a modular synthesis pipeline. |
| Outcome: | The proposed framework surpasses open-source RMs by an average of 8.2%. |
Gold-Medal-Level Olympiad Geometry Solving with Efficient Heuristic Auxiliary Constructions (2026.acl-long)
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Boyan Duan, Xiao Liang, Shuai Lu, Yaoxiang Wang, Yelong Shen, Kai-Wei Chang, Ying Nian Wu, Mao Yang, Weizhu Chen, Yeyun Gong
| Challenge: | Existing methods for geometry theorem proving in Euclidean geometry are challenging and require a neural network to perform. |
| Approach: | They propose a method for adding auxiliary points in geometry that runs on CPUs without relying on neural network-based inference. |
| Outcome: | The proposed method achieves silver-medal-level human performance on IMO-30 benchmark. |
ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering (2025.emnlp-main)
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| Challenge: | Chart question answering (CQA) is a multimodal task for evaluating the reasoning capabilities of vision-language models. |
| Approach: | They propose a chart question answering benchmark that incorporates multilingual contexts and supports open-domain textual outputs. |
| Outcome: | The proposed framework outperforms the previous three common CQA paradigms: instruction-following, OCR-enhanced, and chain-of-thought. |
IPL: Leveraging Multimodal Large Language Models for Intelligent Product Listing (2024.emnlp-industry)
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Kang Chen, Qing Zhang, Chengbao Lian, Yixin Ji, Xuwei Liu, Shuguang Han, Guoqiang Wu, Fei Huang, Jufeng Chen
| Challenge: | Unlike professional Business-to-Consumer (B2C) e-commerce platforms, consumer-to consumer (C2C), is mainly targeting individual sellers. |
| Approach: | They develop an intelligent product listing tool that generates product descriptions using various product attributes such as category, brand, color, condition, etc. |
| Outcome: | The proposed tool outperforms the base model in domain-specific tasks while producing less hallucination. |
Generalized Category Discovery with Large Language Models in the Loop (2024.findings-acl)
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Wenbin An, Wenkai Shi, Feng Tian, Haonan Lin, QianYing Wang, Yaqiang Wu, Mingxiang Cai, Luyan Wang, Yan Chen, Haiping Zhu, Ping Chen
| Challenge: | Generalized Category Discovery (GCD) is a crucial task that aims to recognize both known and novel categories from a set of unlabeled data. |
| Approach: | They propose a framework that introduces Large Language Models into the training loop to generate category names without human effort. |
| Outcome: | The proposed framework outperforms SOTA models on three benchmark datasets and generates accurate category names for the discovered clusters. |
Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model (D18-1)
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| Challenge: | Existing neural semantic parsers extract word order features while neglecting other valuable syntactic information. |
| Approach: | They propose to use syntactic graph to represent three types of syntaktic information . they then employ a graph-to-sequence model to encode the syntastic graph and decode a logical form . |
| Outcome: | The proposed model is comparable to the state-of-the-art on Jobs640, ATIS, and Geo880. |
Structure-aware Fine-tuning for Code Pre-trained Models (2024.lrec-main)
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| Challenge: | Existing CodePTMs are mainly structure-free and structurebased, but how to fine-tune them remains a challenge. |
| Approach: | They propose a plug-and-play fine-tuning method that incorporates structural knowledge into pre-trained code models. |
| Outcome: | The proposed method can benefit CodePTMs more with limited training data. |
GPT-NER: Named Entity Recognition via Large Language Models (2025.findings-naacl)
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Shuhe Wang, Xiaofei Sun, Xiaoya Li, Rongbin Ouyang, Fei Wu, Tianwei Zhang, Jiwei Li, Guoyin Wang, Chen Guo
| Challenge: | Large-scale language models (LLMs) have shown impressive ability for in-context learning with limited training data. |
| Approach: | They propose a novel sequence labeling task that transforms a sequence labeled as a text-generation task into a self-verification task that LLMs can adapt to. |
| Outcome: | The proposed model performs better on NER than supervised models on a variety of tasks . the proposed model can be easily adapted by LLMs to generate a text sequence . |
GUITester: Enabling GUI Agents for Exploratory Defect Discovery (2026.findings-acl)
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| Challenge: | Exploratory GUI testing is essential for software quality but suffers from high manual costs. |
| Approach: | They propose a framework that decouples navigation from verification via two modules . they propose 143 tasks and a GUITestBench benchmark that features 26 defects . |
| Outcome: | The proposed framework outperforms state-of-the-art benchmarks in 143 tasks and 26 defects. |
Beyond the Individual: Virtualizing Multi-Disciplinary Reasoning for Clinical Intake via Collaborative Agents (2026.findings-acl)
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Huangwei Chen, Wu Li, Junhao Jia, Yining Chen, Xiaotao Pang, Ya-Long Chen, Li Gonghui, Haishuai Wang, Jiajun Bu, Lei Wu
| Challenge: | Initial outpatient consultations are costly and difficult to scale to real-time intake. |
| Approach: | They propose a synchronous virtual MDT framework that formalizes the consultation state using a structured SOAP representation, separating evidence collection from diagnostic reasoning to improve traceability and bias control. |
| Outcome: | The proposed framework outperforms state-of-the-art models on ClinicalBench and a real-world RAPID-IPN dataset in documentation quality and consultation capability. |
LMR-BENCH: Evaluating LLM Agent’s Ability on Reproducing Language Modeling Research (2025.emnlp-main)
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Shuo Yan, Ruochen Li, Ziming Luo, Zimu Wang, Daoyang Li, Liqiang Jing, Kaiyu He, Peilin Wu, Juntong Ni, George Michalopoulos, Yue Zhang, Ziyang Zhang, Mian Zhang, Zhiyu Chen, Xinya Du
| Challenge: | Large language model (LLM) agents have demonstrated remarkable potential in advancing scientific discovery, but their capability in reproducing code from research papers remains underexplored. |
| Approach: | They propose to evaluate LLM agents' ability to reproduce scientific research papers by analyzing code reproduction tasks from 23 research papers published in top-tier NLP venues. |
| Outcome: | The proposed benchmark systematically evaluates the capability of large language model (LLM) agents on code reproduction from Language Modeling Research. |
Large Language Models Can Be Contextual Privacy Protection Learners (2024.emnlp-main)
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Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Quanquan Gu, Haifeng Chen, Wei Wang, Wei Cheng
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable linguistic comprehension and generation capability, but when applied to specialized industries, they face challenges such as hallucination, insufficient domain knowledge, and failing to incorporate the latest domain knowledge. |
| Approach: | They propose a paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding inference-time data privacy. |
| Outcome: | The proposed model protects private data while enhancing the model's knowledge. |
Your Reasoning Model Knows What Counts: Self-Guided Chain-of-Thought Pruning for Efficient Reasoning (2026.acl-long)
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| Challenge: | Existing approaches to Chain-of-Thought reasoning are often degraded because they disregard the model’s intrinsic reasoning dependency. |
| Approach: | They propose a self-guided pruning framework that leverages the model’s intrinsic likelihood landscape to identify segments that are extraneous to its specific reasoning pattern. |
| Outcome: | The proposed framework reduces output length while maintaining or improving accuracy on multiple benchmarks. |
Adversarial Metric Learning for Fine-Grained Emotion Classification (2026.acl-long)
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| Challenge: | Recent advances in fine-grained emotion classification relied on contrastive learning with hard-pair mining. |
| Approach: | They propose an adversarial metric learning framework that replaces fixed similarity metrics with a learnable metric family and trains representations to remain discriminative under worst-case similarity distortions. |
| Outcome: | The proposed framework trains a pairwise discriminator to maximally confuse two hard pair types while training the encoder to remain discriminative under worst-case similarity distortions. |
D4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat (2022.emnlp-main)
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| Challenge: | Existing human-machine dialogue systems are not able to provide diagnostic information for depression diagnosis due to stigma associated with mental illness. |
| Approach: | They propose to construct a Chinese Dialogue Dataset for depression-diagnosis-oriented chat based on clinical depression diagnostic criteria. |
| Outcome: | The proposed system can be used to diagnose depression using a Chinese Dialogue Dataset. |
DuQM: A Chinese Dataset of Linguistically Perturbed Natural Questions for Evaluating the Robustness of Question Matching Models (2022.emnlp-main)
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| Challenge: | a comprehensive evaluation of QM models should be conducted on natural texts, not on artificial adversarial examples . ral models are often not robust to adversarials, which means they predict unexpected outputs . |
| Approach: | They use a Chinese dataset to evaluate the robustness of QM models . they show that the effect of artificial adversarial examples does not work on natural texts . |
| Outcome: | The proposed model is more robust than other models on natural questions with 32 linguistic perturbations. |
Icon2: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation (2025.emnlp-main)
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Qiyuan Chen, Hongsen Huang, Qian Shao, Jiahe Chen, Jintai Chen, Hongxia Xu, Renjie Hua, Ren Chuan, Jian Wu
| Challenge: | Large Language Models (LLMs) require high quality preference datasets to align with human preferences. |
| Approach: | They propose a framework that leverages inherent regulation of LLMs’ representation space for efficient and tailored preference dataset construction, named Icon2. |
| Outcome: | The proposed framework improves performance on benchmarks like AlpacaEval 2.0 and Arena-Hard while reducing computational costs by up to 48.1%. |
A Survey of Inductive Reasoning for Large Language Models (2026.acl-long)
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Kedi Chen, Dezhao Ruan, Yuhao Dan, Yaoting Wang, Siyu Yan, Xuecheng Wu, Yinqi Zhang, Qin Chen, Jie Zhou, Liang He, Biqing Qi, Linyang Li, Qipeng Guo, Xiaoming Shi, Wei Zhang
| Challenge: | Inductive reasoning is an important task for large language models (LLMs). |
| Approach: | They propose a survey of inductive reasoning for large language models . they categorize methods into three main areas: post-training enhancement, test-time exploration, and data augmentation. |
| Outcome: | The proposed method improves inductive reasoning in large language models. |
Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations (2024.acl-long)
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| Challenge: | Existing methods for process-oriented math reward models rely on manual annotation. |
| Approach: | They propose a process-oriented math process reward model called Math-shepherd which assigns a reward score to each step of math problem solutions. |
| Outcome: | The proposed model breaks the bottleneck of manual supervision in two scenarios. |
ALLabel: Three-stage Active Learning for LLM-based Entity Recognition using Demonstration Retrieval (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) are increasingly used to solve the entity recognition task. |
| Approach: | They propose a framework to select the most informative and representative samples for LLM in-context learning. |
| Outcome: | The proposed framework outperforms baselines on three specialized domain datasets. |
Graph Enhanced Cross-Domain Text-to-SQL Generation (D19-53)
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| Challenge: | Existing deep learning approaches for semantic parsing do not generalize to unseen data sets . existing benchmarks have shown text-to-SQL parsers do not generally perform well to unsen SQL queries. |
| Approach: | They propose a new cross-domain learning scheme to perform text-to-SQL translation . they demonstrate its use on a large-scale cross- domain text- to-Sql data set Spider . |
| Outcome: | The proposed learning scheme improves on a large-scale text-to-SQL data set. |
Weights-Rotated Preference Optimization for Large Language Models (2025.emnlp-main)
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Chenxu Yang, Ruipeng Jia, Mingyu Zheng, Naibin Gu, Zheng Lin, Siyuan Chen, Weichong Yin, Hua Wu, Weiping Wang
| Challenge: | Existing methods to align large language models with high reward hacking are limited by the complexity of the parameter space and the complexity. |
| Approach: | They propose a weights-rotated preference optimization algorithm that constrains the output layer logits with the KL divergence inherited from DPO and fine-tunes the intermediate hidden states. |
| Outcome: | The proposed algorithm achieves a 3.27-point improvement on AlpacaEval 2 and surpasses the best baseline by 6.2 to 7.5 points on MT-Bench with merely 0.015% of the trainable parameters. |
LLMs as Collaborator: Demands-Guided Collaborative Retrieval-Augmented Generation for Commonsense Knowledge-Grounded Open-Domain Dialogue Systems (2024.findings-emnlp)
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| Challenge: | Existing methods to capture the unique knowledge demands of LLMs are unsatisfactory because of their overestimation and lack of knowledge. |
| Approach: | They propose a novel approach to capture the unique knowledge demands for each dialogue context using CoT and RAG methods. |
| Outcome: | The proposed model can capture the unique knowledge demands for each dialogue context and bring higher-quality responses. |
GUI Agents: A Survey (2025.findings-acl)
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Dang Nguyen, Jian Chen, Yu Wang, Gang Wu, Namyong Park, Zhengmian Hu, Hanjia Lyu, Junda Wu, Ryan Aponte, Yu Xia, Xintong Li, Jing Shi, Hongjie Chen, Viet Dac Lai, Zhouhang Xie, Sungchul Kim, Ruiyi Zhang, Tong Yu, Mehrab Tanjim, Nesreen K. Ahmed, Puneet Mathur, Seunghyun Yoon, Lina Yao, Branislav Kveton, Jihyung Kil, Thien Huu Nguyen, Trung Bui, Tianyi Zhou, Ryan A. Rossi, Franck Dernoncourt
| Challenge: | Large Foundation Models (LFMs) have transformed the landscape of AI research and day-to-day life. |
| Approach: | They propose a framework that delineates GUI agents' perception, reasoning, planning, and acting capabilities. |
| Outcome: | The proposed framework delineates their perception, reasoning, planning, and acting capabilities. |
MirrorCAPTCHA: Wild CAPTCHA, Wild Distribution, Wild Web-based Platform Meet Multimodal LLM Agents (2026.acl-long)
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Xiangyu Wu, Yuwei Hu, Tianyu Cui, Yueying Tian, Qing-Guo Chen, Zhao Xu, Weihua Luo, Kaifu Zhang, Yang Yang, Jianfeng Lu
| Challenge: | Existing agent benchmarks fail to evaluate an agent's real-world capacity to handle CAPTCHA . Existing benchmarks ignore this practical challenge, failing to evaluate agents' ability to handle complex visual CAPTchas. |
| Approach: | They propose a benchmark annotated with Weighted Pass Rate and a new metric to measure agent's ability to handle CAPTCHA. |
| Outcome: | The proposed benchmark outperforms current state-of-the-art closed-source models on mirrorCAPTCHA and achieves 9.4% higher average weighted pass rate and 2.13% higher average Completion degree. |
Target-oriented Opinion Words Extraction with Target-fused Neural Sequence Labeling (N19-1)
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| Challenge: | Opinion target extraction and opinion words extraction are two fundamental subtasks in Aspect Based Sentiment Analysis (ABSA). |
| Approach: | They propose a new subtask for Aspect Based Sentiment Analysis to extract opinion words as pairs from a given opinion target. |
| Outcome: | The proposed model outperforms existing methods significantly on several popular ABSA benchmarks. |
QuantumQA: Enhancing Scientific Reasoning via Physics-Consistent Dataset and Verification-Aware Reinforcement Learning (2026.acl-long)
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Songxin Qu, Tai-Ping Sun, Yun-Jie Wang, Huan-Yu Liu, Cheng Xue, Xiao-Fan Xu, Han Fang, Yang Yang, Yu-Chun Wu, Guo-Ping Guo, Zhao-Yun Chen
| Challenge: | Large language models lack reliability in scientific domains that require strict adherence to physical constraints. |
| Approach: | They propose a large-scale dataset constructed via a task-adaptive strategy and a hybrid verification protocol that combines deterministic solvers with semantic auditing to guarantee scientific rigor. |
| Outcome: | The proposed model outperforms baselines and general-purpose preference models and is competitive with proprietary models. |
Combining the Best of Both Worlds: A Method for Hybrid NMT and LLM Translation (2025.findings-acl)
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Zhanglin Wu, Daimeng Wei, Xiaoyu Chen, Hengchao Shang, Jiaxin Guo, Zongyao Li, Yuanchang Luo, Jinlong Yang, Zhiqiang Rao, Hao Yang
| Challenge: | Large language models have advantages over neural machine translation systems, but they suffer from high computational costs and significant latency. |
| Approach: | They propose a scheduling policy that optimizes translation result while ensuring fast speed and as little LLM usage as possible. |
| Outcome: | The proposed model achieves optimal translation performance with less LLM usage on multilingual test sets. |
Discord Questions: A Computational Approach To Diversity Analysis in News Coverage (2022.findings-emnlp)
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| Challenge: | Modern news aggregators do the hard work of organizing the news, but choosing which source to read remains challenging. |
| Approach: | They propose a framework to help readers identify source differences and gain an understanding of news coverage diversity by generating questions with a diverse answer pool and reusing existing methods. |
| Outcome: | The proposed framework improves performance from current question generation methods by 5% and achieves 81% balanced accuracy on a realistic test set. |
Embracing Imperfection: Simulating Students with Diverse Cognitive Levels Using LLM-based Agents (2025.acl-long)
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| Challenge: | Large language models (LLMs) are becoming increasingly popular in education, enabling researchers to simulate students' learning patterns and learning patterns. |
| Approach: | They propose a training-free framework for student simulation that takes into account student cognitive diversity and realism. |
| Outcome: | The proposed model outperforms baseline models and achieves 100% improvement in simulation accuracy and realism. |
Can Large Language Models Tackle Graph Partitioning? (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have remarkable capabilities in understanding complex tasks, but they can only handle graph partitioning tasks that require global perception abilities. |
| Approach: | They propose a pipeline for coarsening, reasoning, and refining to enable LLMs to perform graph partitioning on small-scale graphs. |
| Outcome: | The proposed pipeline can handle graph partitioning tasks on small graphs with coarsening, reasoning, and refining. |
The Dominance of Text Space: Unveiling the Asymmetric Nature of Cross-Modal Alignment in Large Language Models (2026.acl-long)
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| Challenge: | Existing methods for cross-modal alignment assume a symmetric interaction between visual and textual modalities, implying that both spaces adapt to each other. |
| Approach: | They propose a method that regularizes the projector to maintain the geometric structure of the text embedding space via spectral filtering. |
| Outcome: | The proposed method preserves the LLM’s inherent linguistic capabilities and reduces object hallucination significantly better than standard fine-tuning methods. |
Chain of Methodologies: Scaling Test Time Computation without Training (2025.findings-acl)
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| Challenge: | Existing prompts for complex reasoning tasks are limited to specific tasks with few-shot examples due to constraints like context length and information extraction accuracy. |
| Approach: | They propose a method to build structured reasoning processes by injecting human insights into LLMs' training data. |
| Outcome: | The proposed framework outperforms baselines in the analysis of large language models. |
Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction (2022.coling-1)
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| Challenge: | Existing methods for ECPE fail to model specific features and interactive features in between, or suffer from inconsistency of label prediction. |
| Approach: | They propose to align ECPE with a feature-task alignment mechanism to model emotion-&cause-specific features and the shared interactive feature. |
| Outcome: | The proposed model outperforms existing systems on all ECA subtasks. |
MIND: A Large-scale Dataset for News Recommendation (2020.acl-main)
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Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu, Ming Zhou
| Challenge: | Personalized news recommendation is an important technique for personalized news service. |
| Approach: | They propose to build a large-scale news recommendation dataset from Microsoft News . they demonstrate that news recommendation relies on the quality of news content understanding . |
| Outcome: | The proposed dataset contains 1 million users and more than 160k English news articles, each of which has rich textual content such as title, abstract and body. |
A Semantic Uncertainty Sampling Strategy for Back-Translation in Low-Resources Neural Machine Translation (2025.acl-srw)
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Yepai Jia, Yatu Ji, Xiang Xue, Shilei@imufe.edu.cn Shilei@imufe.edu.cn, Qing-Dao-Er-Ji Ren, Nier Wu, Na Liu, Chen Zhao, Fu Liu
| Challenge: | Back-translation methods rely on large-scale parallel corpora to enhance performance, but ignore the semantic quality of monolingual data. |
| Approach: | They propose a method which prioritizes sentences with higher semantic uncertainty as training samples by computationally evaluating the complexity of unannotated monolingual data. |
| Outcome: | The proposed method improves translation accuracy and fluency by +1.7 on all three translation tasks. |
Dancing in Chains: Reconciling Instruction Following and Faithfulness in Language Models (2024.emnlp-main)
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Zhengxuan Wu, Yuhao Zhang, Peng Qi, Yumo Xu, Rujun Han, Yian Zhang, Jifan Chen, Bonan Min, Zhiheng Huang
| Challenge: | Modern language models fail to follow human instructions while being faithful . a trade-off exists between instruction following and faithfulness when training LMs . |
| Approach: | They propose a method that relies on Reject-sampling by Self-instruct with Continued Fine-tuning to train LMs to follow human instructions while being faithful. |
| Outcome: | The proposed method outperforms vanilla MTL with high-quality data, but with significantly smaller data. |
FreeAL: Towards Human-Free Active Learning in the Era of Large Language Models (2023.emnlp-main)
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| Challenge: | Modern machine learning models require a huge collection of precisely labeled data, which can be labor-intensive and time-consuming. |
| Approach: | They propose a collaborative learning framework that interactively distills and filters the task-specific knowledge from LLMs. |
| Outcome: | The proposed framework improves zero-shot performance on eight benchmark datasets without human supervision. |
Can LLMs Evaluate Complex Attribution in QA? Automatic Benchmarking using Knowledge Graphs (2025.acl-long)
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Nan Hu, Jiaoyan Chen, Yike Wu, Guilin Qi, Hongru Wang, Sheng Bi, Yongrui Chen, Tongtong Wu, Jeff Z. Pan
| Challenge: | Attributed Question Answering (AQA) has attracted wide attention, but there are several limitations in evaluating the attributions. |
| Approach: | They propose a large-scale benchmark containing comprehensive attribution categories . they compare 25 automatic evaluators with human evaluers and tested LLM evalators . |
| Outcome: | The proposed method can compare attributions with subtle differences and provide feedback to improve them. |
Hit the Sweet Spot! Span-Level Ensemble for Large Language Models (2025.coling-main)
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| Challenge: | a recent study focused on sample-level and token-level ensembles, which hinder dynamic correction and enhancement of outputs during the generation process. |
| Approach: | They propose a span-level ensemble method that balances real-time adjustments and accurate ensemble decisions. |
| Outcome: | The proposed method improves performance across language generation tasks significantly. |
MAPLE: Enhancing Review Generation with Multi-Aspect Prompt LEarning in Explainable Recommendation (2025.acl-long)
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Ching-Wen Yang, Zhi-Quan Feng, Ying-Jia Lin, Che Wei Chen, Kun-da Wu, Hao Xu, Yao Jui-Feng, Hung-Yu Kao
| Challenge: | Existing models that generate generic aspects do not provide personalized informative recommendations. |
| Approach: | They propose a model that integrates aspect category as another input dimension to facilitate memorizing fine-grained aspect terms. |
| Outcome: | The proposed model outperforms baseline model on restaurant review datasets in the restaurant domain. |
Rethinking Pragmatics in Large Language Models: Towards Open-Ended Evaluation and Preference Tuning (2024.emnlp-main)
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| Challenge: | Existing methods to assess social-pragmatic inference in large language models are inadequacy, and preferential tuning is the best approach. |
| Approach: | They propose to use free-form models' responses as a measure to assess social-pragmatic reasoning and advocate for preference optimization over supervised finetuning (SFT). |
| Outcome: | The proposed model outperforms supervised finetuning (SFT) and offers a near-free launch in pragmatic abilities without compromising general capabilities. |
Development and Validation of a Corpus for Machine Humor Comprehension (2020.lrec-1)
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| Challenge: | a Chinese humor corpus was labeled with five levels of funniness, eight skill sets of humor, and six dimensions of intent by only one annotator. |
| Approach: | They develop a Chinese humor corpus with 3,365 jokes labeled with five levels of funniness, eight skill sets of humor, and six dimensions of intent by only one annotator. |
| Outcome: | The proposed corpus contains 3,365 jokes from over 40 sources. |
MRAG: A Modular Retrieval Framework for Time-Sensitive Question Answering (2025.findings-emnlp)
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| Challenge: | Existing methods for answering time-sensitive questions lack temporal reasoning . existing methods struggle with these time-intensive questions, authors say . |
| Approach: | They propose a temporal-based question-answering framework that integrates temporal perturbations and gold evidence labels into a question processing framework. |
| Outcome: | The proposed framework outperforms baseline retrieval methods in retrieval performance. |
Sparse Growing Transformer: Training-Time Sparse Depth Allocation via Progressive Attention Looping (2026.findings-acl)
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Yao Chen, Yilong Chen, Yinqi Yang, Junyuan Shang, Zhenyu Zhang, Zefeng Zhang, Shuaiyi Nie, Shuohuan Wang, Yu Sun, Hua Wu, Haifeng Wang, Tingwen Liu
| Challenge: | Existing approaches to increasing effective depth of LLMs rely on parameter reuse, extending computation through recursive execution. |
| Approach: | They propose a training-time sparse depth allocation framework that progressively increases depth for a small subset of parameters as training evolves. |
| Outcome: | The proposed model outperforms existing approaches to increasing the effective depth of language models while reducing training FLOPs overhead from approximately 16–20% to only 1–3% relative to a standard Transformer backbone. |
Learning Representation Mapping for Relation Detection in Knowledge Base Question Answering (P19-1)
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| Challenge: | Existing approaches to detect relation detection only get high accuracy for questions whose relations have been seen in training data. |
| Approach: | They propose a method to learn representation mapping for both seen and unseen relations based on previously learned relation embedding. |
| Outcome: | The proposed method improves the performance of unseen relations while keeping the performance comparable to the state-of-the-art. |
MRT: Multi-modal Short- and Long-range Temporal Convolutional Network for Time-sync Comment Video Behavior Prediction (2024.lrec-main)
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| Challenge: | Using time-sync comments, it is difficult to understand user behavior due to complexity of interactions between users, videos, and comments. |
| Approach: | They propose a novel time-sync comment behavior prediction model that takes historical behavior into account and optimizes it on the basis of user preferences. |
| Outcome: | The proposed model improves the performance of time-sync comments on visual frames and textual comments on two cats playing simultaneously. |
End-to-end Spoken Conversational Question Answering: Task, Dataset and Model (2022.findings-naacl)
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| Challenge: | Existing methods for conversational question answering significantly degrade on datasets . a new task aims to enable systems to model complex dialogues flow given the speech documents . |
| Approach: | They propose a new Spoken Conversational Question Answering task to model human conversations . they propose DDNet, which ingests cross-modal information to achieve fine-grained representations of speech and language modalities. |
| Outcome: | The proposed method achieves superior performance in spoken conversational question answering. |
Chinese Metaphorical Relation Extraction: Dataset and Models (2023.findings-emnlp)
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| Challenge: | Metaphor identification is a core task in metaphor processing, which involves recognizing and analyzing metaphorical expressions in text. |
| Approach: | They propose a new formulation of metaphor identification as a relation extraction problem . they use a dataset to analyze metaphorical relations between two spans, a target and a source . |
| Outcome: | The proposed model can capture the properties of the target and source in Chinese sentences. |
Relaxing the Constraints: A Dual-Importance Projection Mechanism for Lifelong Model Editing (2026.findings-acl)
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Zhenghai Chen, Senbin Xu, Jiaxi Tan, Xinhua Wu, Yan Zhang, Xiawu Zheng, Shengchuan Zhang, Ke Li, Sicheng Zhao, Liujuan Cao, Rongrong Ji
| Challenge: | Existing knowledge editing methods rely on strict orthogonal projection to preserve previously edited knowledge, but this constraint limits gradient expressiveness, resulting in degradation of model generalization and overall performance as the number of edits increases. |
| Approach: | They propose a method that leverages Singular Value Decomposition to identify critical gradient subspaces and introduces a dual mechanism comprising "accumulated importance" and "projection importance" |
| Outcome: | Extensive experiments on five mainstream LLMs show that the proposed method achieves an average comprehensive performance improvement of 10.36% and effectively maintains the model’s general capabilities on downstream tasks. |
Can MLLMs Reason Beyond Language? VisReason: A Comprehensive Benchmark for Vision-Centric Reasoning (2026.findings-acl)
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| Challenge: | Recent advances in multimodal large language models demonstrate strong performance on visual reasoning benchmarks. |
| Approach: | They propose a benchmark for vision-centric reasoning that integrates visual and textual information for non-trivial reasoning. |
| Outcome: | The proposed benchmark exposes gaps between humans and current MLLMs and reveals limited benefits from test-time reasoning strategies. |
OS Agents: A Survey on MLLM-based Agents for Computer, Phone and Browser Use (2025.acl-long)
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Xueyu Hu, Tao Xiong, Biao Yi, Zishu Wei, Ruixuan Xiao, Yurun Chen, Jiasheng Ye, Meiling Tao, Xiangxin Zhou, Ziyu Zhao, Yuhuai Li, Shengze Xu, Shenzhi Wang, Xinchen Xu, Shuofei Qiao, Zhaokai Wang, Kun Kuang, Tieyong Zeng, Liang Wang, Jiwei Li, Yuchen Eleanor Jiang, Wangchunshu Zhou, Guoyin Wang, Keting Yin, Zhou Zhao, Hongxia Yang, Fan Wu, Shengyu Zhang, Fei Wu
| Challenge: | a new generation of (M)LLMs is enabling the creation of superintelligent AI assistants . OS Agents can complete tasks autonomously and have the potential to significantly enhance the lives of billions of users worldwide. |
| Approach: | They propose to build OS Agents that operate within operating systems' GUIs and GUIs . they examine evaluation metrics and benchmarks to identify promising directions . |
| Outcome: | The proposed agents are based on operating systems (OS) and operating systems frameworks. |
Learning Dynamic Context Augmentation for Global Entity Linking (D19-1)
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Xiyuan Yang, Xiaotao Gu, Sheng Lin, Siliang Tang, Yueting Zhuang, Fei Wu, Zhigang Chen, Guoping Hu, Xiang Ren
| Challenge: | Existing collective entity linking methods are expensive and often lack local context information. |
| Approach: | They propose a dynamic context-augmented inference model that can be used to make collective inference. |
| Outcome: | The proposed model can cope with different local EL models with different learning settings, base models, decision orders and attention mechanisms. |
Exploring the Choice Behavior of Large Language Models (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) are increasingly being adopted across various domains where they help to make choices. |
| Approach: | They construct a virtual QA platform that includes three different experimental conditions, with four models from GPT and Llama series participating in repeated experiments. |
| Outcome: | The proposed model includes three experimental conditions and four models from GPT and Llama series. |
ConceptMath: A Bilingual Concept-wise Benchmark for Measuring Mathematical Reasoning of Large Language Models (2024.findings-acl)
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Yanan Wu, Jie Liu, Xingyuan Bu, Jiaheng Liu, Zhanhui Zhou, Yuanxing Zhang, Chenchen Zhang, ZhiqiBai ZhiqiBai, Haibin Chen, Tiezheng Ge, Wanli Ouyang, Wenbo Su, Bo Zheng
| Challenge: | ConceptMath evaluates concept-wise mathematical reasoning of Large Language Models (LLMs) Existing benchmarks that evaluate general mathematical reasoning with an average accuracy fail to probe the fine-grained failure modes of mathematical reasoning on specific datasets. |
| Approach: | They introduce a bilingual, fine-grained benchmark that evaluates concept-wise mathematical reasoning of Large Language Models. |
| Outcome: | The proposed benchmarks evaluate concept-wise mathematical reasoning of Large Language Models with concept-based accuracies. |
APEX: Learning Adaptive Priorities for Multi-Objective Alignment in Vision-Language Generation (2026.findings-acl)
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Dongliang Chen, Xinlin Zhuang, Junjie Xu, Luojian Xie, Zehui Wang, Jiaxi Zhuang, Haolin Yang, Liang Dou, Xiao He, Xingjiao Wu, Ying Qian
| Challenge: | APEX optimizes for text-to-image generation by combining learning potential, conflict penalty, and progress need. |
| Approach: | They propose an algorithm that stabilizes heterogeneous rewards and dynamically schedules objectives . they propose a method that achieves better Pareto trade-offs across four heterogenous objectives based on P3 Adaptive Priorities . |
| Outcome: | The proposed algorithm achieves better pareto trade-offs across four heterogeneous objectives while maintaining competitive OCR accuracy. |
DeCoT: Debiasing Chain-of-Thought for Knowledge-Intensive Tasks in Large Language Models via Causal Intervention (2024.acl-long)
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| Challenge: | In large language models, external knowledge is required to augment their internal knowledge through prompts, but this does not guarantee that LLMs can identify and use relevant information in the prompts to conduct chain-of-thought reasoning. |
| Approach: | They propose a structural causal model to formally explain the internal knowledge bias of large language models (LLMs) they review the chain-of-thought (CoT) prompting from a causal perspective and find that biased information from pretrained models can impair LLMs’ reasoning abilities. |
| Outcome: | The proposed model enables more accurate CoT reasoning and enhances LLM generation on knowledge-intensive tasks. |
Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models (2024.emnlp-main)
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| Challenge: | Existing studies on parameter-efficient fine-tuning (PEFT) for dense-architecture LLMs are lacking. |
| Approach: | They propose an expert-specialized fine-tuning method that tunes the experts most relevant to downstream tasks while freezing the other experts. |
| Outcome: | The proposed method matches or surpasses full-parameter fine-tuning. |
Prompt-based Connective Prediction Method for Fine-grained Implicit Discourse Relation Recognition (2022.findings-emnlp)
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| Challenge: | Existing methods to aid implicit discourse relation recognition (IDRR) lack explicit connectives and are difficult to implement on fine-grained IDRR. |
| Approach: | They propose a Prompt-based Connective Prediction method that instructs large-scale pre-trained models to use knowledge relevant to discourse relation and utilizes strong correlation between connectives and discourse relation to help the model recognize implicit discourse relations. |
| Outcome: | The proposed method surpasses the state-of-the-art model and achieves significant improvements on those fine-grained few-shot discourse relation classes. |
IDEA: Enhancing the Rule Learning Ability of Large Language Model Agent through Induction, Deduction, and Abduction (2025.findings-acl)
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| Challenge: | RULEARN is a benchmark to assess the rule-learning abilities of large language models (LLMs) in interactive environments. |
| Approach: | They propose a framework that integrates the process of **I**nduction, **De**duction, and **A**bduction. |
| Outcome: | The proposed framework improves on the baseline and human-like rule learning in real-world scenarios. |
Explainable and Sparse Representations of Academic Articles for Knowledge Exploration (2020.coling-main)
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| Challenge: | a system for summarizing academic articles by concept tagging has shown great coverage and high accuracy of concept identification. |
| Approach: | They propose to transform tagged concepts into sparse vectors as representations of academic documents. |
| Outcome: | The proposed system can be applied to a broader class of applications. |
Shallow Focus, Deep Fixes: Enhancing Shallow Layers Vision Attention Sinks to Alleviate Hallucination in LVLMs (2025.emnlp-main)
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Xiaofeng Zhang, Yihao Quan, Chen Shen, Chaochen Gu, Xiaosong Yuan, Shaotian Yan, Jiawei Cao, Hao Cheng, Kaijie Wu, Jieping Ye
| Challenge: | Multimodal large language models (MLLMs) demonstrate excellent abilities for understanding visual information, but the hallucination remains a challenging problem. |
| Approach: | They propose a training-free approach to enhance vision attention sinks to facilitate convergence of the image token attention sink within shallow layers. |
| Outcome: | The proposed approach improves the convergence of the image token attention sink within shallow layers and strengthens the layer’s focus on the image itself. |
Search Wisely: Mitigating Sub-optimal Agentic Searches By Reducing Uncertainty (2025.emnlp-main)
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| Challenge: | Recent advances in Large Language Models (LLMs) have propelled their use in informationintensive tasks such as question answering and knowledge synthesis. |
| Approach: | They propose a reinforcement learning-based training method that incorporates confidence thresholds to reward high-certainty search decisions. |
| Outcome: | The proposed method outperforms baseline models on seven QA benchmarks and demonstrates that it is more efficient than existing methods. |
AdvPicker: Effectively Leveraging Unlabeled Data via Adversarial Discriminator for Cross-Lingual NER (2021.acl-long)
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| Challenge: | Named entity recognition models rely on expensive labeled data for training, which is not always available across languages. |
| Approach: | They propose an adversarial approach where an encoder learns entity domain knowledge from labeled source-language data and better shared features are captured via adversarially trained discriminators. |
| Outcome: | The proposed approach outperforms existing state-of-the-art methods on standard benchmark datasets and outperformed existing methods on the target language. |
Equal Truth: Rumor Detection with Invariant Group Fairness (2025.findings-emnlp)
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| Challenge: | Existing rumor detection methods rarely consider fairness issues inherent in the model . this can lead to biased predictions across stakeholder groups, undermining their detection effectiveness . |
| Approach: | They propose a framework to address fairness issues inherent in rumor detection models . they perform unsupervised partitioning to dynamically identify potential unfair data patterns . then, they apply invariant learning to these partitions to extract fair and informative feature representations . |
| Outcome: | The proposed method outperforms strong baselines regarding detection and fairness performance . it also shows robust performance on out-of-distribution samples . |
From “Thinking” to “Justifying”: Aligning High-Stakes Explainability with Professional Communication Standards (2026.findings-acl)
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| Challenge: | Existing explanations for large language models (LLMs) need to be able to verify outputs. |
| Approach: | They propose a method that constrains output communication to present a conclusion before its structured justification. |
| Outcome: | The proposed approach achieves 83.9% accuracy and correctness over CoT. |
TeachMaster: Generative Teaching via Code (2026.acl-industry)
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Yuheng Wang, Runde Yang, Lin Wu, Jie Zhang, Jingru Fan, Tianle Zhou, Ruoyu Fu, Huatao Li, Ruijie Shi, Siheng Chen, Weinan E, Chen Qian
| Challenge: | Existing methods for creating video content are limited by high costs and slow update cycles. |
| Approach: | They propose a paradigm shifting educators from manual creators to high-level directors who focus on pedagogical intents while agents handle execution. |
| Outcome: | The proposed framework reduces production costs to 0.3% of traditional course videos and provides a robust solution for scalable education. |
SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement (2025.acl-short)
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Runnan Fang, Xiaobin Wang, Yuan Liang, Shuofei Qiao, Jialong Wu, Zekun Xi, Ningyu Zhang, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen
| Challenge: | Using Large Language Models (LLMs)-based agents can enhance their understanding of environments and tasks. |
| Approach: | They propose a framework that allows agents to synthesize possible scenarios with multi-step action invocation within the action space and perform Monte Carlo Tree Search exploration to refine their action knowledge in the current environment. |
| Outcome: | The proposed framework synthesizes possible scenarios with multi-step action invocation within the action space and performs Monte Carlo Tree Search exploration to refine action knowledge in the current environment. |
LAGCL4Rec: When LLMs Activate Interactions Potential in Graph Contrastive Learning for Recommendation (2025.findings-emnlp)
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Leqi Zheng, Chaokun Wang, Canzhi Chen, Jiajun Zhang, Cheng Wu, Zixin Song, Shannan Yan, Ziyang Liu, Hongwei Li
| Challenge: | Traditional contrastive learning methods treat negative feedback as equally hard or easy, ignoring informative semantic difficulty during training. |
| Approach: | They propose a framework leveraging Large Language Models to Activate interactions in Graph Contrastive Learning for Recommendation. |
| Outcome: | The proposed framework outperforms state-of-the-art benchmarks on multiple benchmarks. |
Layer-Aware Task Arithmetic: Disentangling Task-Specific and Instruction-Following Knowledge (2025.findings-emnlp)
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Yan-Lun Chen, Yi-Ru Wei, Chia-Yi Hsu, Chia-Mu Yu, Chun-Ying Huang, Ying-Dar Lin, Yu-Sung Wu, Wei-Bin Lee
| Challenge: | Large language models (LLMs) demonstrate strong task-specific capabilities through fine-tuning, but merging multiple fine- tuned models often leads to degraded performance due to overlapping instruction-following components. |
| Approach: | They propose a layer-wise approach that assigns layer-specific weights to task vectors based on their alignment with instruction-following or task-specific components. |
| Outcome: | The proposed approach outperforms existing methods in learning and forgetting tasks while preserving overall model utility. |
LLM Prompt Duel Optimizer: Efficient Label-Free Prompt Optimization (2026.findings-acl)
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Yuanchen Wu, Saurabh Verma, Justin Lee, Fangzhou Xiong, Poppy Zhang, Amel Awadelkarim, Xu Chen, Yubai Yuan, Shawndra Hill
| Challenge: | Large language models (LLMs) are highly sensitive to prompts, but most automatic prompt optimization methods assume access to ground-truth references that are costly to obtain. |
| Approach: | They propose a sample-efficient framework for label-free prompt optimization based on pairwise preference feedback from an LLM judge. |
| Outcome: | Experiments on BIG-bench Hard and MS MARCO show that the proposed framework identifies stronger prompts than label-free baselines while offering favorable quality–cost trade-offs. |
CPRM: A LLM-based Continual Pre-training Framework for Relevance Modeling in Commercial Search (2025.naacl-industry)
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Kaixin Wu, Yixin Ji, Zeyuan Chen, Qiang Wang, Cunxiang Wang, Hong Liu, Baijun Ji, Xu Jia, Zhongyi Liu, Jinjie Gu, Yuan Zhou, Linjian Mo
| Challenge: | Relevance modeling between queries and items is a key component of commercial search engines. |
| Approach: | They propose a framework for continual pre-training of LLMs to enhance domain knowledge . they employ queries and multi-field item to jointly pre-train for enhancing domain knowledge. |
| Outcome: | The proposed model achieves convincing performance compared to strong baselines. |
Dr3: Ask Large Language Models Not to Give Off-Topic Answers in Open Domain Multi-Hop Question Answering (2024.lrec-main)
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Yuan Gao, Yiheng Zhu, Yuanbin Cao, Yinzhi Zhou, Zhen Wu, Yujie Chen, Shenglan Wu, Haoyuan Hu, Xinyu Dai
| Challenge: | Open Domain Multi-Hop Question Answering (ODMHQA) is one of the most challenging tasks in Natural Language Processing (NLP) |
| Approach: | They propose a mechanism that leverages the intrinsic capabilities of Large Language Models to judge whether the generated answers are off-topic. |
| Outcome: | The proposed method reduces the occurrence of off-topic answers by nearly 13%, improving the performance in Exact Match (EM) by nearly 3% compared to the baseline method without the Dr3 mechanism. |
Thinking Beyond the Local: Multi-View Instructed Adaptive Reasoning in KG-Enhanced LLMs (2026.findings-eacl)
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| Challenge: | Existing methods for large language models adopt query-driven iterative reasoning from a local perspective, limiting efficiency and accuracy for complex multi-hop tasks. |
| Approach: | They propose a multi-view instructed adaptive reasoning of LLM on Knowledge Graphs that allows LLMs to plan, evaluate, and adapt reasoning paths from a global perspective. |
| Outcome: | The proposed model overcomes the limitations of local exploration by enabling LLMs to plan, evaluate, and adapt reasoning paths from a global perspective. |
Modelling Variability in Human Annotator Simulation (2024.findings-acl)
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| Challenge: | Human annotator simulation (HAS) is a cost-effective alternative to human evaluation tasks. |
| Approach: | They propose a framework to model human annotation variability via meta-learning . conditional softmax flow model leverages diverse human annotations via meta learning . results demonstrate that method can predict aggregated behaviours of human annotators . |
| Outcome: | The proposed method achieves state-of-the-art performance on two real-world human evaluation tasks: emotion recognition and toxic speech detection. |
Retrospective Learning from Interactions (2025.acl-long)
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| Challenge: | Multi-turn interactions between large language models and users naturally include implicit feedback signals. |
| Approach: | They propose a method to learn from feedback signals in past interactions without annotations . they use a multimodal LLM to solve a reasoning task with a combinatorial solution space . |
| Outcome: | The proposed method improves task completion rate from 31% to 82% without annotations. |
Revealing Procedural Reasoning Structures in Chain-of-Thought Training via Span-Level Gradient Organization (2026.acl-long)
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| Challenge: | Chain-of-Thought (CoT) prompts elicit multi-step reasoning, yet how reasoning related structure is expressed during training remains poorly understood. |
| Approach: | They propose a framework that tracks span-level gradients during fine-tuning on reasoning benchmarks to understand how models develop structured, step-by-step reasoning capabilities. |
| Outcome: | The proposed framework tracks span-level gradients during fine-tuning on reasoning benchmarks to understand how models develop structured, step-by-step reasoning capabilities. |
A Survey on Foundation Language Models for Single-cell Biology (2025.acl-long)
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| Challenge: | Existing single-cell foundation language models are based on pre-trained and large language models. |
| Approach: | They review the development of single-cell foundation language models . they discuss data tokenization strategies and pre-training paradigms . |
| Outcome: | The proposed models have shown remarkable performance in a variety of single-cell data analysis tasks. |
Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction (2021.emnlp-main)
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Mahsa Yarmohammadi, Shijie Wu, Marc Marone, Haoran Xu, Seth Ebner, Guanghui Qin, Yunmo Chen, Jialiang Guo, Craig Harman, Kenton Murray, Aaron Steven White, Mark Dredze, Benjamin Van Durme
| Challenge: | Zero-shot cross-lingual information extraction (IE) is a technique for training data in a source language but not in . |
| Approach: | They explore techniques including data projection and self-training to improve zero-shot cross-lingual information extraction (IE) IE is a construction of an IE model for some target language given existing annotations exclusively in English. |
| Outcome: | The proposed techniques show that they perform better than any single strategy. |
Divide-Then-Align: Honest Alignment based on the Knowledge Boundary of RAG (2025.acl-long)
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Xin Sun, Jianan Xie, Zhongqi Chen, Qiang Liu, Shu Wu, Yuehe Chen, Bowen Song, Zilei Wang, Weiqiang Wang, Liang Wang
| Challenge: | Large language models (LLMs) augmented with retrieval systems have significantly advanced natural language processing tasks by integrating external knowledge sources. |
| Approach: | They propose a method that conditions large language models to generate answers even in the absence of reliable knowledge. |
| Outcome: | The proposed approach balances accuracy with appropriate abstention, enhancing the reliability and trustworthiness of retrieval-augmented systems. |
LMOD: A Large Multimodal Ophthalmology Dataset and Benchmark for Large Vision-Language Models (2025.findings-naacl)
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Zhenyue Qin, Yu Yin, Dylan Campbell, Xuansheng Wu, Ke Zou, Ninghao Liu, Yih Chung Tham, Xiuzhen Zhang, Qingyu Chen
| Challenge: | Existing benchmarks for large vision-language models (LVLMs) are limited to ophthalmology-specific applications. |
| Approach: | They introduce a large-scale multimodal ophthalmology benchmark consisting of 21,993 instances across five ocular imaging modalities and 13 state-of-the-art LVLM representatives from closed-source, open-source and medical domains. |
| Outcome: | The proposed model shows significant performance drop in ophthalmology compared to other domains. |
Think Earlier, Not Longer: Prompt Optimization via Reducing Unhealthy Exploration (2026.findings-acl)
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| Challenge: | Existing approaches to improve reasoning performance ignore the presence of unhealthy exploration that increases token usage without contributing to effective problem-solving. |
| Approach: | They propose an entropy-dynamics-aware prompt optimization framework that trains a lightweight optimizer to generate concise clarifications. |
| Outcome: | The proposed framework reduces ambiguity-induced early-stage uncertainty while preserving the model's reasoning capabilities. |
TagRouter: Learning Route to LLMs through Tags for Open-Domain Text Generation Tasks (2025.findings-acl)
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| Challenge: | Existing models with limited performance and limited training can be difficult to use in large-scale applications. |
| Approach: | They propose a training-free model routing method that optimizes synergy among multiple LLMs for open-domain text generation tasks. |
| Outcome: | The proposed method outperforms 13 baseline models and reduces costs by 17.20%. |
Deep Research with Open-Domain Evaluation and Multi-Stage Guardrails for Safety (2026.acl-long)
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Wei-Chieh Huang, Henry Peng Zou, Yaozu Wu, Dongyuan Li, Yankai Chen, Weizhi Zhang, Yangning Li, Angelo Zangari, Jizhou Guo, Chunyu Miao, Liancheng Fang, Langzhou He, Yinghui Li, Renhe Jiang, Philip S. Yu
| Challenge: | Existing deep research frameworks lack adequate evaluation procedures and stage-specific protections. |
| Approach: | They propose a framework with open-domain evaluation and a stage-wise safety benchmark to address this oversight. |
| Outcome: | The proposed framework improves defense success rates by 16.53% while reducing over-refusal rates to approximately 6%. |
GlossaGen: Making Academic Translation Smarter with Glossing (2026.findings-acl)
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| Challenge: | Existing machine translation systems obscure or mistranslate key terminology, while paraphrasing aimed at lay readers often oversimplifies it, hindering their ability to master domain-specific technical vocabulary. |
| Approach: | They propose a task which produces translations dynamically adapted to a reader’s academic proficiency, or level, and a framework to address this challenge. |
| Outcome: | The proposed framework achieves higher scores than baselines on a synthesized benchmark and human evaluations. |
GoViG: Goal-Conditioned Visual Navigation Instruction Generation via Multimodal Reasoning (2026.findings-acl)
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Fengyi Wu, Yifei Dong, Yilong Dai, Guangyu Chen, Qifeng Wu, Huiting Huang, Hang Wang, Qi Dai, Alexander G Hauptmann, Zhi-Qi Cheng
| Challenge: | Current methods for instruction generation depend on privileged inputs such as semantic maps, landmark annotations, and panoramic views. |
| Approach: | They propose a task that generates coherent navigation instructions from egocentric visual observations. |
| Outcome: | The proposed task generates coherent navigation instructions from egocentric visual data . the proposed task improves performance over state-of-the-art methods in BLEU-4 and CIDEr scores . |
MCC-KD: Multi-CoT Consistent Knowledge Distillation (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable abilities in complex reasoning through chain of thought (CoT) prompting. |
| Approach: | They propose to generate multiple rationales for each question and enforce consistency among their predictions by minimizing the bidirectional KL-divergence between the answer distributions. |
| Outcome: | The proposed model achieves superior performance on in-distribution and commonsense reasoning benchmarks. |
Cache-of-Thought: Master-Apprentice Framework for Cost-Effective Vision Language Model Reasoning (2025.emnlp-main)
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Mingyuan Wu, Jize Jiang, Haozhen Zheng, Meitang Li, Zhaoheng Li, Beitong Tian, Bo Chen, Yongjoo Park, Minjia Zhang, ChengXiang Zhai, Klara Nahrstedt
| Challenge: | Recent Vision Language Models (VLMs) have shown tremendous promise in a wide range of realworld applications, but their size has made at-scale deployment and operation challenging due to high consumption of cloud computing resource, high latency, and expensive API calls. |
| Approach: | They propose a master–apprentice framework for collaborative inference between large and small vision language models. |
| Outcome: | The proposed framework improves reasoning performance on widely-recognized and challenging general reasoning benchmarks and specifically boosts reasoning of apprentice VLMs by 36.6%. |
SciConceptMiner: A system for large-scale scientific concept discovery (2021.acl-demo)
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| Challenge: | SciConceptMiner is a self-supervised system for the capture of scientific concepts . the system is scalable to the size of documents and the number of topics it can model . |
| Approach: | They propose a self-supervised system for the automatic capture of scientific concepts from academic publications and semi-structured data. |
| Outcome: | The proposed system achieves high accuracy (94.7%) with more than 740K scientific concepts. |
Making Pretrained Language Models Good Long-tailed Learners (2022.emnlp-main)
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| Challenge: | Prompt-tuning has shown appealing performance in few-shot classification . however, it is less promising in long-tailed classification due to long tail . |
| Approach: | They propose to use prompt-tuning to make pretrained language models at least good long-tailed learners by bridging the gap between prompt- and commonly used finetun. |
| Outcome: | The proposed method makes pretrained language models at least good long-tailed learners, bridging the gap between prompt-tuning and finetunation. |
VenusFactory: An Integrated System for Protein Engineering with Data Retrieval and Language Model Fine-Tuning (2025.acl-demo)
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Yang Tan, Chen Liu, Jingyuan Gao, Wu Banghao, Mingchen Li, Ruilin Wang, Lingrong Zhang, Huiqun Yu, Guisheng Fan, Liang Hong, Bingxin Zhou
| Challenge: | Pre-trained protein language models have been used in protein engineering, but their adoption is limited due to data collection, task benchmarking, and application challenges. |
| Approach: | They propose a versatile engine that integrates biological data retrieval, standardized task benchmarking, and modular fine-tuning of PLMs. |
| Outcome: | The proposed engine integrates biological data retrieval, task benchmarking, and modular fine-tuning of PLMs. |
Character-centric Story Visualization via Visual Planning and Token Alignment (2022.emnlp-main)
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| Challenge: | Story visualization is a task that requires machines to understand long text inputs and produce a globally consistent image sequence that illustrates the contents of the story. |
| Approach: | They propose to augment VQ-VAE with a text-to-visual-token (transformer) architecture to enable multiple image generation based on a complete story. |
| Outcome: | The proposed method excels at preserving characters and produces higher quality image sequences compared with baselines. |
MMEvalPro: Calibrating Multimodal Benchmarks Towards Trustworthy and Efficient Evaluation (2025.naacl-long)
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Jinsheng Huang, Liang Chen, Taian Guo, Fu Zeng, Yusheng Zhao, Bohan Wu, Ye Yuan, Haozhe Zhao, Zhihui Guo, Yichi Zhang, Jingyang Yuan, Wei Ju, Luchen Liu, Tianyu Liu, Baobao Chang, Ming Zhang
| Challenge: | Large Multimodal Models (LMMs) exhibit impressive cross-modal understanding and reasoning abilities, but many benchmarks suffer from systematic biases. |
| Approach: | They propose a benchmark to avoid Type-I errors by creating one perception question and one knowledge anchor question through a meticulous annotation process. |
| Outcome: | The proposed benchmark avoids Type-I errors while maintaining reliability of MCQ evaluations. |
Multi-Agent Autonomous Driving Systems with Large Language Models: A Survey of Recent Advances, Resources, and Future Directions (2025.findings-emnlp)
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Yaozu Wu, Dongyuan Li, Yankai Chen, Renhe Jiang, Henry Peng Zou, Wei-Chieh Huang, Yangning Li, Liancheng Fang, Zhen Wang, Philip S. Yu
| Challenge: | Large Language Models (LLMs) are used to assist with driving decisions, but they face limitations in perception and computational demands. |
| Approach: | They propose a survey of LLM-based multi-agent ADSs and their applications . they analyze agent-human interactions in scenarios where LLM agents engage with humans . |
| Outcome: | The proposed approach reduces human intervention and improves safety and efficiency. |
OpenRLHF: A Ray-based Easy-to-use, Scalable and High-performance RLHF Framework (2025.emnlp-demos)
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Jian Hu, Xibin Wu, Wei Shen, Jason Klein Liu, Weixun Wang, Songlin Jiang, Haoran Wang, Hao Chen, Bin Chen, Wenkai Fang, null Xianyu, Yu Cao, Haotian Xu, Yiming Liu
| Challenge: | Existing RLHF frameworks face inference bottlenecks and complexity barriers restricting their accessibility for newcomers. |
| Approach: | They propose an open-source RLHF framework that can be used to train large language models. |
| Outcome: | The proposed framework achieves superior training efficiency with speedups ranging from 1.22 to 1.68 across different model sizes compared to state-of-the-art frameworks, while requiring significantly fewer lines of code for implementation. |
A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code (2026.findings-acl)
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Keke Lian, Wang Bin, Lei Zhang, Libo Chen, Junjie Wang, Ziming Zhao, Yujiu Yang, Miaoqian Lin, Haotong Duan, Haoran Zhao, Shuang Liao, Mingda Guo, Quan Jiazheng, Yilu Zhong, Chenhao He, Chen Zichuan, Jie Wu, Haoling Li, Zhaoxuan Li, Jiongchi Yu, Hui LI, Dong Zhang
| Challenge: | Existing security evaluation benchmarks lack relevance to real-world AI programming tasks . current LLMs struggle with secure coding, research shows . |
| Approach: | They propose a repository-level evaluation benchmark to assess security of AI-generated code. |
| Outcome: | The proposed framework mirrors real-world AI programming tasks and offers valuable insights into the state of AI code generation. |
Named Entity Recognition in Multi-level Contexts (2020.aacl-main)
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| Challenge: | Existing methods for named entity recognition are unsatisfactory for recognizing entities in limited or ambiguous sentence-level contexts. |
| Approach: | They propose a framework to incorporate multi-level contexts for named entity recognition using TagLM as a baseline model and an auxiliary task to mine word-level contextual information. |
| Outcome: | The proposed framework is based on a set of sentence-level contexts and a document-level task to mine word-level contextual information. |
Mapping Long-term Causalities in Psychiatric Symptomatology and Life Events from Social Media (2024.naacl-long)
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Siyuan Chen, Meilin Wang, Minghao Lv, Zhiling Zhang, Juqianqian Juqianqian, Dejiyangla Dejiyangla, Yujia Peng, Kenny Zhu, Mengyue Wu
| Challenge: | Existing studies focus on the semantic content of social media posts, overlooking the evolving nature of mental disorders and symptoms. |
| Approach: | They extract causality between psychiatric symptoms and life events from social media posts and extract temporal attributes to improve diagnosis and treatment planning. |
| Outcome: | The extracted causality features improve diagnostic and treatment planning and improve performance in tasks such as depression and diagnosis point detection. |
ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding (2022.findings-emnlp)
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Qiming Peng, Yinxu Pan, Wenjin Wang, Bin Luo, Zhenyu Zhang, Zhengjie Huang, Yuhui Cao, Weichong Yin, Yongfeng Chen, Yin Zhang, Shikun Feng, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang
| Challenge: | Existing methods for visually rich document understanding lack layout-centered knowledge . experimental results show that ERNIE-Layout improves layout awareness . |
| Approach: | They propose a document pre-training solution with layout knowledge enhancement in the whole workflow to learn better representations that combine the features from text, layout, and image. |
| Outcome: | The proposed model outperforms existing models on key downstream tasks. |
Embedding and Gradient Say Wrong: A White-Box Method for Hallucination Detection (2024.emnlp-main)
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| Challenge: | Existing methods for hallucination detection have attracted more attention from the community. |
| Approach: | They propose to model the distributional distance between the regular conditional output and the unconditional output, which is generated without a given input text. |
| Outcome: | The proposed model achieves state-of-the-art on the hallucination benchmarks HADES and other datasets. |
Symptom Identification for Interpretable Detection of Multiple Mental Disorders on Social Media (2022.emnlp-main)
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| Challenge: | Mental disease detection (MDD) from social media has suffered from poor generalizability and interpretability due to lack of symptom modeling. |
| Approach: | They propose to annotate a social media corpus of symptom classes related to 7 mental disorders using a knowledge graph and a new annotation framework to facilitate further research. |
| Outcome: | The proposed model outperforms strong pure-text baselines and provides convincing MDD explanations with case studies. |
Detecting Speaker Personas from Conversational Texts (2021.emnlp-main)
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| Challenge: | Existing studies on personas are pre-defined and hard to obtain before a conversation . a new task aims to detect speaker persona based on conversational text . |
| Approach: | They propose a task to detect speaker personas based on conversational text . they build a dataset for SPD and propose utterance-to-profile matching networks . |
| Outcome: | The proposed task outperforms baseline models and utterance-to-profile (U2P) matching networks. |
Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models (2024.lrec-main)
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Derong Xu, Ziheng Zhang, Zhenxi Lin, Xian Wu, Zhihong Zhu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen
| Challenge: | Knowledge graph completion (KGC) is a widely used method to tackle incompleteness in knowledge graphs (KGs). |
| Approach: | They propose a general framework to compensate for the deficiency of contextualized knowledge by querying large language models from various perspectives. |
| Outcome: | The proposed framework improves knowledge graph completion (KGC) by querying large language models from various perspectives. |
PRoLoRA: Partial Rotation Empowers More Parameter-Efficient LoRA (2024.acl-long)
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| Challenge: | Partially Rotation-enhanced Low-Rank Adaptation (PRoLoRA) is an intra-layer sharing mechanism that circumvents the drawbacks of peer parameter-sharing methods. |
| Approach: | They propose a partially rotation-enhanced low-rank adaptation (PRoLoRA) that shares four components to reduce the cost of LoRA and improves model capacity. |
| Outcome: | Empirical results show that PRoLoRA outperforms LoRA on multiple instruction tuning datasets. |
Ambiguous Learning from Retrieval: Towards Zero-shot Semantic Parsing (2023.acl-long)
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Shan Wu, Chunlei Xin, Hongyu Lin, Xianpei Han, Cao Liu, Jiansong Chen, Fan Yang, Guanglu Wan, Le Sun
| Challenge: | Existing neural semantic parsers require a large amount of training data which is expensive and difficult to obtain. |
| Approach: | They propose a framework for a supervised retrieval system based on pretrained language models . they propose ambiguous supervision to improve the precision and coverage of the task . |
| Outcome: | The proposed approach outperforms state-of-the-art zero-shot parsing methods in ambiguous supervision. |
Effectiveness of Pre-training for Few-shot Intent Classification (2021.findings-emnlp)
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| Challenge: | Existing paradigms further pre-train language models such as BERT on vast amount of unlabeled corpus, but we find it highly effective and efficient to simply fine-tune BERT with roughly 1,000 labeled utterances from public datasets. |
| Approach: | They propose to fine-tune BERT with a small set of labeled utterances from public datasets to achieve a pre-trained model based on a set of 1,000 labeles. |
| Outcome: | The proposed model can outperform existing models on domains with very different semantics on novel domains. |
Scaling Collaborative Effort with Agents (2026.findings-acl)
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Shannon Zejiang Shen, Valerie Chen, Ken Gu, Alexis Ross, Zixian Ma, Jillian Ross, Alex Gu, Chenglei Si, Wayne Chi, Andi Peng, Jocelyn J Shen, Ameet Talwalkar, Tongshuang Wu, David Sontag
| Challenge: | Current evaluations of agents focus on producing high-quality, final outputs in one shot, failing to account for the inherently iterative nature of many real-world problems. |
| Approach: | They propose a framework that captures how an agent’s utility grows with increasing user involvement. |
| Outcome: | The proposed framework captures how an agent’s utility grows with increasing user involvement, revealing a missing ingredient in agent design: the ability to sustain engagement and scaffold user understanding. |
KnowMe-Bench: Benchmarking Person Understanding for Lifelong Digital Companions (2026.acl-long)
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Tingyu Wu, Zhisheng Chen, Ziyan Weng, Shuhe Wang, Shuo Zhang, Sen Hu, Silin Wu, Qizhen Lan, Huacan Wang, Ronghao Chen
| Challenge: | Existing long-horizon memory benchmarks use multi-turn dialogues or synthetic user histories . despite rapid progress on long-term memory evaluation, there are gaps in existing benchmarks . |
| Approach: | They propose a long-form autobiographical narrative benchmark that reconstructs each narrative into a flashback-aware, time-anchored stream and evaluates models with evidence-linked questions. |
| Outcome: | The proposed benchmarks build from long-form autobiographical narratives . they show that retrieval-augmented systems improve factual accuracy while errors persist on temporally grounded explanations and higher-level inferences. |
SCOUT: Selective Coupling via Optimal Unbalanced Transport for Interpretable Text Classification (2026.acl-long)
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| Challenge: | Standard interpretable models often rely on scalar similarities that obscure the true evidentiary basis of a prediction. |
| Approach: | They propose a new paradigm that grounds prototype reasoning in the selective correspondence of discriminative fragments. |
| Outcome: | The proposed model outperforms rationale extraction and post-hoc attribution methods on seven benchmarks. |
Hi-ToM: A Benchmark for Evaluating Higher-Order Theory of Mind Reasoning in Large Language Models (2023.findings-emnlp)
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| Challenge: | Theory of Mind (ToM) is the ability to reason about one's own and others' mental states. |
| Approach: | They propose a higher-order theory of mind benchmark and introduce a new deception mechanism to evaluate ToM reasoning. |
| Outcome: | The proposed benchmarks show that the LLMs are not performing well on higher-order tasks. |
Position Paper: Data-Centric AI in the Age of Large Language Models (2024.findings-emnlp)
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Xinyi Xu, Zhaoxuan Wu, Rui Qiao, Arun Verma, Yao Shu, Jingtan Wang, Xinyuan Niu, Zhenfeng He, Jiangwei Chen, Zijian Zhou, Gregory Kang Ruey Lau, Hieu Dao, Lucas Agussurja, Rachael Hwee Ling Sim, Xiaoqiang Lin, Wenyang Hu, Zhongxiang Dai, Pang Wei Koh, Bryan Kian Hsiang Low
| Challenge: | a paper proposes a data-centric perspective of AI research, focusing on large language models. |
| Approach: | They propose a data-centric viewpoint of AI research, focusing on large language models . they propose four scenarios centered around data, including data curation, attribution, knowledge transfer . |
| Outcome: | The proposed research focuses on large language models with data centric benchmarks . the proposed benchmarks can be used to develop new data curation methods . |
TOREE: Evaluating Topic Relevance of Student Essays for Chinese Primary and Middle School Education (2024.findings-acl)
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Xinlin Zhuang, Hongyi Wu, Xinshu Shen, Peimin Yu, Gaowei Yi, Xinhao Chen, Tu Hu, Yang Chen, Yupei Ren, Yadong Zhang, Youqi Song, Binxuan Liu, Man Lan
| Challenge: | Existing research on Automatic Essay Scoring (AES) for Chinese essays has overlooked topic relevance and lacks detailed feedback. |
| Approach: | They propose to use TOREE to assess topic relevance in Chinese primary and middle school students’ essays to improve automatic and human evaluations. |
| Outcome: | The proposed method significantly improves both automatic and human evaluations across four diverse LLMs. |
Exploring Contextual Word-level Style Relevance for Unsupervised Style Transfer (2020.acl-main)
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| Challenge: | Existing methods to unsupervised style transfer lack fine-grained control of the influence from the target style. |
| Approach: | They propose a model that exploits the relevance of each output word to the target style . they pretrain a style classifier and train an attentional Seq2seq model to reconstruct input sentences . |
| Outcome: | The proposed model achieves state-of-the-art performance in terms of transfer accuracy and content preservation. |
Escaping the Sisyphus Dilemma: Experience Replay for Robust Text-to-Optimization Modeling (2026.findings-acl)
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| Challenge: | Existing retrieval-augmented strategies for large language models fail to capture dynamic reasoning required to resolve execution failures. |
| Approach: | They propose a framework that implements Experience Replay to transform transient rectification steps into persistent knowledge. |
| Outcome: | The proposed framework improves model accuracy by 8.45% on complex tasks while reducing token consumption by 28.65% and interaction turns by 25.82%. |
Improved Unbiased Watermark for Large Language Models (2025.acl-long)
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| Challenge: | Unbiased watermarks allow to distinguish between text generated by humans and machines without causing distortion. |
| Approach: | They introduce a family of unbiased, Multi-Channel-based watermarks that partition the language model into segments and promote token probabilities within a selected segment based on a watermark key. |
| Outcome: | The proposed watermarks preserve the original distribution of the language model and offer significant improvements in detectability and robustness over existing unbiased watermark systems. |
An Effective Deployment of Diffusion LM for Data Augmentation in Low-Resource Sentiment Classification (2024.emnlp-main)
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| Challenge: | Existing models for textual data augmentation (DA) are highly data-hungry and struggle to perform satisfactorily under noisy conditions. |
| Approach: | They propose to leverage a diffusion language model to capture in-domain knowledge and generate pseudo samples by reconstructing strong label-related tokens. |
| Outcome: | The proposed method captures in-domain knowledge and generates pseudo samples by reconstructing strong label-related tokens. |
EdgeInfinite: A Memory-Efficient Infinite-Context Transformer for Edge Devices (2025.acl-industry)
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| Challenge: | Existing KV cache optimizations struggle with irreversible token eviction in long-output tasks . alternative sequence modeling architectures prove costly to adopt within established Transformer infrastructures. |
| Approach: | They propose a memory-efficient solution for infinite contexts that integrates compressed memory into Transformer-based LLMs through a trainable memory-gating module. |
| Outcome: | The proposed solution achieves comparable performance to baseline Transformer-based LLMs while optimizing memory consumption and time to first token. |
Creativity in LLM-based Multi-Agent Systems: A Survey (2025.emnlp-main)
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Yi-Cheng Lin, Kang-Chieh Chen, Zhe-Yan Li, Tzu-Heng Wu, Tzu-Hsuan Wu, Kuan-Yu Chen, Hung-yi Lee, Yun-Nung Chen
| Challenge: | Large language model (LLM)-driven multi-agent systems (MAS) are transforming how humans and AIs collaboratively generate ideas and artifacts. |
| Approach: | They present a taxonomy of agent proactivity and persona design and an overview of generation techniques. |
| Outcome: | The proposed framework and roadmap offers a roadmap for advancing the development, evaluation, and standardization of creative MAS. |
Conic10K: A Challenging Math Problem Understanding and Reasoning Dataset (2023.findings-emnlp)
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| Challenge: | Existing benchmarks or datasets require only a few steps of reasoning, making it difficult to analyse AI’s behaviour with reference to different problems within a specific topic in detail. |
| Approach: | They propose a conic10K math problem dataset that requires only a few steps of reasoning to be analysed. |
| Outcome: | The proposed dataset shows that existing language models exhibit weak performance on complex reasoning. |
Look Again, Think Slowly: Enhancing Visual Reflection in Vision-Language Models (2025.emnlp-main)
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| Challenge: | Recent advances in text-only "slow thinking" reasoning have prompted efforts to transfer this capability to vision-language models (VLMs). |
| Approach: | They propose a VRM Reflection-V which enhances visual reflection based on reasoning data for cold-start and reward design for reinforcement learning. |
| Outcome: | The proposed model improves visual reflection for cold-start and reward design for reinforcement learning (RL) it maintains a stronger and more consistent reliance on visual information during visual reasoning, indicating effective enhancement in visual reflection capabilities. |