Papers by Yu Wang
NeuralFSM: Adaptive Multi-Agent Coordination via Learning Finite-State Execution Policy (2026.acl-long)
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| Challenge: | Existing approaches to multi-agent problem solving rely on hand-crafted protocols or automatically designed topologies. |
| Approach: | They propose a state-driven framework that formulates multi-agent problem solving as a finite-state execution process. |
| Outcome: | The proposed framework outperforms baselines on diverse benchmarks by 6.74%–19.39% while reducing token consumption. |
UniCodec: Unified Audio Codec with Single Domain-Adaptive Codebook (2025.acl-long)
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Yidi Jiang, Qian Chen, Shengpeng Ji, Yu Xi, Wen Wang, Chong Zhang, Xianghu Yue, ShiLiang Zhang, Haizhou Li
| Challenge: | Existing neural audio codecs are not capable of handling multi-domain audio data . et al., 2023) integrate speech modality with text-based large language models . |
| Approach: | They propose a unified audio codec with a single codebook to support multi-domain audio data . they propose combining a mix-of-experts strategy and a partitioned domain-adaptive codebook method . |
| Outcome: | The proposed codec outperforms existing codecs on acoustic and semantic representation capabilities. |
Multimodal Fusion and Coherence Modeling for Video Topic Segmentation (2025.findings-acl)
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| Challenge: | Traditional video topic segmentation methods struggle to discern topical transitions . supervised approaches have improved performance on video action or scene segmentation . |
| Approach: | They propose a new task for video topic segmentation that enhances multimodality alignment and fusion by exploring different architectures using Cross-Attention and Mixture of Experts. |
| Outcome: | The proposed model improves on educational videos, in the form of lectures . it combines cross-attention and mixture of experts to strengthen multimodality alignment and fusion . |
Large Language Models are good multi-lingual learners : When LLMs meet cross-lingual prompts (2025.coling-main)
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| Challenge: | Experimental results show that Large Language Models can generate rule-based data in long contexts without following all specified rules. |
| Approach: | They propose a novel prompting strategy Multi-Lingual Prompt which automatically translates the error-prone rule that an LLM struggles to follow into another language, thus drawing greater attention to it. |
| Outcome: | The proposed framework outperforms state-of-the-art prompting methods on public datasets across various tasks, with a specific case study in text-to-MIP instances. |
Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs (2026.acl-long)
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Yu Li, Xiaoran Shang, Qizhi Pei, Yun Zhu, Xin Gao, Honglin Lin, Zhanping Zhong, Zhuoshi Pan, Zheng Liu, Xiaoyang Wang, Conghui He, Dahua Lin, Feng Zhao, Lijun Wu
| Challenge: | High-quality post-training data is the primary engine driving LLM capabilities . datasets are often treated as isolated artifacts, overlooking their true developmental context . |
| Approach: | They propose a framework to reconstruct the evolutionary graph of dataset development using data lineage. |
| Outcome: | The proposed framework characterizes domain-specific structural patterns in Math-oriented datasets and general-domain corpora. |
Mask-then-Fill: A Flexible and Effective Data Augmentation Framework for Event Extraction (2022.findings-emnlp)
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| Challenge: | Existing data augmentation methods for event extraction are costly and time-consuming. |
| Approach: | They propose a data augmentation framework that randomly masks out an adjunct sentence fragment and infills a variable-length text span with a fine-tuned infilling model. |
| Outcome: | The proposed framework can generate more diverse data while keeping the original structure unchanged . it can replace a fragment of arbitrary length in the text with another fragment of variable length . |
MTSA: Multi-turn Safety Alignment for LLMs through Multi-round Red-teaming (2025.acl-long)
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| Challenge: | Existing jailbreak techniques rely on single-round interactions, pro-Corresponding author. |
| Approach: | They propose a multi-turn safety alignment framework to address the challenge of securing large language models in multi-round interactions. |
| Outcome: | The proposed framework exhibits state-of-the-art attack capabilities while improving safety performance on safety benchmarks. |
HSDreport: Heart Sound Diagnosis with Echocardiography Reports (2024.findings-emnlp)
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Zihan Zhao, Pingjie Wang, Liudan Zhao, Yuchen Yang, Ya Zhang, Kun Sun, Xin Sun, Xin Zhou, Yu Wang, Yanfeng Wang
| Challenge: | Existing methods for heart sound diagnosis are limited to a few fixed categories and do not utilize echocardiography reports, the gold standard in the diagnosis of related diseases. |
| Approach: | They propose a benchmark that mandates the direct utilization of heart sounds obtained from auscultation to predict echocardiography reports. |
| Outcome: | The proposed method outperforms existing methods and existing multimodal LLMs in detecting key abnormalities in heart sounds. |
A Survey of Large Language Models in Psychotherapy: Current Landscape and Future Directions (2025.findings-acl)
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Hongbin Na, Yining Hua, Zimu Wang, Tao Shen, Beibei Yu, Lilin Wang, Wei Wang, John Torous, Ling Chen
| Challenge: | Large language models (LLMs) can handle extensive context and multi-turn reasoning. |
| Approach: | They propose a taxonomy dividing psychotherapy into stages of assessment, diagnosis, and treatment to examine LLM advancements and challenges. |
| Outcome: | The proposed taxonomy reveals imbalances in current research, such as a focus on common disorders, linguistic biases, fragmented methods, and limited theoretical integration. |
NL-Debugging: Exploiting Natural Language as an Intermediate Representation for Code Debugging (2025.emnlp-main)
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Weiming Zhang, Qingyao Li, Xinyi Dai, Jizheng Chen, Kounianhua Du, Weiwen Liu, Yasheng Wang, Ruiming Tang, Yong Yu, Weinan Zhang
| Challenge: | Early debugging efforts focused on code-level analysis, which often fails when addressing complex programming errors. |
| Approach: | They propose a framework that employs natural language as an intermediate representation to improve code debugging by debuggating at a natural language level. |
| Outcome: | The proposed framework outperforms traditional debugging methods and enables a broader modification space through direct refinement guided by execution feedback. |
ERNIE-Doc: A Retrospective Long-Document Modeling Transformer (2021.acl-long)
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| Challenge: | Existing models for document-level language pretraining are not suitable for long documents due to their quadratically increasing memory and time consumption. |
| Approach: | They propose a document-level language pretraining model based on Recurrence Transformers. |
| Outcome: | The proposed model outperforms existing models on language understanding tasks. |
ParaSuite: Boosting LLM Reasoning via Paradox Resolution (2026.acl-long)
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| Challenge: | Existing benchmarks for paradox research focus on checking basic logical consistency and not reflective reasoning. |
| Approach: | They propose a pipeline dedicated to paradox research that automates data synthesis, evaluation, and training. |
| Outcome: | The proposed pipeline improves paradoxical and general STEM reasoning. |
CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools (2023.emnlp-demo)
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Jingwei Ni, Julia Bingler, Chiara Colesanti-Senni, Mathias Kraus, Glen Gostlow, Tobias Schimanski, Dominik Stammbach, Saeid Ashraf Vaghefi, Qian Wang, Nicolas Webersinke, Tobias Wekhof, Tingyu Yu, Markus Leippold
| Challenge: | a lack of transparency in sustainability reporting is a key challenge due to the sheer volume and complexity of sustainability reports . only a few entities worldwide have the resources to analyze these reports at scale . a novel LLM-based system to automate the analysis of corporate sustainability reports is needed . |
| Approach: | They propose a novel LLM-based system to automate the analysis of corporate sustainability reports. |
| Outcome: | The proposed system automates the analysis of corporate sustainability reports. |
Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning (2026.findings-acl)
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Yibo Yan, Shen Wang, Jiahao Huo, Jingheng Ye, Zhendong Chu, Xuming Hu, Philip S. Yu, Carla P Gomes, Bart Selman, Qingsong Wen
| Challenge: | Current scientific reasoning models struggle with generalization across domains and fall short of multimodal perception. |
| Approach: | They propose to use multimodal large language models to integrate text, images, and other modalities to enhance scientific reasoning. |
| Outcome: | The proposed models can integrate text, images, and other modalities and improve reasoning across disciplines. |
Enhancing Speech-to-Speech Dialogue Modeling with End-to-End Retrieval-Augmented Generation (2025.findings-emnlp)
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| Challenge: | End-to-end speech-to speech (S2S) dialogue systems face key challenges in incorporating external knowledge into their models. |
| Approach: | They propose a framework that directly retrieves relevant textual knowledge from speech queries. |
| Outcome: | The proposed framework improves the performance of end-to-end speech-tospeech dialogue systems while achieving higher retrieval efficiency. |
AIDA-SEAT: Towards Reliable AI Doctor Assistant via State-Evaluation-Action Tree Enhanced LLMs in Online Hospital (2026.acl-industry)
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Lianxin Sun, Xiaoying Ying, Guangya Yu, Weiyan Zhang, Chenhao Guan, Hao He, Mingxi Shang, Jianhua Li, ChunMing Wang, Tong Ruan
| Challenge: | Existing systems rely on large language models or retrieval-augmented generation (RAG) but these methods lack the explicit logical pathways essential for multi-step reasoning. |
| Approach: | They propose an AIDA-SEAT framework to provide reliable clinical decision-making support by transforming and modifying medical documents and doctors' state-evaluation-action trees. |
| Outcome: | The proposed framework achieves 1.01% higher than current state-of-the-art (SOTA) baselines across five departments, including common RAG-based methods. |
Backdoor Collapse: Eliminating Unknown Threats Via Known Backdoor Aggregation In Language Models (2026.acl-long)
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Liang Lin, Miao Yu, Moayad Aloqaily, Zhenhong Zhou, Kun Wang, Linsey Pang, Prakhar Mehrotra, Qingsong Wen
| Challenge: | Existing defenses rely on impractical assumptions about trigger settings to mitigate backdoor attacks . a recent study found that small amounts of training data can systematically induce harmful behaviors in large language models. |
| Approach: | They propose a backdoor defense framework that requires no prior knowledge of trigger settings . they use a two-stage process to aggregate backdoor representations and fine-tune recovery . |
| Outcome: | The proposed defense reduces the average Attack Success Rate to 4.41% across multiple benchmarks . the proposed framework generalizes across different types of backdoors, confirming its robustness in practical deployment scenarios. |
Safety Sidecar: Reflection-Driven Runtime Control for Safer Agents (2026.findings-acl)
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| Challenge: | Existing safety controls fail to provide runtime intervention or cross-architecture portability for autonomous LLM agents. |
| Approach: | They propose a model-agnostic, plug-and-play module to provide arbitrary agent safety control and auditability. |
| Outcome: | The proposed module improves the secure-solution rate by 2.9–11.2 percentage points . it adds only 3.2s to end-to-end latency and a negligible average cost of 5.37 10-4 per scenario . |
Learn and Review: Enhancing Continual Named Entity Recognition via Reviewing Synthetic Samples (2022.findings-acl)
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| Challenge: | Existing methods for named entity recognition classify mentions into fixed set of predefined entity types but in many real-world scenarios, new entity types are incrementally involved. |
| Approach: | They propose a two-stage framework Learn-and-Review for continual named entity recognition to alleviate inter-type confusion. |
| Outcome: | The proposed framework outperforms the state-of-the-art method on CoNLL-03 and OntoNotes-5.0. |
QUITO-X: A New Perspective on Context Compression from the Information Bottleneck Theory (2025.findings-emnlp)
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Yihang Wang, Xu Huang, Bowen Tian, Yueyang Su, Lei Yu, Huaming Liao, Yixing Fan, Jiafeng Guo, Xueqi Cheng
| Challenge: | Existing methods for compressing context by removing redundant tokens are inconsistent with the objective of retaining the most important tokens when conditioning on a given query. |
| Approach: | They propose a method that uses information bottleneck theory to compress context . they propose to remove redundant tokens using metrics such as self-information or perplexity . |
| Outcome: | The proposed method achieves a 25% increase in compression rate compared to the state-of-the-art . |
Improving Long Document Topic Segmentation Models With Enhanced Coherence Modeling (2023.emnlp-main)
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| Challenge: | Recent supervised neural models have greatly promoted the development of topic segmentation, but the deeper relationship between coherence and topic segmenting is underexplored. |
| Approach: | They propose to use topic-aware Sentence Structure Prediction and Contrastive Semantic Similarity Learning to capture coherence from logical structure and semantic similarity perspectives to further improve topic segmentation performance. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on WIKI-727K and achieves an average relative reduction of 4.3% on Pk on WikiSection. |
CORD: Bridging the Audio–Text Reasoning Gap via Weighted On-policy Cross-modal Distillation (2026.findings-acl)
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Hu Jing, Danxiang Zhu, Xianlong Luo, Dan Zhang, Shuwei He, Yishu Lei, Shikun Feng, Hai-Tao Zheng, Jingzhou HE, Yu Sun, Hua Wu, Haifeng Wang
| Challenge: | Large Audio Language Models (LALMs) exhibit a degradation in knowledge and reasoning capabilities . empirical results show that CORD significantly bridges the audio–text performance gap . |
| Approach: | They propose a framework that performs online cross-modal self-distillation to bridge the acoustic-semantic gap between LALMs and text-based models. |
| Outcome: | The proposed framework bridges the acoustic-semantic gap between LALMs and text-based models . it employs on-policy reverse KL divergence with importance-aware weighting . |
When Reasoning Meets Information Aggregation: A Case Study with Sports Narratives (2024.emnlp-main)
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| Challenge: | Using sports data, an LLM can analyze sports narratives to infer points from actions, identify related entities, attribute points accurately to players and teams, and draw conclusions. |
| Approach: | They propose a method to synthesize NBA basketball game narratives using real NBA basketball data and propose 'SportsGen' they find that most models fail to accurately aggregate basketball scores due to frequent scoring patterns and open-source models suffer from significant score hallucinations. |
| Outcome: | The proposed method can evaluate LLMs’ reasoning capabilities under complex scenarios with varying narrative lengths and density of information. |
TestNUC: Enhancing Test-Time Computing Approaches and Scaling through Neighboring Unlabeled Data Consistency (2025.acl-long)
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Henry Peng Zou, Zhengyao Gu, Yue Zhou, Yankai Chen, Weizhi Zhang, Liancheng Fang, Yibo Wang, Yangning Li, Kay Liu, Philip S. Yu
| Challenge: | Test-time computing approaches that leverage additional computational resources during inference have been proven effective in enhancing large language model performance. |
| Approach: | They propose a linearly scaling approach that leverages local consistency of neighboring unlabeled data to improve test-time predictions. |
| Outcome: | The proposed approach outperforms baseline methods such as prompting and self-consistency across eight datasets and performs robustly across embedding models. |
Automatic Table Union Search with Tabular Representation Learning (2023.findings-acl)
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Xuming Hu, Shen Wang, Xiao Qin, Chuan Lei, Zhengyuan Shen, Christos Faloutsos, Asterios Katsifodimos, George Karypis, Lijie Wen, Philip S. Yu
| Challenge: | Existing methods to identify uniability based on column representations are insufficient to reveal latent relational features to describe column relation between pair of columns. |
| Approach: | They propose a self-supervised table union search framework called AutoTUS to learn column relational representations in a multi-stage manner. |
| Outcome: | The proposed framework improves on the SOTA baseline and on real-world datasets. |
Retrieval Augmentation for Commonsense Reasoning: A Unified Approach (2022.emnlp-main)
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| Challenge: | Existing methods for retrieving encyclopedic knowledge lack a large corpus and effective commonsense retriever. |
| Approach: | They propose a framework for retrieval-augmented commonsense reasoning with a large commonsensense corpus and a commonseense retriever. |
| Outcome: | The proposed framework outperforms existing methods on commonsense reasoning tasks. |
ESGenius: Benchmarking LLMs on Environmental, Social, and Governance (ESG) and Sustainability Knowledge (2025.emnlp-main)
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Chaoyue He, Xin Zhou, Yi Wu, Xinjia Yu, Yan Zhang, Lei Zhang, Di Wang, Shengfei Lyu, Hong Xu, Wang Xiaoqiao, Wei Liu, Chunyan Miao
| Challenge: | ESGenius is a comprehensive benchmark for evaluating Large Language Models on ESG and sustainability knowledge. |
| Approach: | They introduce ESGenius, a benchmark for evaluating and enhancing ESG proficiency . they use a rigorous two-stage evaluation protocol and a repository of foundational frameworks . |
| Outcome: | ESGenius is a benchmark for evaluating and enhancing the proficiency of Large Language Models (LLMs) in ESG and sustainability-focused question answering. |
SecFormer: Fast and Accurate Privacy-Preserving Inference for Transformer Models via SMPC (2024.findings-acl)
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| Challenge: | a growing number of cloud-based inference services are relying on SMPC to protect data privacy. |
| Approach: | They propose a framework for Privacy-Preserving Inference for Transformer models that eliminates exponential and maximum operations in PPI without sacrificing model performance. |
| Outcome: | The proposed framework outperforms MPCFormer in terms of performance and efficiency . it is 3.57 and 3.58 times faster than PUMA for BERTBASE and BERTLARGE . |
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024.emnlp-main)
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Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip Yu, Wenpeng Yin
| Challenge: | a comparative analysis of paper (meta-)reviews by large language models (LLMs) aims to identify and distinguish LLMs from human activities . |
| Approach: | They present a comparative analysis to identify and distinguish LLM activities from human activities. |
| Outcome: | The proposed analysis aims to improve recognition of instances when someone implicitly uses LLMs for reviewing activities. |
HTML: Hierarchical Topology Multi-task Learning for Semantic Parsing in Knowledge Base Question Answering (2025.findings-acl)
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| Challenge: | Existing approaches struggle with mapping questions to precise logical forms . Existing frameworks struggle with complex mapping of questions to logical form . |
| Approach: | They propose a framework that leverages a hierarchical multi-task learning paradigm to enhance the performance of logical form generation. |
| Outcome: | The proposed framework outperforms supervised fine-tuning methods and training-free ones on large language models. |
Grounded Multimodal Named Entity Recognition on Social Media (2023.acl-long)
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| Challenge: | Existing studies on Multimodal Named Entity Recognition only extract entity-type pairs in text, which is useless for multimodal knowledge graph construction. |
| Approach: | They propose a task to identify named entities in text and their bounding box groundings in image . they extend four well-known MNER methods to establish a number of baseline systems . |
| Outcome: | The proposed framework outperforms baseline systems on the GMNER task. |
EvoEdit: Evolving Null-space Alignment for Robust and Efficient Knowledge Editing (2026.findings-acl)
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| Challenge: | Existing approaches to modifying large language models require continual updates to rectify outdated or erroneous knowledge. |
| Approach: | They propose a model editing strategy that mitigates catastrophic interference through sequential null-space alignment. |
| Outcome: | EvoEdit achieves better or comparable performance than prior state-of-the-art techniques with up to 3.53 speedup. |
An Information Bottleneck Perspective for Effective Noise Filtering on Retrieval-Augmented Generation (2024.acl-long)
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Kun Zhu, Xiaocheng Feng, Xiyuan Du, Yuxuan Gu, Weijiang Yu, Haotian Wang, Qianglong Chen, Zheng Chu, Jingchang Chen, Bing Qin
| Challenge: | Experimental results show that retrieval-augmented generation improves accuracy and relevance of large language models. |
| Approach: | They propose to introduce the information bottleneck theory into retrieval-augmented generation by maximizing mutual information between compression and ground output while minimizing mutual information . |
| Outcome: | The proposed approach improves accuracy and correctness of answer generation and conciseness with 2.5% compression rate. |
Detecting Causal Language Use in Science Findings (D19-1)
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| Challenge: | Prior studies on identifying inappropriate use of causal language relied on manual content analysis, which is not scalable for examining a large volume of science publications. |
| Approach: | They developed a prediction model that classifies conclusion sentences into “no relationship”, “correlational”, “conditional causal” and “direct causal” categories. |
| Outcome: | The proposed model can be used to identify the inappropriate use of causal language in scientific publications and news articles. |
Leveraging Unit Language Guidance to Advance Speech Modeling in Textless Speech-to-Speech Translation (2025.findings-acl)
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Yuhao Zhang, Xiangnan Ma, Kaiqi Kou, Peizhuo Liu, Weiqiao Shan, Benyou Wang, Tong Xiao, Yuxin Huang, Zhengtao Yu, JingBo Zhu
| Challenge: | Existing textless speech-to-speech translation models have two main challenges: 1) learning cross-modal features and 2) learning alignment of difference languages in long sequences. |
| Approach: | They propose a unit language to overcome two main modeling challenges . they propose task prompt modeling to utilize the unit language in guiding the modeling process. |
| Outcome: | The proposed language improves over a strong baseline and achieves comparable performance to models trained with text. |
Self-Supervised Learning for Contextualized Extractive Summarization (P19-1)
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| Challenge: | Existing models for extractive summarization are usually trained from scratch with a cross-entropy loss . previous work builds an end-to-end system to learn to choose sentences without explicitly modeling document context . |
| Approach: | They propose three auxiliary pre-training tasks that learn to capture the document context in a self-supervised fashion. |
| Outcome: | The proposed models outperform existing models on a CNN/DM dataset. |
Generating Commonsense Reasoning Questions with Controllable Complexity through Multi-step Structural Composition (2025.coling-main)
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| Challenge: | Existing work mainly learns to map text into questions, lacking a mechanism to control results with desired complexity. |
| Approach: | They propose a novel controllable framework to generate QGs with desired complexity using contextual and commonsense clues from text. |
| Outcome: | The proposed framework can generate complex questions with desired complexity levels. |
Pre-training Multi-task Contrastive Learning Models for Scientific Literature Understanding (2023.findings-emnlp)
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| Challenge: | Pre-trained language models (LMs) have shown effectiveness in literature understanding tasks, especially when tuned via contrastive learning. |
| Approach: | They propose a multi-task contrastive learning framework that enables common knowledge sharing across different scientific literature understanding tasks while preventing task-specific skills from interfering with each other. |
| Outcome: | The proposed framework outperforms state-of-the-art pre-trained language models on a comprehensive dataset. |
DuReader_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World Applications (2021.acl-short)
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| Challenge: | In order to comprehensively verify the robustness and generalization of MRC models, we construct a real-world Chinese dataset - DuReader_robust . |
| Approach: | They introduce a real-world Chinese dataset to evaluate the robustness and generalization of MRC models from three aspects: over-sensitivity, over-stability and generalisation. |
| Outcome: | The proposed model fails to perform well on the challenge test set and may provide suggestions for future model development. |
Simple yet Effective Bridge Reasoning for Open-Domain Multi-Hop Question Answering (D19-58)
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| Challenge: | Existing work on open-domain multi-hop question answering relies on off-the-shelf information retrieval techniques to retrieve answer passages. |
| Approach: | They propose a new subproblem for open-domain multi-hop question answering . they aim to recognize the anchor from a set of start passages with a reading comprehension model . |
| Outcome: | The proposed method significantly improves the baseline method on the open-domain hotpotQA benchmark. |
What Makes AI Research Replicable? Executable Knowledge Graphs as Scientific Knowledge Representations (2026.acl-short)
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Yujie Luo, Zhuoyun Yu, Xuehai Wang, Yuqi Zhu, Ningyu Zhang, Lanning Wei, Lun Du, Da Zheng, Huajun Chen
| Challenge: | Existing approaches to replicate AI research are limited by insufficient background knowledge and the limitations of retrieval-augmented generation methods. |
| Approach: | They propose a pluggable, paper-centric knowledge base that integrates code snippets and technical insights extracted from scientific literature into a verifiable, executable representation. |
| Outcome: | The proposed knowledge base shows significant performance gains on paperBench when integrated into three agent frameworks with two different LLMs. |
Two Directions for Clinical Data Generation with Large Language Models: Data-to-Label and Label-to-Data (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) can generate natural language texts for various domains and tasks, but their potential for clinical text mining is under-explored. |
| Approach: | They propose a pragmatic taxonomy for AD sign and symptom progression based on expert knowledge and train a system to detect AD-related signs and symptoms from EHRs. |
| Outcome: | The proposed taxonomy outperforms existing methods using only the gold dataset and silver datasets. |
Beyond the Leaderboard: Rethinking Medical Benchmarks for Large Language Models (2026.acl-long)
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| Challenge: | Large language models (LLMs) are proving significant potential in healthcare, prompting numerous benchmarks to evaluate their capabilities. |
| Approach: | They propose a framework that deconstructs benchmark development into five stages from design to governance and provides a checklist of 46 medically-tailored criteria. |
| Outcome: | The framework deconstructs benchmark development into five stages from design to governance and provides a comprehensive checklist of 46 medically-tailored criteria. |
Self-Taught Agentic Long Context Understanding (2025.acl-long)
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Yufan Zhuang, Xiaodong Yu, Jialian Wu, Ximeng Sun, Ze Wang, Jiang Liu, Yusheng Su, Jingbo Shang, Zicheng Liu, Emad Barsoum
| Challenge: | Extensive experiments across seven long-context tasks demonstrate that AgenticLU significantly outperforms state-of-the-art prompting methods and specialized long-consumer LLMs. |
| Approach: | They propose a framework to enhance an LLM's understanding of long-context questions by integrating targeted self-clarification with contextual grounding within an agentic workflow. |
| Outcome: | The proposed framework outperforms state-of-the-art prompting methods and specialized long-context LLMs in seven long-constitut tasks. |
Distantly Supervised Course Concept Extraction in MOOCs with Academic Discipline (2023.acl-long)
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| Challenge: | Existing methods to extract knowledge concepts from MOOCs are noisy and incomplete because of the limited dictionary and diverse MOOC. |
| Approach: | They propose to automatically extract course concepts using distant supervision to eliminate the heavy work of human annotations. |
| Outcome: | The proposed framework outperforms state-of-the-art methods with 7% absolute improvement in F1 score. |
OVM, Outcome-supervised Value Models for Planning in Mathematical Reasoning (2024.findings-naacl)
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| Challenge: | Large language models (LLMs) struggle with maintaining accuracy throughout multiple reasoning steps, especially in mathematical reasoning where an error in earlier steps can propagate to subsequent ones and ultimately leading to an incorrect answer. |
| Approach: | They propose an Outcome-supervised Value Model (OVM) that employs outcome supervision for training a value model, which prioritizes steps that lead to accurate conclusions. |
| Outcome: | The proposed model performs better on two multi-step reasoning datasets, GSM8K and Game of 24. |
Flexible Thinking for Multimodal Emotional Support Conversation via Reinforcement Learning (2025.findings-emnlp)
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| Challenge: | Current Chain-of-Thought based ESC methods often employ rigid, text-only reasoning, limiting adaptability in dynamic, multimodal interactions and introducing reasoning noise that degrades support quality. |
| Approach: | They propose a framework that integrates supervised fine-tuning with reinforcement learning to improve ESC models' response quality. |
| Outcome: | The proposed framework enables models to select contextually relevant thinking aspects: Visual Scene, Emotion, Situation, and Response Strategy. |
Steering Knowledge Selection Behaviours in LLMs via SAE-Based Representation Engineering (2025.naacl-long)
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Yu Zhao, Alessio Devoto, Giwon Hong, Xiaotang Du, Aryo Pradipta Gema, Hongru Wang, Xuanli He, Kam-Fai Wong, Pasquale Minervini
| Challenge: | Large language models store factual knowledge in their parameters but their parametric knowledge can conflict with the information provided in the context. |
| Approach: | They propose a training-free representation engineering method that uses pre-trained sparse auto-encoders to control the knowledge selection behaviour of large language models. |
| Outcome: | The proposed method can control the use of both knowledge sources to resolve knowledge conflict in open-domain question-answering tasks surpassing existing representation engineering methods (+10%) and contrastive decoding methods (+5%). |
Domain Generalization via Switch Knowledge Distillation for Robust Review Representation (2023.findings-acl)
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| Challenge: | Existing models for review representations of unseen or anonymous users are limited by their in-domain nature. |
| Approach: | They propose to use in-domain user and product information to generalize reviews . they use switch knowledge distillation to learn review representations for unseen users . |
| Outcome: | The proposed model performs well for existing or anonymous unseen users. |
ReFreeKV: Towards Threshold-Free KV Cache Compression (2026.findings-acl)
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| Challenge: | Towards the KV cache efficiency, we propose a new objective that lifts the threshold constraints for robust KV compression. |
| Approach: | They propose a method that adjusts KV cache budgets while preserving full-cache performance. |
| Outcome: | The proposed method can reduce memory consumption while preserving full-cache performance. |
MedQA-CS: Objective Structured Clinical Examination (OSCE)-Style Benchmark for Evaluating LLM Clinical Skills (2026.eacl-long)
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Zonghai Yao, Zihao Zhang, Chaolong Tang, Xingyu Bian, Youxia Zhao, Zhichao Yang, Junda Wang, Huixue Zhou, Won Seok Jang, Feiyun Ouyang, Hong Yu
| Challenge: | Current clinical LLM benchmarks fail to evaluate advanced clinical skills in AI and large language models (LLMs). |
| Approach: | They propose a framework to evaluate large language models (LLMs) using two instruction-following tasks designed to reflect real clinical scenarios. |
| Outcome: | The proposed framework evaluates LLMs through two instruction-following tasks designed to reflect real clinical scenarios. |
EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets (2021.acl-long)
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| Challenge: | Large-scale pre-trained language models require enormous computational resources and long training time. |
| Approach: | They propose an algorithm to reduce inference time and train large NLP models by slimming the self-attention and fully-connected sub-layers inside a transformer. |
| Outcome: | The proposed algorithm achieves comparable performance to standard BERT with 35 45% less training time. |
Spec-VLA: Speculative Decoding for Vision-Language-Action Models with Relaxed Acceptance (2025.emnlp-main)
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| Challenge: | Visual Language Models (VLMs) have significant parameter size and autoregressive (AR) decoding nature impose considerable computational demands on VLA models. |
| Approach: | They propose a framework to relax acceptance utilizing the relative distances represented by the action tokens of the VLA model. |
| Outcome: | Empirical results show that the proposed framework improves the speed of the prediction task by 44%. |
HGCLIP: Exploring Vision-Language Models with Graph Representations for Hierarchical Understanding (2025.coling-main)
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| Challenge: | Object categories are typically organized into a multi-granularity taxonomic hierarchy . traditional uni-modal approaches focus primarily on image features, revealing limitations in complex scenarios. |
| Approach: | They propose a framework that combines vision-language models with a deeper exploitation of the hierarchy. |
| Outcome: | The proposed framework shows significant improvements on 11 diverse visual recognition benchmarks. |
Resource-Limited Joint Multimodal Sentiment Reasoning and Classification via Chain-of-Thought Enhancement and Distillation (2026.findings-acl)
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| Challenge: | Current approaches for Multimodal Sentiment Analysis (MSA) rely on parameter-heavy LLMs for classification, overlooking multimodal sentiment reasoning generation in resource-limited environments. |
| Approach: | They propose a multimodal sentiment reasoning distillation model that employs a teacher-assistant-student paradigm to address deployment constraints in resource-limited environments. |
| Outcome: | The proposed model performs well on a resource-limited JMSRC task with only 3B parameters and shows generalization and interpretability. |
Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers (P19-1)
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| Challenge: | Existing approaches to extract multiple relations from a paragraph require multiple passes over the paragraph. |
| Approach: | They propose a method to extract multiple relations from a paragraph by encoding the paragraph only once. |
| Outcome: | The proposed approach can perform state-of-the-art on the benchmark ACE 2005. |
RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework (2025.acl-long)
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Kunlun Zhu, Yifan Luo, Dingling Xu, Yukun Yan, Zhenghao Liu, Shi Yu, Ruobing Wang, Shuo Wang, Yishan Li, Nan Zhang, Xu Han, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing evaluation metrics for RAG systems are lacking due to high costs of data construction and lack of factual accuracy. |
| Approach: | They propose a framework to evaluate RAG systems in specialized scenarios . they propose three new metrics to evaluate LLM-generated responses . |
| Outcome: | The proposed framework outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples. |
CodeFlowBench: A Multi-turn, Iterative Benchmark for Complex Code Generation (2026.acl-long)
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| Challenge: | Modern software development demands code that is maintainable, testable, and scalable by organizing the implementation into modular components with iterative reuse of existing codes. |
| Approach: | They propose a benchmark to evaluate LLMs' ability to perform codeflow by reusing existing functions over multiple turns. |
| Outcome: | The proposed benchmarks show that LLMs perform significantly worse in multi-turn codeflow scenarios and that their performance inversely correlates with dependency complexity. |
BANER: Boundary-Aware LLMs for Few-Shot Named Entity Recognition (2025.coling-main)
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| Challenge: | Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP) that aims to detect the entity spans of text and classify them into pre-defined set of entity types. |
| Approach: | They propose a boundary-aware contrastive learning strategy to enhance the LLM’s ability to perceive entity boundaries for generalized entity spans. |
| Outcome: | The proposed framework outperforms prior methods and validates its effectiveness across a range of LLM architectures. |
Efficient Tool Use with Chain-of-Abstraction Reasoning (2025.coling-main)
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Silin Gao, Jane Dwivedi-Yu, Ping Yu, Xiaoqing Ellen Tan, Ramakanth Pasunuru, Olga Golovneva, Koustuv Sinha, Asli Celikyilmaz, Antoine Bosselut, Tianlu Wang
| Challenge: | Recent large language models have made progress at interpreting and executing instructions. |
| Approach: | They propose a method to decouple general reasoning from specialized knowledge . they propose to use abstract reasoning chains and domain tools to reify each chain . |
| Outcome: | The proposed method outperforms baseline methods on QA and mathematical reasoning domains. |
Argumentation Mining on Essays at Multi Scales (2020.coling-main)
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| Challenge: | Argumentation mining on essays is a new task in natural language processing. |
| Approach: | They propose a multi-scale argumentation mining model which aims to identify the types and locations of argumentation components from essay text. |
| Outcome: | The proposed model outperforms existing models on mining all types of argumentation components on the Persuasive Essay dataset. |
CachePrune: Teaching LLMs What Not to Follow via KV-Cache Editing (2026.acl-long)
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Rui Wang, Junda Wu, Yu Xia, Tong Yu, Ruiyi Zhang, Ryan A. Rossi, Subrata Mitra, Lina Yao, Julian McAuley
| Challenge: | Existing Large Language Models exhibit critical vulnerability to indirect prompt injection attacks, where instructions injected within in the prompt context can override the user's intent. |
| Approach: | They propose a neural pruning algorithm that prunes neurons associated with instruction-following during KV cache encoding of the prompt context. |
| Outcome: | The proposed approach significantly reduces the attack success rate while preserving the model's ability to follow user instructions. |
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. |
Event Semantic Classification in Context (2024.findings-eacl)
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| Challenge: | In this work, we focus on the semantic classification of events in context to help machines gain a deeper understanding of events. |
| Approach: | They propose to integrate event semantics into downstream tasks to help machines understand events better. |
| Outcome: | The proposed model improves the understanding of events in context. |
Continual Learning Long Short Term Memory (2020.findings-emnlp)
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| Challenge: | Existing approaches to prevent catastrophic forgetting in neural networks are based on the stability-plasticity dilemma, but only a limited size of old data is available. |
| Approach: | They propose a Continual Learning Long Short Term Memory cell in Recurrent Neural Network (RNN) that considers the state of each individual task's output gates and the correlation of the states between tasks. |
| Outcome: | The proposed method significantly improves on spoken language understanding tasks over state-of-the-art approaches. |
Enhancing LLM Knowledge Learning through Generalization (2025.findings-emnlp)
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| Challenge: | Continued pre-training on paraphrased data has shown empirical promise for enhancing knowledge acquisition, but this approach is costly and unreliable as it relies on external models or manual effort for rewriting. |
| Approach: | They propose formatting-based data augmentation which diversifies documents conveying the same knowledge by altering document formats rather than their content. |
| Outcome: | The proposed methods improve generalization to diverse paraphrased contexts and enhance pre-training and instruction tuning. |
From Synthesis to Clinical Assistance: A Strategy-Aware Agent Framework for Autism Intervention based on Real Clinical Dataset (2026.acl-long)
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| Challenge: | Applied Behavior Analysis (ABA) is the gold standard for clinical intervention, but large language models struggle to adhere to its standardized procedures. |
| Approach: | They propose a strategy-aware framework to unify high-fidelity intervention dialogue synthesis and clinical decision support. |
| Outcome: | Experiments show that ASDAgent achieves nearly 80% strategic consistency with human experts. |
UnCo: Uncertainty-Driven Collaborative Framework of Large and Small Models for Grounded Multimodal NER (2025.emnlp-main)
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| Challenge: | Existing methods to identify unseen multimodal entities struggle with limited knowledge and generalization. |
| Approach: | They propose a framework that leverages the strengths of small fine-tuned models and MLLMs to generate unambiguous predictions. |
| Outcome: | Extensive experiments show that the proposed framework retains the in-domain knowledge of small models while utilizing the capabilities of MLLMs to handle unseen entities. |
Self-Consistency Boosts Calibration for Math Reasoning (2024.findings-emnlp)
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| Challenge: | Existing solutions for math reasoning tasks use semantic parsing or AST decoding, but performance can degrade dramatically even with slight changes to the questions. |
| Approach: | They propose three calibration methods based on self-consistency for math reasoning tasks. |
| Outcome: | The proposed methods bridge model confidence and accuracy better than existing methods based on p(True) or logit. |
OpenHuEval: Evaluating Large Language Model on Hungarian Specifics (2025.findings-acl)
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Haote Yang, Xingjian Wei, Jiang Wu, Noémi Ligeti-Nagy, Jiaxing Sun, Yinfan Wang, Győző Zijian Yang, Junyuan Gao, Jingchao Wang, Bowen Jiang, Shasha Wang, Nanjun Yu, Zihao Zhang, Shixin Hong, Hongwei Liu, Wei Li, Songyang Zhang, Dahua Lin, Lijun Wu, Gábor Prószéky, Conghui He
| Challenge: | Recent advances in Large Language Models (LLMs) represent significant strides toward artificial general intelligence (AGI). |
| Approach: | They introduce OpenHuEval, the first benchmark for LLMs focusing on the Hungarian language and specifics. |
| Outcome: | The framework reveals intrinsic patterns and mechanisms of LLMs in non-English languages, with Hungarian serving as an example. |
Topology-of-Question-Decomposition: Enhancing Large Language Models with Information Retrieval for Knowledge-Intensive Tasks (2025.coling-main)
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| Challenge: | Large language models (LLMs) are constrained to chaining immediate reasoning steps and relying solely on parametric knowledge. |
| Approach: | They propose a framework that activates retrieval only when necessary to improve answer accuracy. |
| Outcome: | Experiments show that the proposed framework improves performance in knowledge-intensive tasks. |
Leveraging Dependency Forest for Neural Medical Relation Extraction (D19-1)
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| Challenge: | Existing methods for medical relation extraction use dependency syntax as a source of features. |
| Approach: | They propose a method to extract relational information from medical literature by using dependency forests. |
| Outcome: | The proposed method outperforms the standard tree-based methods in the medical domain. |
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. |
Large Language Models in Bioinformatics: A Survey (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) are revolutionizing bioinformatics, enabling advanced analysis of DNA, RNA, proteins, and single-cell data. |
| Approach: | They examine the evolution of Large Language Models (LLMs) in bioinformatics and precision medicine by focusing on genomic sequence modeling, RNA structure prediction, protein function inference, and single-cell transcriptomics. |
| Outcome: | The proposed models are capable of predicting RNA structure and function and predicting single-cell transcriptomics. |
CoCo: Coherence-Enhanced Machine-Generated Text Detection Under Low Resource With Contrastive Learning (2023.emnlp-main)
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| Challenge: | Recent proposed methods fail to consider the linguistic structure of texts and lack the ability to handle the low-resource problem. |
| Approach: | They propose a coherence-based contrastive learning model named CoCo to detect MGTs under low-resource scenario. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two datasets and two self-constructed datasets. |
FOREVER: Forgetting Curve-Inspired Memory Replay for Language Model Continual Learning (2026.acl-long)
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Yujie Feng, Hao Wang, Jian Li, Xu Chu, Zhaolu Kang, Yiran Liu, Yasha Wang, Philip S. Yu, Xiao-Ming Wu
| Challenge: | Continual learning (CL) for large language models (LLMs) aims to enable sequential knowledge acquisition without catastrophic forgetting. |
| Approach: | They propose a framework that aligns replay schedules with a model-centric notion of time. |
| Outcome: | Experiments on three benchmarks show that FOREVER consistently mitigates catastrophic forgetting. |
ShifCon: Enhancing Non-Dominant Language Capabilities with a Shift-based Multilingual Contrastive Framework (2025.acl-long)
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Hengyuan Zhang, Chenming Shang, Sizhe Wang, Dongdong Zhang, Yiyao Yu, Feng Yao, Renliang Sun, Yujiu Yang, Furu Wei
| Challenge: | Experiments show that ShifCon significantly enhances the performance of non-dominant languages due to the imbalance in training data across languages. |
| Approach: | They propose a Shift-based multilingual Contrastive framework that aligns the internal forward process of other languages toward that of the dominant one. |
| Outcome: | The proposed framework significantly improves performance of non-dominant languages, particularly for low-resource ones. |
ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch (2026.acl-long)
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Zheng Liu, Honglin Lin, Xiaoyang Wang, Xin Gao, Yu Li, Mengzhang Cai, Yun Zhu, Zhanping Zhong, Qizhi Pei, Zhuoshi Pan, Xiaoran Shang, Conghui He, Bin Cui, Wentao Zhang, Lijun Wu
| Challenge: | Existing open-source vision language models lack high-quality training data for chart reasoning . current models are simplistic and repetitive, while associated QA pairs are prone to hallucinations . |
| Approach: | They propose a framework to synthesize complex charts and reliable reasoning data from scratch. |
| Outcome: | Experimental results show that ChartVerse-8B surpasses existing models in QA and difficulty . lack of high-quality training data hampers development of open-source models . |
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 . |
Privacy in Action: Towards Realistic Privacy Mitigation and Evaluation for LLM-Powered Agents (2025.findings-emnlp)
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Shouju Wang, Fenglin Yu, Xirui Liu, Xiaoting Qin, Jue Zhang, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan
| Challenge: | Existing benchmarks for privacy performance of LLM agents are limited to static, simplified scenarios. |
| Approach: | They propose a model-agnostic, contextual integrity based mitigation approach that effectively reduces privacy leakage from 36.08% to 7.30% on DeepSeek-R1 and from 33.06% to 8.32% on GPT-4o. |
| Outcome: | The proposed approach reduces privacy leakage from 36.08% to 7.30% on DeepSeek-R1 and from 33.06% to 8.32% on GPT-4o while preserving task helpfulness. |
Generating User-Engaging News Headlines (2023.acl-long)
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| Challenge: | Personalized news recommendation systems present the same headline to all users, making it difficult for them to understand the connection between their interests and the recommended article. |
| Approach: | They propose a framework that incorporates user profiling to generate personalized headlines and a combination of automated and human evaluation methods to determine user preference for personalized headline generation. |
| Outcome: | The proposed framework can generate personalized headlines that meet the needs of a diverse audience. |
One Cognitive Loop Is Enough: SODA unlocks Pure-Text Spatial Reasoning in Large Language Models (2026.acl-long)
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Shunwen Bai, Jiahuan Zhang, Haoran Huang, Yurun Wang, Jiale Liu, Yanxi Wu, Ningzhe Yu, Yudong Gao, Mingjun Cheng
| Challenge: | Existing large language models (LLMs) lack visual input, leading to errors in basic numerical comparisons. |
| Approach: | They propose a spatial OODA framework that integrates the OODAC cognitive loop into multiple control tasks and integrates it into LLMs. |
| Outcome: | The proposed model significantly improves the spatial reasoning capabilities of large language models across multiple scenarios including SPOD-Bench, SPACE and applications. |
Detecting Subtle Differences between Human and Model Languages Using Spectrum of Relative Likelihood (2024.emnlp-main)
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| Challenge: | Existing methods for detecting modelgenerated texts from human texts are limited by the fact that absolute likelihood values of texts are bound to certain linguistic and cognitive constraints. |
| Approach: | They propose to use relative likelihood values instead of absolute ones to extract useful features from the spectrum-view of likelihood for the human-model text detection task. |
| Outcome: | The proposed method can reveal subtle differences between human and model languages, which find theoretical roots in psycholinguistics studies. |
Revisiting Data Reconstruction Attacks on Real-world Dataset for Federated Natural Language Understanding (2024.lrec-main)
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Zhuo Zhang, Jintao Huang, Xiangjing Hu, Jingyuan Zhang, Yating Zhang, Hui Wang, Yue Yu, Qifan Wang, Lizhen Qu, Zenglin Xu
| Challenge: | Existing DRA methods fail to accurately recover the original text of real-world privacy data. |
| Approach: | They propose to use a real-world privacy dataset to examine the performance of federated learning (FL) methods. |
| Outcome: | The proposed method improves on a real-world privacy dataset and shows that the tokens within a recovery sentence are disordered and intertwined with tokens from other sentences in the same training batch. |
Seed-Guided Topic Discovery with Out-of-Vocabulary Seeds (2022.naacl-main)
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| Challenge: | Existing topic models adopt a fully unsupervised setting and their discovered topics may not reflect user preferences well due to their unsupervised nature. |
| Approach: | They propose a framework that allows out-of-vocabulary seeds to be used to find latent topics from text corpora. |
| Outcome: | The proposed framework can find topics that are never seen in the corpus and can benefit from the general knowledge of pre-trained language models. |
DeFine: Decision-Making with Analogical Reasoning over Factor Profiles (2025.findings-acl)
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| Challenge: | Large language models are ideal for decision-making, but they can be difficult to process when they are verbose and include repetition, hedging, and vagueness. |
| Approach: | They propose a framework that constructs probabilistic factor profiles from complex scenarios and integrates them with analogical reasoning to guide LLMs in making decisions in new situations. |
| Outcome: | The proposed framework separates the tasks of quantifying uncertainty and incorporating it into LLM decision-making. |
DebateCoder: Towards Collective Intelligence of LLMs via Test Case Driven LLM Debate for Code Generation (2025.acl-long)
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Jizheng Chen, Kounianhua Du, Xinyi Dai, Weiming Zhang, Xihuai Wang, Yasheng Wang, Ruiming Tang, Weinan Zhang, Yong Yu
| Challenge: | Existing debate-based approaches to code generation are limited due to several reasons: 1) Reliance on different instances of the same LLM for debate, 2) under-utilization of test cases, and 3) reliance on third-party moderators for result consolidation and decision-making. |
| Approach: | They propose to use test cases to analyze code and identify bugs while opposing models generate test cases for each other to challenge each other's code during the debate process. |
| Outcome: | The proposed model collects intelligence of LLMs via test case-driven debate for code generation. |
F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching (2025.acl-long)
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| Challenge: | Recent research in Text-to-Speech (TTS) has experienced great advancement . current models can synthesize speech for any given text and mimic the speaker of audio prompt. |
| Approach: | They propose a fully non-autoregressive text-to-speech system based on flow matching with Diffusion Transformer (DiT) without complex designs such as duration model, text encoder, and phoneme alignment, the text input is simply padded with filler tokens to the same length as input speech, and then denoising is performed for speech generation. |
| Outcome: | The proposed system achieves an inference RTF of 0.15, which is greatly improved compared to state-of-the-art diffusion-based models. |
Evaluating Text Generation Quality Using Spectral Distances of Surprisal (2025.findings-emnlp)
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| Challenge: | Existing metric fails to capture text surprisal, but FACE-2 produces stronger agreement with human preferences. |
| Approach: | They propose a new automatic evaluation metric for open-ended text generation . they propose metric that extracts the dynamic patterns (spectrum) of text surprisal . |
| Outcome: | The proposed metric outperforms existing methods in revealing the model scaling effect . it produces stronger agreement with human preferences from a large human-annotated dataset . |
Cluster-Former: Clustering-based Sparse Transformer for Question Answering (2021.findings-acl)
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| Challenge: | Existing models for encoding long sequences in deep learning suffer from high latency and memory demands. |
| Approach: | They propose a clustering-based sparse Transformer framework to perform attention across chunked sequences. |
| Outcome: | The proposed framework achieves state-of-the-art on several major QA benchmarks. |
LM-Infinite: Zero-Shot Extreme Length Generalization for Large Language Models (2024.naacl-long)
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| Challenge: | Currently, large language models (LLMs) train on short text segments due to the computational overhead quadratic in the input lengths of their Transformer architectures. |
| Approach: | They propose a method that allows LLMs pre-trained with 2K or 4K-long segments to generalize to up to 200M length inputs while retaining perplexity. |
| Outcome: | The proposed method achieves 2.7 decoding speed up and 7.5 memory saving over the original model. |
Skeleton-Guided-Translation: A Benchmarking Framework for Code Repository Translation with Fine-Grained Quality Evaluation (2025.findings-emnlp)
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Xing Zhang, Jiaheng Wen, Fangkai Yang, Yu Kang, Pu Zhao, Junhao Wang, Maoquan Wang, Yufan Huang, Shengyu Fu, Elsie Nallipogu, Qingwei Lin, Yingnong Dang, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Existing code translation benchmarks focus on individual functions, overlooking repository-level challenges like intermodule coherence and dependency management. |
| Approach: | They propose a framework for benchmarking Java-to-C# translation at the repository level . it uses a translation framework guided by skeletons and fine-grained quality evaluation . |
| Outcome: | The proposed framework improves Java-to-C# translation quality at the repository level. |
Context-aware Information-theoretic Causal De-biasing for Interactive Sequence Labeling (2022.findings-emnlp)
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| Challenge: | Existing deep learning models for sequence labeling are expensive and time-consuming. |
| Approach: | They propose an interactive sequence labeling that allows training directly with the user feedback . they identify context and feedback biases by formulating interactive sequence labels via a Structural Causal Model. |
| Outcome: | The proposed approach can effectively alleviate the biases and can be learnt with the user feedback. |
A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery (2024.emnlp-main)
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| Challenge: | Existing surveys on scientific LLMs focus on one or two fields or a single modality. |
| Approach: | They survey 260 scientific LLMs and examine their architectures and pre-training techniques . they also discuss commonalities and differences between LLM architectures . |
| Outcome: | The proposed model architectures and evaluation techniques are used to improve scientific discovery. |
T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Concepts (2025.acl-long)
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Ziwei Huang, Wanggui He, Quanyu Long, Yandi Wang, Haoyuan Li, Zhelun Yu, Fangxun Shu, Weilong Dai, Hao Jiang, Fei Wu, Leilei Gan
| Challenge: | Existing studies on text-to-image (T2I) models focus on text alignment, image quality, and object composition capabilities. |
| Approach: | They propose a T2I-FactualBench benchmark to evaluate the factuality of knowledge-intensive concept generation. |
| Outcome: | The proposed framework evaluates the factuality of knowledge-intensive concept generation tasks. |
TRAMS: Training-free Memory Selection for Long-range Language Modeling (2023.findings-emnlp)
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| Challenge: | Existing methods like Transformer-XL are plagued by ineffective memory selections due to the high number of tokens involved in attention calculation. |
| Approach: | They propose a plug-and-play strategy that selects tokens participating in attention calculation based on one simple metric and ignores the other ones. |
| Outcome: | The proposed strategy keeps tokens with high attention scores and ignores the other ones on word-level and character-level benchmarks without additional training or adding additional parameters. |
Identifying the Periodicity of Information in Natural Language (2026.acl-long)
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| Challenge: | Existing methods to detect periodicity of information in natural language are based on a canonical periodicity detection algorithm. |
| Approach: | They propose a method to detect periods in surprisal sequences in natural language . they propose to use this method to identify periods outside the distributions of typical units . |
| Outcome: | The proposed method can detect significant periods in a single document. |
Mitigating Visual Knowledge Forgetting in MLLM Instruction-tuning via Modality-decoupled Gradient Descent (2025.findings-emnlp)
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Junda Wu, Yuxin Xiong, Xintong Li, Yu Xia, Ruoyu Wang, Yu Wang, Tong Yu, Sungchul Kim, Ryan A. Rossi, Lina Yao, Jingbo Shang, Julian McAuley
| Challenge: | Existing fine-tuning and continual learning methods compress visual representations and emphasize task alignment over visual retention. |
| Approach: | They propose a modality-decoupled gradient descent (MDGD) that regulates gradient updates to preserve effective rank of visual features and explicitly disentangles visual learning from task-specific alignment. |
| Outcome: | The proposed model reduces visual forgetting and improves visual retention . it disentangles visual learning from task-specific alignment and preserves effective rank . |
Towards Context-Aware Code Comment Generation (2020.findings-emnlp)
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| Challenge: | Existing methods for code comments generate comments manually, but they suffer from poor scalability and high maintenance cost due to the expensive overhead of writing comment templates. |
| Approach: | They propose a method to automatically generate code comments at a function level by targeting object-oriented programming languages. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods and is comparable with existing methods. |
Exploiting the Index Gradients for Optimization-Based Jailbreaking on Large Language Models (2025.coling-main)
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| Challenge: | Despite advances in training Large Language Models, they remain vulnerable to jailbreak, an adversarial attack method. |
| Approach: | They propose an adversarial jailbreak algorithm that exploits the gradient information of the suffix tokens to accelerate the optimization process. |
| Outcome: | The proposed model achieves 1.5x speedup while maintaining high attack success rates. |
MedAdapter: Efficient Test-Time Adaptation of Large Language Models Towards Medical Reasoning (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have improved generation and reasoning capabilities compared to traditional BERT-sized models due to massive number of parameters and extensive pre-training on vast textual corpora. |
| Approach: | They propose a unified post-hoc adapter for test-time adaptation of large language models . they propose to fine-tune only a small BERT-sized adapter to rank candidate LLMs . |
| Outcome: | The proposed adapter improves performance on four biomedical tasks without requiring computational resources or sharing data with third parties. |
Make-A-Voice: Revisiting Voice Large Language Models as Scalable Multilingual and Multitask Learners (2024.acl-long)
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Rongjie Huang, Chunlei Zhang, Yongqi Wang, Dongchao Yang, Jinchuan Tian, Zhenhui Ye, Luping Liu, Zehan Wang, Ziyue Jiang, Xuankai Chang, Jiatong Shi, Chao Weng, Zhou Zhao, Dong Yu
| Challenge: | Large language models (LLMs) have been used for general-purpose interfaces across multiple tasks and languages. |
| Approach: | They propose to use large language models as a general-purpose interface across multiple tasks and languages. |
| Outcome: | The proposed model performs better on 200K hours of 6-language data for voice generation applications. |
It is AI’s Turn to Ask Humans a Question: Question-Answer Pair Generation for Children’s Story Books (2022.acl-long)
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| Challenge: | Existing question answering (QA) techniques are created mainly to answer questions asked by humans, but in educational applications, teachers often need to decide what questions to ask . |
| Approach: | They propose to use a fairytale-themed storybook as input to generate QA pairs that can test a student's comprehension skills. |
| Outcome: | The proposed system outperforms state-of-the-art QAG baseline systems and builds an interactive story-telling application for the future real-world deployment. |
I2E: From Image Pixels to Actionable Interactive Environments for Text-Guided Image Editing (2026.acl-long)
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Jinghan Yu, Junhao Xiao, Chenyu Zhu, Jiaming Li, Jia Li, HanMing Deng, Xirui Wang, Guoli Jia, Jianjun Li, Xiang Bai, Bowen Zhou, Zhiyuan Ma
| Challenge: | Existing text-guided image editing methods rely on end-to-end pixel-level inpainting paradigm . existing models lack such intermediate representations and Reasoning-then-action process . |
| Approach: | They propose a "Decompose-then-Action" paradigm that revisits image editing as an actionable interaction process within a structured environment. |
| Outcome: | The proposed paradigm outperforms existing methods in compositional editing tasks. |
Mixture of Structural-and-Textual Retrieval over Text-rich Graph Knowledge Bases (2025.findings-acl)
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Yongjia Lei, Haoyu Han, Ryan A. Rossi, Franck Dernoncourt, Nedim Lipka, Mahantesh M Halappanavar, Jiliang Tang, Yu Wang
| Challenge: | Existing methods for textual and structural retrieval ignore mutual reinforcement and only use structural retrievals for text-rich Graph Knowledge Bases (TG-KBs). |
| Approach: | They propose a Mixture of Structural-and-Textual Retrieval to retrieve textual and structural knowledge via a Planning-Reasoning-Organizing framework. |
| Outcome: | Experiments show that the proposed framework performs better than existing methods in analyzing TG-KBs and integrating structural trajectories for candidate reranking. |
Pruning before Fine-tuning: A Retraining-free Compression Framework for Pre-trained Language Models (2024.lrec-main)
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| Challenge: | Structured pruning is an effective technique for compressing pre-trained language models (PLMs), but it requires retraining, leading to additional computational overhead. |
| Approach: | They propose a task-specific pruning framework that prunes redundant modules of pre-trained language models before fine-tuning them. |
| Outcome: | The proposed pruning framework achieves higher performance on GLUE, SQUAD, WikiText-2, Wik-103, and PTB datasets while reducing the time required for fine-tuning. |
A Unified Temporal Knowledge Graph Reasoning Model Towards Interpolation and Extrapolation (2024.acl-long)
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| Challenge: | Existing methods for temporal knowledge graphs de-emphasize temporal correlations between facts sequences and ignore inferring clues from missing facts. |
| Approach: | They propose a Temporal PAth-based reasoning model that is robust to ambiguous temporal data. |
| Outcome: | The proposed model outperforms SOTA methods on the link prediction task. |
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. |
Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints (2020.acl-main)
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| Challenge: | Existing methods for text generation ignore faithfulness between generated text and table . current methods ignore faithfulity, leading to generated information that goes beyond table content . |
| Approach: | They propose a Transformer-based generation framework to enforce faithfulness between generated text and table . they propose metric to evaluate faithfulness and automatic metric for automatic generating . |
| Outcome: | The proposed framework outperforms state-of-the-art methods in automatic evaluations and human evaluations. |
ATFormer: A Learned Performance Model with Transfer Learning Across Devices for Deep Learning Tensor Programs (2023.emnlp-main)
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| Challenge: | Compilation-based methods with performance models have poor measurement accuracy and transferability between platforms. |
| Approach: | They propose a compiler that automatically generates tensors and automatically tunes them for different hardware platforms. |
| Outcome: | The proposed model reduces inference time and costs on modern DNN benchmarks. |
BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers (2024.emnlp-main)
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Ran Xu, Wenqi Shi, Yue Yu, Yuchen Zhuang, Yanqiao Zhu, May Dongmei Wang, Joyce Ho, Chao Zhang, Carl Yang
| Challenge: | Developing effective biomedical retrieval models is important for excelling at knowledge-intensive biomedically tasks but still challenging due to the lack of sufficient publicly annotated biomedic data and computational resources. |
| Approach: | They propose a series of dense retrievers for enhancing biomedical retrieval via unsupervised pre-training on large biomedically corpora, followed by instruction fine-tuning on a combination of labeled datasets and synthetic pairs. |
| Outcome: | Experiments on 5 biomedical tasks across 11 datasets confirm the performance of the retrieval model on various biomedically demanding tasks. |
Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models (2026.findings-acl)
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Hengyuan Zhang, Zhihao Zhang, Ercong Nie, Mingyang Wang, Zunhai Su, Yiwei Wang, Qianli Wang, Shuzhou Yuan, Xufeng Duan, Qibo Xue, Zeping Yu, Chenming Shang, Xiao Liang, Jing Xiong, Hui Shen, Chaofan Tao, Zhengwu Liu, Senjie Jin, Zhiheng Xi, Dongdong Zhang, Sophia Ananiadou, Tao Gui, Ruobing Xie, Hayden Kwok-Hay So, Hinrich Schuetze, Xuanjing Huang, Qi Zhang, Ngai Wong
| Challenge: | Existing literature on mechanistic interpretation (MI) treats it as an observational science, leaving practical applications underexplored. |
| Approach: | They propose a survey structured around the pipeline to identify and improve MI models. |
| Outcome: | The proposed framework enables tangible improvements in Alignment, Capability, and Efficiency. |
DI-BENCH: Benchmarking Large Language Models on Dependency Inference with Testable Repositories at Scale (2025.findings-acl)
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Linghao Zhang, Junhao Wang, Shilin He, Chaoyun Zhang, Yu Kang, Bowen Li, Jiaheng Wen, Chengxing Xie, Maoquan Wang, Yufan Huang, Elsie Nallipogu, Qingwei Lin, Yingnong Dang, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
| Challenge: | Existing studies highlight that dependency-related issues cause over 40% of observed runtime errors on the generated repository. |
| Approach: | They propose a large-scale benchmark and evaluation framework specifically designed to assess LLMs’ capability on dependency inference. |
| Outcome: | The proposed model achieves only a 48% execution pass rate on Python, indicating room for improvement. |
Simple Recurrent Units for Highly Parallelizable Recurrence (D18-1)
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| Challenge: | recurrent neural networks scale poorly due to the intrinsic difficulty in parallelizing their state computations. |
| Approach: | They propose a simple recurrent unit that provides expressive recurrence and allows highly parallel implementation. |
| Outcome: | The proposed model achieves 5—9x speed-up over cuDNN-optimized LSTM on classification and question answering datasets and delivers stronger results than LS and convolutional models. |
Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization (2023.acl-long)
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Pengcheng He, Baolin Peng, Song Wang, Yang Liu, Ruochen Xu, Hany Hassan, Yu Shi, Chenguang Zhu, Wayne Xiong, Michael Zeng, Jianfeng Gao, Xuedong Huang
| Challenge: | Z-Code++ is a pre-trained language model optimized for abstractive text summarization. |
| Approach: | They propose a pre-trained language model optimized for abstractive text summarization that uses a two-phase pre-training technique to improve model's performance. |
| Outcome: | The proposed model outperforms the competing models on low-resource summarization tasks in zero-shot and few-shot settings. |
Granular Entity Mapper: Advancing Fine-grained Multimodal Named Entity Recognition and Grounding (2024.findings-emnlp)
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| Challenge: | Existing methods for fine-grained content extraction are limited by long-tailed distribution of textual entity categories and performance of object detectors. |
| Approach: | They propose a multi-granularity entity recognition module and a reranking module to integrate hierarchical information of entity categories, visual cues, and external textual resources collectively. |
| Outcome: | The proposed framework achieves state-of-the-art on the fine-grained content extraction task. |
SOTOPIA-π: Interactive Learning of Socially Intelligent Language Agents (2024.acl-long)
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Ruiyi Wang, Haofei Yu, Wenxin Zhang, Zhengyang Qi, Maarten Sap, Yonatan Bisk, Graham Neubig, Hao Zhu
| Challenge: | Existing studies on building language agents have not addressed this social learning gap. |
| Approach: | They propose an interactive learning method that improves the social intelligence of language agents by using behavior cloning and self-reinforcement based training on filtered social interaction data. |
| Outcome: | The proposed method allows a 7B LLM to reach the social goal completion ability of an expert model (GPT-4-based agent) without the loss of more generic abilities, such as the ability to answer knowledge-based questions. |
Out-of-Domain Detection for Low-Resource Text Classification Tasks (D19-1)
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| Challenge: | Existing methods for OOD detection and ID classification tasks require massive amounts of ID labeled data and no OOD labeles. |
| Approach: | They propose to use OOD-resistant Prototypical Network to detect OOD cases with limited in-domain (ID) training data to solve this task. |
| Outcome: | The proposed solution outperforms state-of-the-art methods in zero-shot OOD detection task while maintaining a competitive performance on ID classification task. |
MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering (2024.acl-long)
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| Challenge: | Recent advances in few-shot question answering rely on pre-trained large language models and fine-tuning in specific settings. |
| Approach: | They propose to select the most informative data for fine-tuning to improve efficiency . they use an approximate graph algorithm and unsupervised question generation to generate QA pairs . |
| Outcome: | The proposed framework improves the performance of the few-shot question answering task on the open-domain QA task. |
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. |
PairCFR: Enhancing Model Training on Paired Counterfactually Augmented Data through Contrastive Learning (2024.acl-long)
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| Challenge: | Recent research shows that training with CAD may lead models to overly focus on modified features while ignoring other important contextual information. |
| Approach: | They propose to use contrastive learning to promote global feature alignment and learning counterfactual clues to improve model performance. |
| Outcome: | The proposed method outperforms the state-of-the-art on out-of distribution (OOD) datasets. |
Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations (2024.naacl-long)
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Sihao Chen, Hongming Zhang, Tong Chen, Ben Zhou, Wenhao Yu, Dian Yu, Baolin Peng, Hongwei Wang, Dan Roth, Dong Yu
| Challenge: | Sentence embeddings are typically learned to recognize the semantic relation between two text inputs. |
| Approach: | They introduce a contrastively-learned contextual embedding model for fine-grained semantic representation of text. |
| Outcome: | The proposed model is able to produce contextual embeddings corresponding to different atomic propositions, i.e. semantic equivalence between propositions across different text sequences. |
RoseRAG: Robust Retrieval-augmented Generation with Small-scale LLMs via Margin-aware Preference Optimization (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have impressive performance but face high computational costs and latency, limiting their deployment in resource-constrained settings. |
| Approach: | They propose a robust RAG framework for large language models via Margin-aware Preference Optimization to enhance the accuracy and reliability of SLMs. |
| Outcome: | The proposed framework surpasses state-of-the-art benchmarks on three open-domain question answering tasks. |
Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset (2022.emnlp-main)
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Haolin Deng, Yanan Zhang, Yangfan Zhang, Wangyang Ying, Changlong Yu, Jun Gao, Wei Wang, Xiaoling Bai, Nan Yang, Jin Ma, Xiang Chen, Tianhua Zhou
| Challenge: | Existing EE datasets define fixed event types and design specific schemas for each of them, failing to cover diverse events emerging from the online text. |
| Approach: | They propose to use a sentence-level dataset to benchmark Open Event Extraction without restricting event types. |
| Outcome: | The proposed dataset contains more than 42,000 news titles in 34 topics collected from Chinese web pages. |
MCIL: Multimodal Counterfactual Instance Learning for Low-resource Entity-based Multimodal Information Extraction (2024.lrec-main)
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| Challenge: | Existing methods to perform multimodal information extraction only investigated entity-based tasks under supervised learning with adequate labeled data. |
| Approach: | They propose to investigate the entity-based MIE tasks under the low-resource settings by decomposing the features into image, entity, and context factors. |
| Outcome: | The proposed method is able to perform on two public MIE benchmark datasets and the experimental results confirm it. |
Unregulated Chinese-to-English Data Expansion Does NOT Work for Neural Event Detection (2022.coling-1)
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| Challenge: | Experimental results show that cross-language data expansion results in performance degradation. |
| Approach: | They leverage cross-language data expansion and retraining to enhance neural Event Detection on English ACE corpus. |
| Outcome: | The proposed method improves ED performance by 1.6% over the straight data combination. |
The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding (2020.acl-demos)
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Xiaodong Liu, Yu Wang, Jianshu Ji, Hao Cheng, Xueyun Zhu, Emmanuel Awa, Pengcheng He, Weizhu Chen, Hoifung Poon, Guihong Cao, Jianfeng Gao
| Challenge: | MT-DNN is an open-source natural language understanding toolkit . it allows researchers and developers to train customized deep learning models . |
| Approach: | They present MT-DNN, an open-source natural language understanding toolkit . it is designed to facilitate rapid customization for a broad spectrum of NLU tasks . MT supports multi-task knowledge distillation, which can substantially compress a deep neural model without significant performance drop. |
| Outcome: | The proposed model can significantly compress a large model without significant performance drop. |
SPEED++: A Multilingual Event Extraction Framework for Epidemic Prediction and Preparedness (2024.emnlp-main)
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Tanmay Parekh, Jeffrey Kwan, Jiarui Yu, Sparsh Johri, Hyosang Ahn, Sreya Muppalla, Kai-Wei Chang, Wei Wang, Nanyun Peng
| Challenge: | Prior studies focused on English posts to provide early warnings for epidemic prediction, but these work focused on non-English posts. |
| Approach: | They propose a multilingual event extraction framework for extracting epidemic event information for any disease and language using 5.1K tweets in four languages. |
| Outcome: | The proposed framework can provide epidemic warnings for COVID-19 in its earliest stages in Dec 2019 (3 weeks before global discussions) and aggregate community epidemic discussions like symptoms and cure measures, aiding misinformation detection and public attention monitoring. |
ConKI: Contrastive Knowledge Injection for Multimodal Sentiment Analysis (2023.findings-acl)
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Yakun Yu, Mingjun Zhao, Shi-ang Qi, Feiran Sun, Baoxun Wang, Weidong Guo, Xiaoli Wang, Lei Yang, Di Niu
| Challenge: | Existing methods for multimodal sentiment analysis focus on general knowledge, which is inadequate to identify specific sentiments across modalities. |
| Approach: | They propose a method where specific-knowledge representations for each modality can be learned together with general knowledge representations via knowledge injection based on an adapter architecture. |
| Outcome: | The proposed method outperforms all prior methods on three popular benchmarks on multimodal sentiment analysis metrics. |
Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction (2023.emnlp-main)
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| Challenge: | Existing evaluation benchmarks focus on pairwise matching, ignoring robustness . current models exhibit frustrating degradation, with a maximum drop of 23.43 F1 score . |
| Approach: | They propose a benchmark that simulates the evaluation of open information extraction models in the real world . they perform experiments on typical models published in the last decade and a representative large language model . |
| Outcome: | The proposed model is rated robust on a knowledge-invariant clique with different syntactic and expressive forms. |
LlamaCare: An Instruction Fine-Tuned Large Language Model for Clinical NLP (2024.lrec-main)
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| Challenge: | Large language models have shown remarkable abilities in generating natural texts . applying LLMs to clinical domain still poses significant challenges . |
| Approach: | They propose a method of instruction fine-tuning for adapting large language models to clinical domains . they generate instructions, inputs, and outputs covering a wide spectrum of clinical services . |
| Outcome: | The proposed method outperforms baseline LLMs on clinical tasks . it requires domain adaptation, task-specific learning, and reliability . |
A Neural Transition-based Model for Nested Mention Recognition (D18-1)
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| Challenge: | Existing methods to recognize nested mentions are based on Stack-LSTM . nesting mentions can be used for downstream tasks like question answering and relation extraction. |
| Approach: | They propose a scalable transition-based method to model the nested structure of mentions. |
| Outcome: | The proposed method gets the state-of-the-art performance in ACE datasets showing its effectiveness in detecting nested mentions. |
VCB Bench: An Evaluation Benchmark for Audio-Grounded Large Language Model Conversational Agents (2026.findings-acl)
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Jiliang Hu, Wenfu Wang, Zuchao Li, Chenxing Li, Yiyang Zhao, Hanzhao Li, Liqiang Zhang, Meng Yu, Dong Yu
| Challenge: | despite advances in multimodal conversational systems, current benchmarks lack comprehensive evaluation across key dimensions. |
| Approach: | They propose a Chinese benchmark built exclusively on real human speech to fill this gap . they assess LALMs across three complementary axes: instruction following, knowledge understanding, robustness . |
| Outcome: | VCB Bench assesses LALMs across three complementary axes: instruction following, knowledge understanding, and robustness . VCBM Bench provides reproducible and fine-grained framework for Chinese voice chat bots . results show significant performance disparities and offer tangible insights for future improvements . |
EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records (2024.emnlp-main)
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Wenqi Shi, Ran Xu, Yuchen Zhuang, Yue Yu, Jieyu Zhang, Hang Wu, Yuanda Zhu, Joyce Ho, Carl Yang, May Dongmei Wang
| Challenge: | EHRAgent enables clinicians to interact with EHRs using natural language . reliance on rule-based conversion systems often necessitates additional training or effort from data engineers. |
| Approach: | They propose a large language model agent that generates and executes code in natural language to facilitate clinicians in directly interacting with EHRs. |
| Outcome: | The proposed agent outperforms the strongest baseline by up to 29.6% in success rate on three real-world EHR datasets. |
Miner: Mining Intrinsic Mastery for Data-Efficient RL in Large Reasoning Models (2026.acl-long)
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| Challenge: | Current critic-free RL methods for large reasoning models suffer from severe inefficiency when training on positive homogeneous prompts. |
| Approach: | They propose a method that repurposes the policy’s intrinsic uncertainty as a self-supervised reward signal, with no external supervision, auxiliary models, or additional inference cost. |
| Outcome: | Evaluated across six reasoning benchmarks on Qwen3-4B and Qwend3-8B base models, the proposed method achieves state-of-the-art performance among the other four methods. |
Curriculum Pre-training for End-to-End Speech Translation (2020.acl-main)
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| Challenge: | End-to-end speech translation requires a powerful encoder to transcribe, understand and learn cross-lingual semantics simultaneously. |
| Approach: | They propose a curriculum pre-training method that includes an elementary course for transcription learning and two advanced courses for understanding the utterance and mapping words in two languages. |
| Outcome: | The proposed method improves on En-De and En-Fr speech translation benchmarks. |
LLM×MapReduce-V3: Enabling Interactive In-Depth Survey Generation through a MCP-Driven Hierarchically Modular Agent System (2025.emnlp-demos)
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Yu Chao, Siyu Lin, Xiaorong Wang, Zhu Zhang, Zihan Zhou, Haoyu Wang, Shuo Wang, Jie Zhou, Zhiyuan Liu, Maosong Sun
| Challenge: | Generating high-quality long-form survey articles poses significant challenges to AI Agent systems. |
| Approach: | They propose a hierarchically modular agent system for long-form survey generation . they use atomic models to implement skeleton initialization, digest construction, and skelet refinement . human evaluations demonstrate system surpasses representative baselines . |
| Outcome: | The proposed system surpasses representative baselines in both content depth and length, highlighting the strength of MCP-based modular planning. |
Self-Error-Instruct: Generalizing from Errors for LLMs Mathematical Reasoning (2025.acl-long)
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Erxin Yu, Jing Li, Ming Liao, Qi Zhu, Boyang Xue, Minghui Xu, Baojun Wang, Lanqing Hong, Fei Mi, Lifeng Shang
| Challenge: | Existing approaches to learning from errors synthesize training data by extrapolating from isolated bad cases, thereby failing to generalize the extensive patterns inherent within these cases. |
| Approach: | They propose a framework that synthesizes more generalized training data from isolated bad cases by extrapolating from isolated cases. |
| Outcome: | The proposed framework synthesizes more generalized training data to address these model weaknesses. |
Joint Intent Detection and Entity Linking on Spatial Domain Queries (2020.findings-emnlp)
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| Challenge: | Spatial domain queries have unique properties making them more challenging for language understanding than common conversational queries. |
| Approach: | They propose a language understanding framework for spatial domain queries that jointly learns the intent detection and entity linking tasks on a voice assistant service. |
| Outcome: | The proposed framework outperforms baseline methods with a significant margin. |
ImPart: Importance-Aware Delta-Sparsification for Improved Model Compression and Merging in LLMs (2025.acl-long)
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| Challenge: | Recent approaches to reduce resource requirements for task-specific large language models have been developed. |
| Approach: | They propose a delta compression approach that optimizes for importance of a model . they use SVD to dynamically adjust the sparsity ratios of different vectors based on their importance . |
| Outcome: | The proposed approach achieves state-of-the-art in retaining task-specific knowledge even at high sparsity ratios. |
Zero Pronoun Resolution with Attention-based Neural Network (C18-1)
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| Challenge: | Recent neural network methods for zero pronoun resolution use contextual information to encode the zero pronomins since they contain no actual content. |
| Approach: | They propose a self-attention mechanism for encoding zero pronouns that focus on some informative parts of the associated texts and produce an efficient way of encode them. |
| Outcome: | The proposed model significantly surpasses existing Chinese zero pronoun resolution baseline systems. |
SoftMCL: Soft Momentum Contrastive Learning for Fine-grained Sentiment-aware Pre-training (2024.lrec-main)
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| Challenge: | Existing methods for pre-training language models capture general language understanding but fail to distinguish affective impact of a particular context to a specific word. |
| Approach: | They propose a soft momentum contrastive learning method for fine-grained sentiment-aware pre-training that uses valence ratings as soft-label supervision instead of hard labels. |
| Outcome: | The proposed method improves on four sentiment-related tasks and the results are published online. |
TransBench: Breaking Barriers for Transferable Graphical User Interface Agents in Dynamic Digital Environments (2025.findings-acl)
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Yuheng Lu, Qian Yu, Hongru Wang, Zeming Liu, Wei Su, Yanping Liu, Yuhang Guo, Maocheng Liang, Yunhong Wang, Haifeng Wang
| Challenge: | Existing GUI agents struggle to adapt to dynamic and interconnected nature of real-world digital environments, authors show . |
| Approach: | They propose a benchmark to evaluate the transferability of GUI agents across three key dimensions . transBench includes 15 app categories with diverse functionalities . |
| Outcome: | The proposed benchmark shows that existing GUI agents struggle to adapt to dynamic, interconnected environments. |
Do Multi-hop Readers Dream of Reasoning Chains? (D19-58)
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| Challenge: | Existing models for multihop reasoning are limited in their performance . multi-hop reasoning requires the ability to gather information from multiple passages . |
| Approach: | They propose a method that provides the full reasoning chain of multiple passages instead of just one final passage where the answer appears. |
| Outcome: | The proposed model improves on existing models by providing the full reasoning chain of multiple passages instead of just one final passage where the answer appears. |
LORE: Continual Logit Rewriting Fosters Faithful Generation (2025.findings-emnlp)
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| Challenge: | Using aspect-oriented summarization as a case study, we propose **LOgit REwriting**, a new controlled generation paradigm which can be faithful to external knowledge and to the LLM’s intentions. |
| Approach: | They propose a controlled generation paradigm which can be faithful to external knowledge and to the LLM's intentions. |
| Outcome: | The proposed paradigm can be faithful to external knowledge and to the LLM's intentions while balancing that with accuracy. |
Coarse-to-Fine Contrastive Learning in Image-Text-Graph Space for Improved Vision-Language Compositionality (2023.emnlp-main)
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| Challenge: | Recent studies have highlighted severe limitations of contrastive learning models in their ability to perform compositional reasoning over objects, attributes, and relations. |
| Approach: | They propose a graph decomposition framework and negative mining techniques to improve attribute binding and relation understanding of scene graphs. |
| Outcome: | The proposed approach improves attribute binding, relation understanding, generalization, and productivity on multiple benchmarks. |
SPAGBias: Uncovering and Tracing Structured Spatial Gender Bias in Large Language Models (2026.acl-long)
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| Challenge: | Large language models (LLMs) are being used in urban planning but there is concern that they reproduce or amplify such biases. |
| Approach: | They propose a framework to evaluate spatial gender bias in large language models . they use a taxonomy of 62 urban micro-spaces, a prompt library and three diagnostic layers . |
| Outcome: | The proposed framework identifies structured gender-space associations that go beyond the public-private divide, forming nuanced micro-level mappings. |
Non-parallel Accent Transfer based on Fine-grained Controllable Accent Modelling (2023.findings-emnlp)
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| Challenge: | Existing accent transfer methods rely on parallel data or speech recognition models. |
| Approach: | They propose to use mutual information learning to disentangle accent features and control the accent of the generated speech during the inference time. |
| Outcome: | The proposed framework achieves superior performance to baseline models in accentedness and audio quality. |
Eliciting Implicit Acoustic Styles from Open-domain Instructions to Facilitate Fine-grained Controllable Generation of Speech (2025.emnlp-main)
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| Challenge: | Current work relies on pre-defined rules or templates to control the style of speech. |
| Approach: | They propose to use open-domain instructions to generate speech with the acoustic style that meets users’ needs based on their instructions. |
| Outcome: | The proposed model can be used to generate speech with the acoustic style that meets users’ needs based on open-domain instructions. |
EXPLAIN: Enhancing Retrieval-Augmented Generation with Entity Summary (2025.acl-industry)
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Yaozhen Liang, Xiao Liu, Jiajun Yu, Zhouhua Fang, Qunsheng Zou, Linghan Zheng, Yong Li, Zhiwei Liu, Haishuai Wang
| Challenge: | Existing document question answering methods reduce inference costs and input tokens. |
| Approach: | They propose a retrieval-augmented generation method that automatically extracts useful entities and generates summaries from documents. |
| Outcome: | The proposed method surpasses baseline retrieval-augmented generation (RAG) and long-context question answering (LC) methods achieve higher accuracy by processing entire documents, but at the cost of increased computational Corresponding authors. |
A Unified Framework for Modeling Heterogeneous Financial Data via Dual-Granularity Prompting (2026.acl-industry)
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| Challenge: | Recent industrial credit scoring models rely heavily on manually tuned statistical learning methods due to the complexity of heterogeneous financial data and the challenge of modeling evolving creditworthiness. |
| Approach: | They propose a framework that reformulates credit scoring as a multi-scale sequential learning problem. |
| Outcome: | FinLangNet improves KS and bad debt rate by 6.3 pp in real world deployments. |
kNN-ICL: Compositional Task-Oriented Parsing Generalization with Nearest Neighbor In-Context Learning (2024.naacl-long)
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Wenting Zhao, Ye Liu, Yao Wan, Yibo Wang, Qingyang Wu, Zhongfen Deng, Jiangshu Du, Shuaiqi Liu, Yunlong Xu, Philip Yu
| Challenge: | Recent advances in task-oriented parsing involve formulating the task as a sequence-to-sequence problem, relying on a wealth of labeled data. |
| Approach: | They propose a task-oriented parsing framework that integrates nearest-neighbor learning with a nearest-nearest approach. |
| Outcome: | The proposed model can be used to synthesize computer programs based on a natural-language prompt without additional data or specialized prompts. |
Z-LaVI: Zero-Shot Language Solver Fueled by Visual Imagination (2022.emnlp-main)
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| Challenge: | Large-scale pretrained language models suffer from reporting bias, describing the lack of explicit commonsense knowledge in written text. |
| Approach: | They propose to endow language models with visual imagination capabilities by recalling existing images and synthesizing nonexistent images via text-to-image generation. |
| Outcome: | The proposed model improves the performance of existing language models across a diverse set of language tasks. |
PrinciplismQA: A Philosophy-Grounded Approach to Assessing LLM-Human Clinical Medical Ethics Alignment (2026.findings-acl)
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| Challenge: | Existing benchmarks lack systematic approaches to integrate philosophical frameworks and expert validation for ethical reasoning assessment. |
| Approach: | They propose a philosophy-grounded approach to assess medical ethics alignment . PrinciplismQA comprises 3,648 expert-validated questions spanning knowledge assessment and clinical reasoning . |
| Outcome: | PrinciplismQA provides a philosophy-grounded approach to assessing medical ethics alignment. |
BPP-Search: Enhancing Tree of Thought Reasoning for Mathematical Modeling Problem Solving (2025.acl-long)
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Teng Wang, Wing Yin Yu, Zhenqi He, Zehua Liu, HaileiGong HaileiGong, Han Wu, Xiongwei Han, Wei Shi, Ruifeng She, Fangzhou Zhu, Tao Zhong
| Challenge: | Existing datasets in operations research domain lack detailed annotations of the modeling process, focusing only on objective values. |
| Approach: | They propose an annotation-based tree-of-thought tree-based reasoning algorithm that integrates reinforcement learning into a tree- of-though. |
| Outcome: | The proposed algorithm outperforms state-of-the-art methods on StructuredOR, NL4OPT, and MAMO-ComplexLP datasets. |
RAG+: Enhancing Retrieval-Augmented Generation with Application-Aware Reasoning (2025.emnlp-main)
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Yu Wang, Shiwan Zhao, Zhihu Wang, Ming Fan, Xicheng Zhang, Yubo Zhang, Zhengfan Wang, Heyuan Huang, Ting Liu
| Challenge: | Existing RAG paradigms often overlook the cognitive step of applying knowledge, leaving a gap between retrieved facts and task-specific reasoning. |
| Approach: | They introduce a module extension that integrates application-aware reasoning into the RAG pipeline. |
| Outcome: | Experiments show that RAG+ outperforms standard RAG variants and achieves gains of 3–5% in complex scenarios. |
Video-RTS: Rethinking Reinforcement Learning and Test-Time Scaling for Efficient and Enhanced Video Reasoning (2025.emnlp-main)
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| Challenge: | Despite advances in reinforcement learning, data collection and fine-tuning remain costly and hard to scale. |
| Approach: | They propose a video-adaptive test-time scaling strategy that combines RL with a supervised fine-tuning strategy to improve video reasoning capability. |
| Outcome: | The proposed method surpasses existing models by 2.4% in accuracy using only 3.6% training samples. |
DictLLM: Harnessing Key-Value Data Structures with Large Language Models for Enhanced Medical Diagnostics (2024.findings-acl)
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| Challenge: | Structured data processing is a complex and complex process. |
| Approach: | They propose a framework that captures heterogeneity of structured data using large language models . they propose group positional encoding, hierarchical attention bias and optimal transport alignment layer . |
| Outcome: | The proposed framework outperforms baseline methods and few-shot GPT-4 on a medical lab report dataset. |
SEP-MLDC: A Simple and Effective Paradigm for Multi-Label Document Classification (2025.findings-naacl)
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| Challenge: | Existing methods focus on optimizing document features, overlooking the potential of high-quality label features to enhance classification performance. |
| Approach: | They propose a multi-label document classification paradigm that utilizes large language models to expand the label content and generate pseudo-samples for the tail categories. |
| Outcome: | The proposed method significantly outperforms state-of-the-art models. |
Zero-Shot Dialogue State Tracking via Cross-Task Transfer (2021.emnlp-main)
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Zhaojiang Lin, Bing Liu, Andrea Madotto, Seungwhan Moon, Zhenpeng Zhou, Paul Crook, Zhiguang Wang, Zhou Yu, Eunjoon Cho, Rajen Subba, Pascale Fung
| Challenge: | Existing approaches to training a dialogue state tracking model require extensive annotated dialogue data. |
| Approach: | They propose to transfer cross-task knowledge from general question answering corpora to QA model that can handle zero-shot DST. |
| Outcome: | The proposed model improves existing zero-shot and few-shot results on MultiWoz and shows better generalization ability in unseen domains. |
Joint Goal Segmentation and Goal Success Prediction on Multi-Domain Conversations (2022.coling-1)
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| Challenge: | Existing metrics to measure the performance of conversational AI assistants are difficult to establish due to their slow nature. |
| Approach: | They propose an automatic dialogue evaluation framework that performs goal segmentation and success prediction by adding multi-task learning heads. |
| Outcome: | The proposed model achieves on-par with human annotation compared to a gold annotation benchmark. |
Computer Assisted Translation with Neural Quality Estimation and Automatic Post-Editing (2020.findings-emnlp)
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| Challenge: | Using neural machine translation to approximate human parity is difficult due to the lack of parallel training corpora. |
| Approach: | They propose an end-to-end deep learning framework for quality estimation and automatic post-editing of machine translation output. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the English–German dataset and human translators can significantly expedite their post-editing processing with the model. |
Revealing the Attention Floating Mechanism in Masked Diffusion Models (2026.findings-acl)
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Xin Dai, Pengcheng Huang, Zhenghao Liu, Shuo Wang, Yukun Yan, Chaojun Xiao, Yu Gu, Ge Yu, Maosong Sun
| Challenge: | Masked diffusion models (MDMs) leverage bidirectional attention and a denoising process. |
| Approach: | They investigate the attention behaviors of Masked diffusion models by revealing the phenomenon of Attention Floating. |
| Outcome: | The proposed model doubles the performance of autoregressive models in knowledge-intensive tasks. |
Agent Laboratory: Using LLM Agents as Research Assistants (2025.findings-emnlp)
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Samuel Schmidgall, Yusheng Su, Ze Wang, Ximeng Sun, Jialian Wu, Xiaodong Yu, Jiang Liu, Michael Moor, Zicheng Liu, Emad Barsoum
| Challenge: | Agent Laboratory is an autonomous LLM-based framework that can complete the entire research process. |
| Approach: | Agent Laboratory is an autonomous LLM-based framework that can complete the entire research process. |
| Outcome: | Agent Laboratory is an autonomous LLM-based framework that can complete the entire research process. |
An Efficient Framework for Whole-Page Reranking via Single-Modal Supervision (2026.acl-industry)
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Zishuai Zhang, Sihao Yu, null Xiewenyi, Ying Nie, Junfeng Wang, Zhiming Zheng, Dawei Yin, Hainan Zhang
| Challenge: | Existing whole-page reranking methods require large-scale expert annotations to achieve high-quality results. |
| Approach: | They propose a whole-page reranking framework that converts single-modal rankers into page-level guidance by constructing budget-aware candidates for cross-modal annotations and distilling intra-modality preferences to align relevance scales across modalities. |
| Outcome: | The proposed framework reduces annotation costs by 70-90% while outperforming fully-annotated reranking baselines. |
Capturing the Content of a Document through Complex Event Identification (2022.starsem-1)
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| Challenge: | Recent work grouped granular events into more general events, called complex events . however, this approach assumes that a given complex event is always described in consecutive sentences . |
| Approach: | They propose a context-augmented representation learning approach that uses contextual information to model pairwise relation between granular events. |
| Outcome: | The proposed approach outperforms baselines on the complex event identification task. |
Stabilizing Efficient Reasoning with Step-Level Advantage Selection (2026.findings-acl)
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| Challenge: | Large language models generate long and verbose reasoning traces at inference time . short context post-training alone induces substantial reasoning compression . |
| Approach: | They propose a step-level advantage selection approach that reduces reasoning length by over 30% . they propose to use GRPO without any length-aware objective to train models in a shorter context window . |
| Outcome: | The proposed approach reduces average reasoning length by over 30% while improving Pass@1 accuracy by 3.79 points over the strongest length-aware baseline. |
LiGen: Active Lipid Generation via a Molecular Language Model (2026.acl-long)
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| Challenge: | Lipid nanoparticles (LNPs) can deliver cargos to tumor and immune cells . traditional approaches rely on experimental screening and expert judgment . |
| Approach: | They propose a method to generate lipid molecules efficiently and actively using deep learning. |
| Outcome: | The proposed method outperforms baseline methods on multiple cell lines and achieves a 30% improvement over the current methods. |
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. |
Empowering Language Models with Knowledge Graph Reasoning for Open-Domain Question Answering (2022.emnlp-main)
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| Challenge: | Existing Language Models lack the power to store all required knowledge, resulting in a lack of ability to infer out-of-context knowledge. |
| Approach: | They propose a Knowledge Interaction Layer that can be flexibly plugged into existing Transformer-based LMs to interact with a differentiable Knowledge Graph Reasoning module collaboratively. |
| Outcome: | The proposed model can be plugged into existing Transformer-based LMs to interact with a differentiable Knowledge Graph Reasoning module collaboratively. |
Self-Improvement of Non-autoregressive Model via Sequence-Level Distillation (2023.emnlp-main)
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| Challenge: | Existing non-autoregressive Transformers (NAT) models generate the entire sequence in parallel, but the multimodality problem limits their performance. |
| Approach: | They propose a method to generate distilled data by the NAT model itself, eliminating the need for additional teacher networks. |
| Outcome: | The proposed method can generate distilled data by the NAT model without teacher networks and adapt to different NAT models without precise adjustments. |
Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension (2022.acl-long)
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Ying Xu, Dakuo Wang, Mo Yu, Daniel Ritchie, Bingsheng Yao, Tongshuang Wu, Zheng Zhang, Toby Li, Nora Bradford, Branda Sun, Tran Hoang, Yisi Sang, Yufang Hou, Xiaojuan Ma, Diyi Yang, Nanyun Peng, Zhou Yu, Mark Warschauer
| Challenge: | Existing QA datasets rarely distinguish fine-grained reading skills, such as the understanding of varying narrative elements. |
| Approach: | They propose to use FairytaleQA to generate 10,580 questions based on 278 children-friendly stories to assess model's fine-grained learning skills. |
| Outcome: | The proposed dataset consists of 10,580 questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations. |
Low-Resource Generation of Multi-hop Reasoning Questions (2020.acl-main)
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| Challenge: | Existing methods to generate valid and fluent questions from text are limited and insufficient for training. |
| Approach: | They propose to generate multi-hop reasoning questions from the raw text in a low resource circumstance by deducing over multiple relations on several sentences in the text. |
| Outcome: | The proposed model can be applied to the task of machine reading comprehension and achieve significant performance improvements. |
Distilling Large Embeddings via Hyperspherical Householder Quantization (2026.acl-long)
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| Challenge: | Existing methods for quantizing large embeddings rely on Euclidean quantization, which is poorly aligned with the angular geometry induced by contrastive embeddment training. |
| Approach: | They propose a geometry-aware distillation method that compresses large embeddings into short discrete representations via iterative Householder transformations on the unit hypersphere. |
| Outcome: | The proposed method reduces decoding cost and maintains strong semantic retrieval accuracy. |
MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning (2020.acl-main)
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| Challenge: | Existing methods for generating paragraph descriptions for videos require a coherent paragraph and a higher level of coherence. |
| Approach: | They propose a new method that generates a summarized memory state from video segments and sentence history to help better predict the next sentence. |
| Outcome: | The proposed method generates more coherent and less repetitive paragraph captions while maintaining relevance to the input video events. |
Prompt Candidates, then Distill: A Teacher-Student Framework for LLM-driven Data Annotation (2025.acl-long)
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| Challenge: | Existing methods for data annotation use an aggressive approach prompting LLMs to determine a single gold label for each unlabeled sample. |
| Approach: | They propose a teacher-student framework that distills candidate annotations with a Small Language Model (SLM) they propose to use LLMs to generate and distill candidate annotation with slms to ensure unique labels are provided for downstream tasks. |
| Outcome: | The proposed method outperforms existing methods due to uncertainty in LLMs and is noisetolerant. |
Learning Language-guided Adaptive Hyper-modality Representation for Multimodal Sentiment Analysis (2023.emnlp-main)
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| Challenge: | Multimodal Sentiment Analysis (MSA) is effective when using rich information from multiple sources, but the potential sentiment-irrelevant information across modalities may hinder the performance from being further improved. |
| Approach: | They propose an Adaptive Language-guided Multimodal Transformer (ALMT) that learns an irrelevance/conflict-suppressing representation from visual and audio features under guidance of language features at different scales. |
| Outcome: | The proposed model achieves state-of-the-art on several popular datasets and an abundance of ablation shows the effectiveness of the proposed model. |
HeteroCache: A Dynamic Retrieval Approach to Heterogeneous KV Cache Compression for Long-Context LLM Inference (2026.acl-long)
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| Challenge: | Existing static compression methods suffer from coarse-grained caching and high I/O overhead. |
| Approach: | They propose a training-free dynamic compression framework that uses a sparse attention mechanism to categorize attention heads based on stability and similarity. |
| Outcome: | The proposed framework achieves state-of-the-art performance on long-context benchmarks and accelerates decoding by up to 3 compared to the original model with a 224K context. |
Gradient-guided Attention Map Editing: Towards Efficient Contextual Hallucination Mitigation (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) often experience “contextual hallucination” where they prioritize self-generated content over input context, leading to a disregard for pertinent details. |
| Approach: | They propose a method that dynamically adjusts attention maps to enhance contextual relevance by using a trained classifier to identify attention maps likely to induce hallucinations. |
| Outcome: | The proposed approach reduces hallucinations across open-source models on summarization and open-book QA tasks. |
Sparsity-Accelerated Training for Large Language Models (2024.findings-acl)
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| Challenge: | Large language models (LLMs) have demonstrated proficiency across various NLP tasks but often require additional training, such as continual pre-training and supervised fine-tuning. |
| Approach: | They propose to leverage sparsity in pre-trained LLMs to accelerate training by disregarding computations for unimportant neurons. |
| Outcome: | The proposed framework achieves comparable or superior performance to standard training while significantly accelerating the process. |
Generating Long-form Story Using Dynamic Hierarchical Outlining with Memory-Enhancement (2025.naacl-long)
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| Challenge: | Existing methods for long-form story generation rely on rigid outlines or lack macro-level planning, making it difficult to achieve contextual consistency and coherent plot development. |
| Approach: | They propose a Dynamic Hierarchical Outlining with Memory-Enhancement long-form story generation method to generate long-formed story with coherent content and plot. |
| Outcome: | The proposed method significantly improves the fluency, coherence, and overall quality of generated long stories compared to state-of-the-art methods. |
Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space (2020.findings-emnlp)
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| Challenge: | Existing uncertainty sampling methods are time-consuming and can't be executed frequently. |
| Approach: | They propose adversarial uncertainty sampling in discrete space to find informative unlabeled text samples for annotation using adversarials. |
| Outcome: | The proposed approach outperforms baselines on effectiveness on five datasets. |
Reframe Your Life Story: Interactive Narrative Therapist and Innovative Moment Assessment with Large Language Models (2025.emnlp-main)
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Yi Feng, Jiaqi Wang, Wenxuan Zhang, Zhuang Chen, Shen Yutong, Xiyao Xiao, Minlie Huang, Liping Jing, Jian Yu
| Challenge: | Existing approaches to mental health support lack realism and capture therapeutic progression over time. |
| Approach: | They propose a framework that simulates expert narrative therapists by planning therapeutic stages, guiding reflection levels, and generating contextually appropriate responses through retrieval-augmentation. |
| Outcome: | The proposed framework outperforms standard methods in quality and depth on 260 simulated clients and 230 human participants. |
KBAlign: Efficient Self Adaptation on Specific Textual Knowledge Bases (2025.findings-emnlp)
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Zheni Zeng, Yuxuan Chen, Shi Yu, Ruobing Wang, Yukun Yan, Zhenghao Liu, Shuo Wang, Xu Han, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing methods for retrieval-augmented generation (RAG) are limited and fine-tuning incurs prohibitive costs of external signals. |
| Approach: | They propose a self-supervised framework that enhances RAG systems through efficient model adaptation. |
| Outcome: | The proposed framework achieves 90% of the performance gain obtained through GPT-4-supervised adaptation while relying entirely on self-annotation of much smaller models. |
Why Multimodal In-Context Learning Lags Behind? Unveiling the Inner Mechanisms and Bottlenecks (2026.acl-long)
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| Challenge: | In-context learning (ICL) enables models to adapt to new tasks via inference-time demonstrations. |
| Approach: | They propose a simple inference-stage enhancement method that reinforces task mapping transfer. |
| Outcome: | The proposed method strengthens task mapping transfer in multimodal models . it performs comparable to text-only ICL in zero-shot settings but degrades significantly under few-shot demonstrations. |
Contrastive Distillation on Intermediate Representations for Language Model Compression (2020.emnlp-main)
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| Challenge: | Existing methods to compress language models use a simple L_2 loss to distill knowledge in the intermediate representations of a large BERT model to a smaller one. |
| Approach: | They propose a method that uses knowledge distillation to distill knowledge through intermediate layers of the teacher via a contrastive objective. |
| Outcome: | The proposed method outperforms state-of-the-art methods on the GLUE benchmark. |
VocabTailor: Dynamic Vocabulary Selection for Downstream Tasks in Small Language Models (2026.findings-acl)
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| Challenge: | Existing static vocabulary pruning designs that reduce memory usage suffer from rigid, one-size-fits-all designs that cause information loss during the prefill stage and lack flexibility. |
| Approach: | They propose a decoupled dynamic vocabulary selection framework that addresses memory constraints through offloading embedding and implements a hybrid static-dynamic vocabulary selection strategy for LM Head. |
| Outcome: | The proposed framework reduces memory usage by 99% with minimal or no degradation in performance. |
CharPoet: A Chinese Classical Poetry Generation System Based on Token-free LLM (2024.acl-demos)
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| Challenge: | Traditional systems in this field usually accept keywords as user inputs, resulting in limited control over content. |
| Approach: | They propose a Chinese classical poetry generation system based on token-free LLMs that allow unrestricted user instructions to be used. |
| Outcome: | The proposed system outperforms traditional systems including Jiuge and GPT-4 in format accuracy and content quality. |
Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models (2026.acl-long)
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Chao Xue, Yao Wang, Mengqiao Liu, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Chenyao Lu, Lei Jiang, Yu Lu, Haibo Shi, Shuang Liang, Minlong Peng, Flora D. Salim
| Challenge: | Incomplete learning is widespread and heterogeneous in large language models . authors identify five recurrent sources of incomplete learning: missing prerequisite knowledge, conflicts between SFT supervision and pre-training knowledge, internal inconsistencies within SFT data, left-side forgetting during sequential fine-tuning, and insufficient optimization for rare or complex patterns. |
| Approach: | They propose a diagnostic-first framework that maps incomplete learning to causes . they identify five recurrent sources of incomplete learning: missing prerequisite knowledge, conflicts between supervision and pre-training knowledge, internal inconsistencies, left-side forgetting during sequential fine-tuning, and insufficient optimization for rare or complex patterns. |
| Outcome: | The proposed framework maps incomplete learning to causes using observable training and inference signals. |
Empowering GraphRAG with Knowledge Filtering and Integration (2025.emnlp-main)
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| Challenge: | Large language models suffer from knowledge gaps and hallucinations, resulting in incorrect or poor reasoning. |
| Approach: | They propose Graph retrieval-augmented generation (GraphRAG) which integrates structured knowledge from external graphs to enhance model's reasoning. |
| Outcome: | Experiments on knowledge graph QA tasks show that GraphRAG significantly improves reasoning performance across multiple backbone models. |
One-Shot Relational Learning for Knowledge Graphs (D18-1)
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| Challenge: | Existing studies on knowledge graph completion require a large number of positive examples for each relation, but long-tail relations are more common in KGs and those newly added relations do not have many known triples for training. |
| Approach: | They propose a one-shot relational learning framework that utilizes the knowledge distilled by embedding models and learns a matching metric by considering both the learned embeddments and one-hop graph structures. |
| Outcome: | The proposed framework improves on existing embedding models and eliminates the need for retraining when dealing with newly added relations. |
TeCS: A Dataset and Benchmark for Tense Consistency of Machine Translation (2023.acl-short)
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| Challenge: | tense inconsistency is a common problem in machine translation systems. |
| Approach: | They propose a parallel tense test set, containing French-English 552 utterances, and introduce a benchmark, tence prediction accuracy. |
| Outcome: | The proposed model can measure the tense consistency performance of machine translation systems for the first time. |
Accelerating Inference for Pretrained Language Models by Unified Multi-Perspective Early Exiting (2022.coling-1)
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| Challenge: | Existing competitive methods to accelerate inference of pretrained language models are limited by their complexity and computational consumption. |
| Approach: | They propose a unified horizontal and vertical multi-perspective early exiting framework to accelerate inference of transformer-based models. |
| Outcome: | Experiments show that MPEE can achieve higher acceleration inference with competent performance than existing competitive methods. |
ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection (2026.findings-acl)
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Yibo Yan, Shen Wang, Jiahao Huo, Hang Li, Boyan Li, Jiamin Su, Xiong Gao, YiFan Zhang, Tianlong Xu, Zhendong Chu, Aoxiao Zhong, Kun Wang, Hui Xiong, Philip S. Yu, Xuming Hu, Qingsong Wen
| Challenge: | Current mathematical benchmarks focus on evaluating MLLMs’ problem-solving ability, yet there is a crucial gap in addressing more complex scenarios such as error detection. |
| Approach: | They propose to evaluate multimodal error detection by evaluating two sub-tasks error step identification and error categorization. |
| Outcome: | The proposed task evaluates MLLMs' ability to handle multimodal questions compared to text-only models. |
The Moral Integrity Corpus: A Benchmark for Ethical Dialogue Systems (2022.acl-long)
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| Challenge: | Moral integrity corpus captures the moral assumptions of 38k prompt-reply pairs, using 99k distinct Rules of Thumb (RoTs). |
| Approach: | They propose a resource that captures the moral assumptions of 38k prompt-reply pairs, using 99k distinct Rules of Thumb (RoTs). |
| Outcome: | The proposed resource captures the moral assumptions of 38k prompt-reply pairs, using 99k distinct Rules of Thumb (RoTs). |
NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset (2021.findings-emnlp)
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| Challenge: | Existing question answering datasets lack numerical reasoning and reasoning processes . current research on numerical reasoning focuses on simple calculations . |
| Approach: | They propose a conversational and bilingual question answering dataset with numerical reasoning with compound mathematical expressions. |
| Outcome: | The proposed model achieves 55.5 exact match scores while human performance is 89.7. |
Integrating Semantics and Neighborhood Information with Graph-Driven Generative Models for Document Retrieval (2021.acl-long)
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Zijing Ou, Qinliang Su, Jianxing Yu, Bang Liu, Jingwen Wang, Ruihui Zhao, Changyou Chen, Yefeng Zheng
| Challenge: | Existing methods for document hashing combine only one of semantics and neighborhood information, lacking a theoretical principle to guide the integration process. |
| Approach: | They propose to encode neighborhood information with a graph-induced Gaussian distribution and integrate it with generative models. |
| Outcome: | The proposed model can be trained as efficiently as state-of-the-art methods on benchmark datasets. |
HiGen: Hierarchy-Aware Sequence Generation for Hierarchical Text Classification (2024.eacl-long)
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| Challenge: | Hierarchical text classification is a complex subtask under multi-label text classification . the relevance of document sections can vary based on the hierarchy level, necessitating a dynamic document representation. |
| Approach: | They propose a text-generation-based framework that uses language models to encode dynamic text representations. |
| Outcome: | The proposed framework surpasses existing methods while handling data and mitigating class imbalance. |
KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generation (2020.emnlp-main)
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| Challenge: | Existing methods for data-to-text generation rely on labeled data, which is costly to acquire and limits their application to new tasks and domains. |
| Approach: | They propose to leverage pre-training and transfer learning to address this problem by leveraging a general knowledge-grounded generation model and a knowledge-based model. |
| Outcome: | The proposed model can generate knowledge-enriched text on a knowledge-grounded text corpus crawled from the web in three settings. |
AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading (2026.findings-acl)
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| Challenge: | Existing Large Language Models (LLMs) lack the end-to-end optimization needed to learn a coherent strategy from market feedback. |
| Approach: | They propose a single-agent framework that uses reinforcement learning to learn a dynamic policy over a transparent decision workflow. |
| Outcome: | The proposed framework achieves state-of-the-art performance on key financial metrics. |
Towards Database-Free Text-to-SQL Evaluation: A Graph-Based Metric for Functional Correctness (2025.coling-main)
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Yi Zhan, Longjie Cui, Han Weng, Guifeng Wang, Yu Tian, Boyi Liu, Yingxiang Yang, Xiaoming Yin, Jiajun Xie, Yang Sun
| Challenge: | Existing metrics for evaluating functional correctness of SQL queries are prone to false positives due to inadequately prepared test databases. |
| Approach: | They propose a graph-based metric that uses a relational operator tree to extract rich semantic information from the logical execution plan of SQL queries and embed it into a diagram. |
| Outcome: | The proposed method eliminates the need for extensive test database preparation and performs graph matching on unseen SQL queries. |
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. |
Ameli: Enhancing Multimodal Entity Linking with Fine-Grained Attributes (2024.eacl-long)
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| Challenge: | Experimental results show that understanding attributes of mentions from text descriptions and visual images plays a vital role in multimodal entity linking. |
| Approach: | They propose to integrate attributes into multimodal entity linking using a text-image-based knowledge base. |
| Outcome: | The proposed approach integrates attributes into disambiguation. |
Fake Alignment: Are LLMs Really Aligned Well? (2024.naacl-long)
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Yixu Wang, Yan Teng, Kexin Huang, Chengqi Lyu, Songyang Zhang, Wenwei Zhang, Xingjun Ma, Yu-Gang Jiang, Yu Qiao, Yingchun Wang
| Challenge: | Existing studies on large language models have shown that they are poorly aligned in practice. |
| Approach: | They propose a framework to evaluate safety in large language models . they propose two new metrics to quantify fake alignment and obtain corrected performance estimation. |
| Outcome: | The proposed framework and two metrics show that some models with purported safety are poorly aligned in practice. |
ClinicalMC: A Benchmark for Multi-Course Clinical Decision-Making with Large Language Models (2026.findings-acl)
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| Challenge: | Existing benchmarks assess LLM performance in single-course settings and lack systematic evaluation in multi-course scenarios, where a patient’s condition evolves over time. |
| Approach: | They propose to use large language models to assess their performance in multi-course clinical decision-making scenarios where a patient’s condition evolves over time. |
| Outcome: | The proposed model includes 1,275 Chinese and 5,804 English samples across four stages from admission to discharge. |
AlgBench: To What Extent Do Large Reasoning Models Understand Algorithms? (2026.findings-acl)
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| Challenge: | Existing benchmarks for algorithmic reasoning fail to answer a critical question: do LRMs master algorithmic thinking? Empirical evaluations on leading LRM models reveal substantial performance heterogeneity, while models perform well on non-optimized tasks, accuracy drops sharply to around 49% on globally optimized algorithms. |
| Approach: | They propose an algorithm-centric benchmark that evaluates large reasoning models under an algorithmic paradigm. |
| Outcome: | Empirical evaluations on leading LRMs reveal substantial performance heterogeneity . models perform well on non-optimized tasks, accuracy drops sharply to around 49% . |
DuReadervis: A Chinese Dataset for Open-domain Document Visual Question Answering (2022.findings-acl)
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| Challenge: | Open-domain question answering is a task that requires answering questions based on a collection of document images. |
| Approach: | They propose to use document images to answer questions using layouts and visual features instead of text. |
| Outcome: | The proposed approach reduces human cost and improves scalability of QA systems by incorporating layouts and visual features. |
Benchmarking and Learning Real-World Customer Service Dialogue (2026.acl-long)
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| Challenge: | Existing benchmarks and training pipelines for industrial intelligent customer service (ICS) focus on task completion and tool correctness. |
| Approach: | They propose a benchmark-to-optimization loop that bridges offline gains to deployment . they propose OlaMind, which distills reusable reasoning patterns from expert dialogues . |
| Outcome: | The proposed benchmark surpasses GPT-5.2 and Gemini 3 Pro on OlaBench . it delivers an average +23.67% issue resolution and -6.6% human transfer rate versus baseline . |
Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees (2021.eacl-main)
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| Challenge: | Pre-trained language models like BERT achieve superior performances in various NLP tasks without explicit consideration of syntactic information. |
| Approach: | They propose a plug-and-play framework that incorporates syntax trees into pre-trained Transformers. |
| Outcome: | The proposed framework improves on pre-trained models on natural language understanding datasets and shows that it can be used to train pre-structured neural networks. |
Multimodal Clickbait Detection by De-confounding Biases Using Causal Representation Inference (2024.emnlp-main)
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| Challenge: | a new method to detect clickbait posts on the Web is needed to detect such posts. |
| Approach: | They propose a method to detect clickbait posts on the Web using latent factors . they use features in multiple modalities to characterize the posts and causal inference to eliminate noise . |
| Outcome: | The proposed method can detect clickbait posts on popular social media platforms with good generalization ability. |
Response-G1: Explicit Scene Graph Modeling for Proactive Streaming Video Understanding (2026.acl-long)
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Ke Ma, Jiaqi Tang, Bin Guo, Xueting Han, Ruonan Xu, Qingfeng He, Ziheng Wang, Xu Wang, Qifeng Chen, Zhiwen Yu, Yunhao Liu
| Challenge: | Existing methods for streaming video understanding are query-agnostic and implicitly model video evidence. |
| Approach: | They propose a framework that establishes explicit, structured alignment between the accumulated video evidence and the query’s expected response conditions via scene graphs. |
| Outcome: | The proposed model achieves more interpretable and accurate response timing decisions on both proactive and reactive tasks. |
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 . |
From Scores to Steps: Diagnosing and Improving LLM Performance in Evidence-Based Medical Calculations (2025.emnlp-main)
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Benlu Wang, Iris Xia, Yifan Zhang, Junda Wang, null Feiyun Ouyang, Shuo Han, Arman Cohan, Hong Yu, Zonghai Yao
| Challenge: | Existing benchmarks assess only the final answer with a wide numerical tolerance, overlooking systematic reasoning failures and potentially causing serious clinical misjudgments. |
| Approach: | They propose a new step-by-step evaluation pipeline that assesses formula selection, entity extraction, and arithmetic computation. |
| Outcome: | The proposed method improves the accuracy of large language models on medical benchmarks from 16.35% to 53.19%. |
AgentAsk: Multi-Agent Systems Need to Ask (2026.acl-long)
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Bohan Lin, Kuo Yang, Zelin Tan, Yingchuan Lai, Chen Zhang, Guibin Zhang, Xinlei Yu, Miao Yu, Xu Wang, Yudong Zhang, Yang Wang
| Challenge: | Multi-agent systems fail to consistently outperform strong single-a agent baselines due to error propagation at inter-aggent message handoffs. |
| Approach: | They propose an edge-level error taxonomy that identifies four main errors in multi-agent interactions as data gaps, signal corruption, referential drift and capacity gaps as primary sources of failure. |
| Outcome: | The proposed module outperforms existing systems on five benchmarks and is architecture-agnostic. |
Taxonomy-Guided Zero-Shot Recommendations with LLMs (2025.coling-main)
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| Challenge: | Existing approaches to deploy large language models (LLMs) into RecSys have limited prompt length, unstructured item information, and un-constrained generation of recommendations. |
| Approach: | They propose a taxonomy-guided recommendation framework that empowers LLMs with category information in a systematic approach. |
| Outcome: | The proposed framework significantly improves recommendation quality compared to zero-shot approaches. |
Label-Aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition (N18-1)
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Zhenghui Wang, Yanru Qu, Liheng Chen, Jian Shen, Weinan Zhang, Shaodian Zhang, Yimei Gao, Gen Gu, Ken Chen, Yong Yu
| Challenge: | NER is a fundamental problem for medical text mining because of the difference of specialties and cost of human annotation. |
| Approach: | They propose a label-aware double transfer learning framework for medical NER from electronic medical records. |
| Outcome: | The proposed framework improves accuracy over strong baselines on 12 cross-specialty NER tasks. |
Reasoning-Enhanced Domain-Adaptive Pretraining of Multimodal Large Language Models for Short Video Content Governance (2025.emnlp-industry)
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Zixuan Wang, Yu Sun, Hongwei Wang, Baoyu Jing, Xiang Shen, Xin Dong, Zhuolin Hao, Hongyu Xiong, Yang Song
| Challenge: | Existing approaches to identifying inappropriate content require extensive human-labeled data and lack cross-issue generalization. |
| Approach: | They propose a reasoning-enhanced multimodal large language model (MLLM) pretraining paradigm for unified inappropriate content detection. |
| Outcome: | The proposed model improves the MLLM's performance in both zero-shot and supervised fine-tuning settings and shows strong generalization capabilities to emergent, previously unseen issues. |
S2O: Early Stopping for Sparse Attention via Online Permutation (2026.acl-long)
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| Challenge: | Existing block-granularity sparsification can reduce latency, but coarse blocks impose an intrinsic sparsity ceiling. |
| Approach: | They propose a method that performs early stopping for sparse attention via online permutation. |
| Outcome: | The proposed approach reduces the complexity of the model and its performance. |
Language Scaling for Universal Suggested Replies Model (2021.naacl-industry)
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Qianlan Ying, Payal Bajaj, Budhaditya Deb, Yu Yang, Wei Wang, Bojia Lin, Milad Shokouhi, Xia Song, Yang Yang, Daxin Jiang
| Challenge: | We consider scaling automated suggested replies (SR) to multiple languages for a commercial email application. |
| Approach: | They propose a multi-lingual multi-task continual learning framework with auxiliary tasks and language adapters to train universal language representation across regions. |
| Outcome: | The proposed model reduces catastrophic forgetting and improves cross-lingual transfer across languages while reducing training costs. |
AdaDHP: Fine-Grained Fine-Tuning via Dual Hadamard Product and Adaptive Parameter Selection (2025.acl-long)
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| Challenge: | Increasing number of parameters can be challenging under resource-constrained environments. |
| Approach: | They propose a parameter-efficient fine-tuning method with fewer parameters and finer granularity that can adaptively select important parameters for each task. |
| Outcome: | The proposed method can fine-tune important parameters for each task, while maintaining the same weights. |
DICE: Structured Reasoning in LLMs through SLM-Guided Chain-of-Thought Correction (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) often prioritize reasoning over adherence to detailed instructions due to high computational costs and limited parameter access. |
| Approach: | They propose a lightweight framework that guides small language models to refine LLMs’ outputs through chain-of-thought correction. |
| Outcome: | The proposed framework improves the average format accuracy and content correctness of LLM outputs by 35.4% and 29.4%, respectively, achieving state-of-the-art (SOTA) performance over other competitive baselines. |
Generative Music Models’ Alignment with Professional and Amateur Users’ Expectations (2025.findings-acl)
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| Challenge: | Recent years have witnessed rapid advances in text-to-music generation using large language models. |
| Approach: | They propose a task to align AI-generated music with human expressions . they use a dataset of over 1.5 million songs to analyze their content . |
| Outcome: | The proposed framework outperforms baseline models and facilitates end-to-end generation of songs audio. |
MultiFileTest: A Multi-File-Level LLM Unit Test Generation Benchmark and Impact of Error Fixing Mechanisms (2026.findings-acl)
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Yibo Wang, Congying Xia, Wenting Zhao, Jiangshu Du, Chunyu Miao, Zhongfen Deng, Philip S. Yu, Chen Xing
| Challenge: | Existing evaluation benchmarks for LLM unit test generation focus on function-level code rather than on more practical, challenging multi-file codebases. |
| Approach: | They propose a multi-file-level benchmark for unit test generation covering Python, Java, and JavaScript. |
| Outcome: | The proposed benchmarks show that most LLMs exhibit moderate performance on MultiFileTest, highlighting the benchmark’s inherent difficulty. |
Experience is the Teacher: Reusing Atomic Thoughts from LLMs to Improve Medical Dialogue (2026.findings-acl)
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Guangya Yu, Hui Luo, Qi Ye, Ruihui Hou, Weiyan Zhang, Mingxi Shang, Xuanwu Li, ChunMing Wang, Tong Ruan
| Challenge: | Recent large reasoning models (LLMs) lack dynamic and diverse thinking capabilities . reusing atomic thoughts provides a practical pathway toward dynamic reasoning . |
| Approach: | They propose a framework that extracts atomic thoughts from teacher models and reuses them to guide reasoning and generate responses. |
| Outcome: | The proposed framework extracts atomic thoughts from teacher models and reuses them to guide reasoning and generate responses. |
Detoxification for LLM: From Dataset Itself (2026.acl-long)
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| Challenge: | Existing methods for large language models focus on post-training stage or inference time, while few tackle the source of toxicity, namely, the dataset itself. |
| Approach: | They propose to localize and rewrite toxic spans in raw corpora with SoCD, which guides an LLM to localized and preserving semantics while preserving toxicity. |
| Outcome: | The proposed method reduces TP from 0.42 to 0.18 and Expected Maximum Toxicity (EMT) from 0.43 to 0.20 on three LLMs. |
Examining False Positives under Inference Scaling for Mathematical Reasoning (2025.emnlp-main)
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| Challenge: | Recent advances in language models have led to significant improvements in mathematical reasoning across benchmarks. |
| Approach: | They analyze the prevalence of false positives in language models by using heuristic evaluation methods . they find that false positive models produce correct final answers but with flawed deduction paths . |
| Outcome: | The proposed model performance improvements are based on the proposed model and its evaluation metrics. |
FLAG-TRADER: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading (2025.findings-acl)
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Guojun Xiong, Zhiyang Deng, Keyi Wang, Yupeng Cao, Haohang Li, Yangyang Yu, Xueqing Peng, Mingquan Lin, Kaleb E Smith, Xiao-Yang Liu, Jimin Huang, Sophia Ananiadou, Qianqian Xie
| Challenge: | Large language models (LLMs) have impressive reasoning capabilities in financial tasks, but struggle with multi-step, goal-oriented scenarios in interactive financial markets. |
| Approach: | They propose a framework that integrates large language models with gradient-driven reinforcement learning (RL) policy optimization. |
| Outcome: | The proposed framework improves performance in trading and other financial domain tasks. |
Controlling Bias Exposure for Fair Interpretable Predictions (2022.findings-emnlp)
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| Challenge: | Existing approaches to reduce bias in NLP tasks focus on protecting or isolating information related to a sensitive attribute, but they lack control over how much bias is required to be removed. |
| Approach: | They propose a favorable debiasing method that uses sensitive information ‘fairly’, rather than blindly eliminating it. |
| Outcome: | The proposed method achieves a trade-off between debiasing and task performance along with producing debiased rationales as evidence. |
NOVA: An Iterative Planning Framework for Enhancing Scientific Innovation with Large Language Models (2025.findings-acl)
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Xiang Hu, Hongyu Fu, Jinge Wang, Yifeng Wang, Zhikun Li, Renjun Xu, Yu Lu, Yaochu Jin, Lili Pan, Zhenzhong Lan
| Challenge: | Existing approaches to generate research ideas rely on retrieval or prompt engineering to generate ideas. |
| Approach: | They propose a method that uses iterative planning and search to boost creative potential of LLMs by integrating external knowledge with broader and deeper insights. |
| Outcome: | The proposed method outperforms the current state-of-the-art in generating 2.5 times more top-rated ideas based on 170 seed papers in a Swiss Tournament evaluation. |
SPIDE: Serial and Parallel Intertwined Speculative Decoding (2026.findings-acl)
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Wenru Xu, Peixuan Xu, Ziqi Yang, Ming Hu, Zihui Wang, Jianzhong Qi, Rongshan Yu, Xiaoliang Fan, Cheng Wang
| Challenge: | Speculative decoding (SD) is a training-free SD framework that orchestrates dynamic alternation combining serial dynamic drafting with parallel draft verification. |
| Approach: | They propose a serial and parallel intertwined speculative DEcoding framework that orchestrates dynamic alternation combining serial dynamic drafting and parallel draft verification. |
| Outcome: | The proposed framework accelerates inference while reducing the LLM usage costs. |
Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding (2022.findings-acl)
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| Challenge: | Existing approaches to event extraction are limited to a set of pre-defined types. |
| Approach: | They propose a natural language query framework that uses event types and argument roles to extract candidate triggers and arguments from input text. |
| Outcome: | The proposed framework outperforms existing methods on zero-shot event extraction. |
Alignment for Efficient Tool Calling of Large Language Models (2025.emnlp-main)
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| Challenge: | Recent advances in tool learning have enabled large language models to integrate external tools, enhancing their task performance by expanding their knowledge boundaries. |
| Approach: | They propose a framework that combines probabilistic knowledge boundary estimation with dynamic decision-making to allow LLMs to better assess when to invoke tools based on their confidence. |
| Outcome: | The proposed framework shows significant improvements in tool efficiency by reducing unnecessary tool usage. |
SDA: Semantic Discrepancy Alignment for Text-conditioned Image Retrieval (2024.findings-acl)
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| Challenge: | Existing methods for textconditioned image retrieval are limited due to the scale of training and the complexity of attributes in modification texts. |
| Approach: | They propose a general boosting framework to address these issues by employing semantic discrepancy alignment by formulating distance consistency and neighbor consistency between the image and text domains. |
| Outcome: | The proposed framework improves retrieval performance on three prominent datasets with state-of-the-art results. |
TCSinger: Zero-Shot Singing Voice Synthesis with Style Transfer and Multi-Level Style Control (2024.emnlp-main)
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| Challenge: | Existing models fail to generate singing voices rich in stylistic nuances for unseen singers due to multifaceted nature of singing styles. |
| Approach: | They propose a zero-shot SVS model for style transfer across cross-lingual speech and singing styles and multi-level style control. |
| Outcome: | Experimental results show that TCSinger outperforms baseline models in synthesis quality, singer similarity, and style controllability. |
MedEval: A Multi-Level, Multi-Task, and Multi-Domain Medical Benchmark for Language Model Evaluation (2023.emnlp-main)
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| Challenge: | Existing medical datasets require high quality domain-specific datasets. |
| Approach: | They propose a multi-level, multi-task, and multi-domain medical benchmark to facilitate the development of language models for healthcare. |
| Outcome: | The proposed model provides granular potential usage and supports a wide range of tasks. |
SEE: Continual Fine-tuning with Sequential Ensemble of Experts (2025.findings-acl)
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| Challenge: | Continual fine-tuning of large language models suffers from catastrophic forgetting . some approaches use routers to assign tasks to experts, but continual learning often requires retraining . |
| Approach: | They propose a framework that integrates routing and response mechanisms within each expert . it eliminates the need for an additional router and allows each expert to decide whether a query should be handled . |
| Outcome: | The proposed framework outperforms previous approaches in continual fine-tuning . it can handle learning tasks and out-of-distribution instances, paving the way for distributed model ensembling. |
FactVerse: A Benchmark for Factual Consistency in Interleaved Image–Text Generation (2026.acl-long)
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Yubo Shan, Kun Zhang, Qiming Xu, Liping Cao, Yingying Cao, Jian Zhang, Yu Wang, Jingyuan Li, Yuanzhuo Wang
| Challenge: | Existing benchmarks lack effective mechanisms to evaluate factual consistency in interleaved image-text generation. |
| Approach: | They propose a benchmark dedicated to evaluating factual consistency in interleaved image-text generation. |
| Outcome: | The proposed framework outperforms existing evaluation methods in evaluating factual consistency in interleaved image-text generation. |
Can Language Models Follow Multiple Turns of Entangled Instructions? (2025.findings-emnlp)
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Chi Han, Xin Liu, Haodong Wang, Shiyang Li, Jingfeng Yang, Haoming Jiang, Zhengyang Wang, Qingyu Yin, Liang Qiu, Changlong Yu, Yifan Gao, Zheng Li, Bing Yin, Jingbo Shang, Heng Ji
| Challenge: | Despite of significant achievements in improving instruction-following capabilities of large language models, the ability to process multiple potentially entangled or conflicting instructions remains a considerable challenge. |
| Approach: | They construct multi-turn instruction with 1.1K high-quality multi-turned conversations using the human-in-the-loop approach and examine their capabilities. |
| Outcome: | The proposed model shows that it is difficult to integrate multiple turns and balance competing objectives when instructions intersect or conflict. |
Knowledge-Enriched Natural Language Generation (2021.emnlp-tutorials)
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| Challenge: | Knowledge-enriched text generation poses unique challenges in modeling and learning . a roadmap will outline the state-of-the-art methods to tackle these challenges . |
| Approach: | They propose a roadmap to tackle the challenges of knowledge-enriched text generation . they will dive deep into various technical components to illustrate how to represent knowledge . |
| Outcome: | This tutorial outlines the state-of-the-art methods to tackle the problem . it aims to show how to represent knowledge, feed knowledge into a generation model, evaluate results . |
Beyond Single View: A Comprehensive Benchmark for Medical Multimodal Large Language Models on Multi-Image Understanding (2026.acl-long)
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| Challenge: | Existing benchmarks for multimodal large language models are limited to multiview diagnostics. |
| Approach: | They propose a benchmark specifically designed for medical multi-image understanding that evaluates MLLMs across four dimensions. |
| Outcome: | The proposed model performs better in multi-image contexts than open-source models . the model perform better when processing increased visual loads than closed-source ones . |
STAD: Self-Training with Ambiguous Data for Low-Resource Relation Extraction (2022.coling-1)
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| Challenge: | Existing approaches for low-resource relation extraction use only confident instances and uncertain instances. |
| Approach: | They propose a self-training approach for low-resource relation extraction using auto-annotated instances. |
| Outcome: | The proposed method improves on two widely used datasets with low-resource settings. |
AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving (2025.findings-emnlp)
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Kangan Qian, Sicong Jiang, Yang Zhong, Ziang Luo, Zilin Huang, Tianze Zhu, Kun Jiang, Mengmeng Yang, Zheng Fu, Jinyu Miao, Yining Shi, He Zhe Lim, Li Liu, Tianbao Zhou, Hongyi Wang, Huang Yu, Yifei Hu, Guang Li, Guang Chen, Hao Ye, Lijun Sun, Diange Yang
| Challenge: | Vision-Language Models struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate perception and robust step-by-step reasoning. |
| Approach: | AgentThink integrates Chain-of-Thought reasoning with dynamic, agent-style tool invocation for autonomous driving tasks. |
| Outcome: | Experiments on the DriveLMM-o1 benchmark show AgentThink significantly boosts overall reasoning scores by 53.91% and enhances answer accuracy by 33.54% . |
RBPtool: A Deep Language Model Framework for Multi-Resolution RBP-RNA Binding Prediction and RNA Molecule Design (2025.emnlp-main)
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Jiyue Jiang, Yitao Xu, Zikang Wang, Yihan Ye, Yanruisheng Shao, Yuheng Shan, Jiuming Wang, Xiaodan Fan, Jiao Yuan, Yu Li
| Challenge: | RNA-binding proteins play key roles in post-transcriptional gene regulation . existing methods focus on shallow sequence features or coarse structural representations . large language models allow for precise modeling and biologically informed de novo RNA design . |
| Approach: | They extend RPI15223 into a multi-resolution, structure-level RBP-RNA dataset and introduce RBPtool, a framework that fuses sequence and structural information. |
| Outcome: | The proposed framework achieves state-of-the-art performance on public benchmarks and the RPI15223 dataset while supporting fine-grained level predictions. |
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. |
TROVE: A Challenge for Fine-Grained Text Provenance via Source Sentence Tracing and Relationship Classification (2025.acl-long)
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| Challenge: | Large language models have demonstrated great potential in natural language generation, but their widespread adoption has raised concerns regarding content reliability and accountability. |
| Approach: | They propose a challenge to trace each sentence of a target text back to specific source sentences within potentially lengthy or multi-document inputs. |
| Outcome: | The proposed challenge traces each sentence of a target text back to specific source sentences . the dataset includes 11 scenarios covering QA and summarization in english and Chinese . |
AIPO: Adaptive Information Guided Token-Level Reinforcement Learning for Large Language Model Reasoning (2026.acl-long)
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| Challenge: | Existing RLVR methods focus on all generated tokens rather than on which tokens contribute to reasoning. |
| Approach: | They propose to use a Random–Fourier approximation of the Hilbert–Schmidt Independence Criterion to focus updates on decisive tokens discovered on the fly to improve the efficiency of mutual-information estimation. |
| Outcome: | The proposed approach yields +20% accuracy over strong RLVR baselines while updating merely 10% of tokens, demonstrating superior efficiency and effectiveness. |
Few-Shot Dialogue Summarization via Skeleton-Assisted Prompt Transfer in Prompt Tuning (2024.eacl-long)
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Kaige Xie, Tong Yu, Haoliang Wang, Junda Wu, Handong Zhao, Ruiyi Zhang, Kanak Mahadik, Ani Nenkova, Mark Riedl
| Challenge: | Existing prompt transfer techniques lack consideration for dialogue-specific information. |
| Approach: | They propose a method which leverages skeleton generation as extra supervision that functions as a medium connecting the distinct source and target task. |
| Outcome: | The proposed method significantly outperforms baselines on two dialogue summarization benchmarks. |
RA2FD: Distilling Faithfulness into Efficient Dialogue Systems (2024.emnlp-main)
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| Challenge: | Retrieval Augmented Generation (RAG) is effective but inference inefficient, while Retrieral Free Generations (RFG) are more efficient but sacrifice faithfulness. |
| Approach: | They propose a retrieval-free model training scheme that uses a teacher-student framework to distill the faithfulness capacity of a student's knowledge-infused responses. |
| Outcome: | The proposed model surpasses the previous SOTA RFG model on knowledge-grounded dialogue datasets by an average of 33% while improving inference efficiency. |
SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task (D18-1)
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| Challenge: | Existing studies in text-to-SQL do not require generating complex SQL queries with multiple clauses or sub-queries. |
| Approach: | They propose a syntax tree network to address the complex text-to-SQL generation task. |
| Outcome: | The proposed model outperforms the current state-of-the-art model by 9.5% on a large text-to-SQL corpus. |
Learning Invariant Representation Improves Robustness for MRC Models (2022.findings-emnlp)
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| Challenge: | Existing approaches to improve machine reading comprehension models are vulnerable and not robust to adversarial examples. |
| Approach: | They propose to construct positive example pairs which have same answer by augmentation and then introduce stability and contrastive loss to improve invariance of representation. |
| Outcome: | The proposed approach boosts the robustness of QA models across different tasks and attack sets significantly and consistently. |
Mulan: A Multi-Level Alignment Model for Video Question Answering (2023.findings-emnlp)
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| Challenge: | Existing methods focus on visual-language alignment at the video level, but they do not account for fine-grained semantic interaction between video and text. |
| Approach: | They propose a multi-level Alignment Model for Video Question Answering that establishes alignment between visual and textual modalities at the object-level, frame-level and video-level. |
| Outcome: | The proposed model outperforms state-of-the-art methods even with a small amount of extra visual-language pre-training data and a reduced number of trainable parameters. |
Investigating Dynamic Routing in Tree-Structured LSTM for Sentiment Analysis (D19-1)
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| Challenge: | Existing deep neural network models such as LSTM and tree-LSTM have a bias problem where the words in the tail of a sentence are more heavily emphasized than those in the header. |
| Approach: | They propose a capsule tree-LSTM model that uses dynamic routing to build sentence representations by assigning different weights to nodes according to their contributions to prediction. |
| Outcome: | The proposed model improves on the Stanford Sentiment Treebank and EmoBank datasets. |
A Lifelong Multilingual Multi-granularity Semantic Alignment Approach via Maximum Co-occurrence Probability (2024.lrec-main)
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| Challenge: | Existing methods to mask and predict tokens in multilingual text limit multilingual interaction . |
| Approach: | They propose a lifelong multilingual multi-granularity semantic alignment approach which continuously extracts massive aligned linguistic units from noisy data via a maximum co-occurrence probability algorithm. |
| Outcome: | The proposed approach improves translation performance on WMT14 18 benchmarks in twelve directions. |
Wasserstein Distance Regularized Sequence Representation for Text Matching in Asymmetrical Domains (2020.emnlp-main)
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| Challenge: | Asymmetrical text matching is a fundamental problem in information retrieval and natural language processing. |
| Approach: | They propose a method that regularizes features vectors projected from different domains . WD-Match can be used to improve different text matching methods . |
| Outcome: | The proposed method outperforms existing methods and benchmarks on four datasets. |
LLM Agents for Education: Advances and Applications (2025.findings-emnlp)
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Zhendong Chu, Shen Wang, Jian Xie, Tinghui Zhu, Yibo Yan, Jingheng Ye, Aoxiao Zhong, Xuming Hu, Jing Liang, Philip S. Yu, Qingsong Wen
| Challenge: | Large Language Model (LLM) agents are transforming education by automating complex tasks and enhancing both teaching and learning processes. |
| Approach: | This survey analyzes recent advances in applying Large Language Model agents to educational settings . it highlights ethical issues, hallucination and overreliance, and integration with existing ecosystems . |
| Outcome: | The authors analyze the technologies enabling LLM agents and highlight key challenges in deploying them in educational settings. |
DSVD: Dynamic Self-Verify Decoding for Faithful Generation in Large Language Models (2025.emnlp-main)
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| Challenge: | Existing approaches to reliability of large language models often lack self-correction or use costly post-hoc verification. |
| Approach: | They propose a decoding framework that enhances generation reliability through real-time hallucination detection and efficient error correction. |
| Outcome: | Extensive experiments across five benchmarks show the proposed framework improves truthfulness and factual accuracy. |
The ART of LLM Refinement: Ask, Refine, and Trust (2024.naacl-long)
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Kumar Shridhar, Koustuv Sinha, Andrew Cohen, Tianlu Wang, Ping Yu, Ramakanth Pasunuru, Mrinmaya Sachan, Jason Weston, Asli Celikyilmaz
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable generative abilities, but can they judge the quality of their own generations and self-improve? |
| Approach: | They propose a reasoning with a refinement strategy called *ART: Ask, Refine, and Trust* that asks necessary questions to decide when an LLM should refine its output and uses it to affirm or deny trust. |
| Outcome: | The proposed reasoning with a refinement strategy achieves a performance gain of +5 points over baselines on two multistep reasoning tasks. |
Teaching According to Talents! Instruction Tuning LLMs with Competence-Aware Curriculum Learning (2025.findings-emnlp)
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Yangning Li, Tingwei Lu, Yinghui Li, Yankai Chen, Wei-Chieh Huang, Wenhao Jiang, Hui Wang, Hai-Tao Zheng, Philip S. Yu
| Challenge: | Efficient instruction tuning aims to enhance the ultimate performance of large language models (LLMs) current methods suffer from the curriculum rigidity, resulting in a fixed and potentially sub-optimal learning trajectory. |
| Approach: | a framework for efficient instruction tuning is proposed to address the issue of curriculum rigidity . current methods rely on static heuristic difficulty metrics and fail to adapt to evolving capabilities . |
| Outcome: | Efficient instruction tuning aims to enhance the ultimate performance of large language models . current methods suffer from the curriculum rigidity, resulting in a fixed learning trajectory . |
Sentence Embedding Alignment for Lifelong Relation Extraction (N19-1)
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| Challenge: | Existing approaches to relation extraction require a fixed set of relations . Existing methods assume a closed set of relationships and perform once-and-for-all training on a set of datasets. |
| Approach: | They propose to improve the stochastic gradient methods with a replay memory to alleviate the forgetting problem by anchoring the sentence embedding space. |
| Outcome: | The proposed method outperforms state-of-the-art methods on multiple benchmarks. |
DREAM: Improving Video-Text Retrieval Through Relevance-Based Augmentation Using Large Foundation Models (2025.naacl-long)
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| Challenge: | Recent advances in video-text retrieval models have limited training data annotations. |
| Approach: | They propose a Video-Text Retrieval Paradigm with Relevance-based Augmentation which enhances video and text data using large foundation models to learn more generalized features. |
| Outcome: | The proposed method improves video-text retrieval performance over existing methods. |
Outcome Accuracy is Not Enough: Aligning the Reasoning Process of Reward Models (2026.acl-long)
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Binghai Wang, Yantao Liu, Yuxuan Liu, Tianyi Tang, Shenzhi Wang, Chang Gao, Chujie Zheng, Yichang Zhang, Le Yu, Shixuan Liu, Tao Gui, Qi Zhang, Xuanjing Huang, Bowen Yu, Fei Huang, Junyang Lin
| Challenge: | Recent studies observe a phenomenon where reward models achieve high accuracy on static datasets but fail to generalize effectively during RLHF. |
| Approach: | They propose a method that combines rationale consistency with outcome accuracy to improve performance on RM-Bench and JudgeBench. |
| Outcome: | The proposed method surpasses baselines on RM-Bench and JudgeBench by an average of 5% and improves creative writing tasks by 7%. |
G-Safeguard: A Topology-Guided Security Lens and Treatment on LLM-based Multi-agent Systems (2025.acl-long)
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| Challenge: | Large Language Model (LLM)-based Multi-agent Systems (MAS) have demonstrated remarkable capabilities in various complex tasks, but their vulnerability to adversarial attacks, misinformation propagation, and unintended behaviors have raised significant concerns. |
| Approach: | They propose a topology-guided security lens and treatment for robust LLM-MAS that leverages graph neural networks to detect anomalies on the multi-agent utterance graph and employ topological intervention for attack remediation. |
| Outcome: | Experiments show that the proposed security lens recovers 40% of the performance under various attack strategies and integrates with mainstream MAS with security guarantees. |
Marco-Bench-MIF: On Multilingual Instruction-Following Capability of Large Language (2025.acl-long)
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Bo Zeng, Chenyang Lyu, Sinuo Liu, Mingyan Zeng, Minghao Wu, Xuanfan Ni, Tianqi Shi, Yu Zhao, Yefeng Liu, Chenyu Zhu, Ruizhe Li, Jiahui Geng, Qing Li, Yu Tong, Longyue Wang, Weihua Luo, Kaifu Zhang
| Challenge: | Existing datasets for instruction-following are monolingual and centered on English . existing data are unable to capture linguistic and cultural subtle differences . |
| Approach: | They propose an extension of IFEval to a localized multilingual version called Marco-Bench-MIF . their benchmark addresses linguistic constraints and cultural references via translation and verification . |
| Outcome: | The proposed extension of IFEval to a localized multilingual version covers 30 languages with varying levels of localization. |
Towards Interpretable Tabular Reasoning: Enhancing LLM Reasoning on Tabular Data with Pre-Constructed Logic Graph (2026.acl-long)
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Lirong Gao, Zewei Yu, Zhongrui Yin, Qi Zhang, Yuke Zhu, Bo Zheng, Haobo Wang, Junbo Zhao, Gang Chen, Sheng Guo
| Challenge: | Tabular data is used in fields such as finance and healthcare due to its heterogeneity and complexity. |
| Approach: | They propose a Logic-Graph-Enhanced LLM Reasoning framework that integrates the strengths of tree-based models and LLMs to improve their interpretability. |
| Outcome: | The proposed framework outperforms tree-based models and state-of-the-art LLMs on tabular prediction tasks, achieving superior accuracy and interpretability. |
I²B-LPO: Latent Policy Optimization via Iterative Information Bottleneck (2026.acl-long)
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Huilin Deng, Hongchen Luo, Yue Zhu, Long Li, Zhuoyue Chen, Xinghao Zhao, Ming LI, Chuyang Zhao, Jihai Zhang, MengChang Wang, Yang Cao, Yu Kang
| Challenge: | Existing methods for large language model reasoning suffer from exploration collapse due to the semantic homogeneity of random rollouts. |
| Approach: | They propose to use latent policy optimization via iterative information bottleneck to optimize reasoning trajectories by diversifying reasoning . |
| Outcome: | Empirical results show that the proposed method achieves state-of-the-art performance with margins of up to 5.3% in accuracy and 7.4% in diversity metrics. |
SudokuFill: A Multi-Agent Progressive Filling Framework for Document-Level Scientific Information Extraction (2026.findings-acl)
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Yang Li, Yajiao Wang, Yu Zhang, Yuanzhe Zhang, Maodi Hu, Mengting Zhang, Xi Sun, Hua Yue, Zhixiong Zhang
| Challenge: | Scientific information extraction (SciIE) is a key bottleneck for turning unstructured papers into computable knowledge bases. |
| Approach: | They propose a scientific information extraction framework that solves a Sudoku problem as a progressive filling problem. |
| Outcome: | The proposed framework outperforms the GPT-4o model on a document-level adjuvant dataset. |
Why LLMs Hallucinate on Structured Knowledge: A Mechanistic Analysis of Reasoning over Linearized Representations (2026.acl-long)
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Shanghao Li, Jinda Han, Yibo Wang, Yuanjie Zhu, Zihe Song, Langzhou He, Kenan Kamel A Alghythee, Philip S. Yu
| Challenge: | Existing literature primarily addresses this problem through external interventions such as retrieval augmentation and prompt engineering at the input or output level. |
| Approach: | They find that LLMs can still produce hallucinated outputs when using structured external knowledge. |
| Outcome: | The proposed models fail to ground the provided knowledge, causing the model to revert to parametric memory. |
Eye Movement Features Can Predict Human Preferences on Machine-Generated Texts (2026.acl-srw)
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| Challenge: | Existing studies on eye movement in text quality assessment are limited . eye-movement features are important predictors of human judgments of text quality, but are costly and inconsistent. |
| Approach: | They propose to capture eye-movement features during screen reading of LLM-generated text using a dataset that includes eye-motion recordings, reading-time measurements, and post-reading evaluations. |
| Outcome: | The proposed dataset shows that eye-movement features can significantly improve models over other probabilistic metrics, including negative log-likelihood (NLL). |
CoTEvol: Self-Evolving Chain-of-Thoughts for Data Synthesis in Mathematical Reasoning (2026.findings-acl)
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| Challenge: | Existing methods to improve LLMs' reasoning abilities suffer from diminishing returns or high computing overhead. |
| Approach: | They propose a genetic evolutionary framework that casts CoT generation as a population-based search over reasoning trajectories. |
| Outcome: | The proposed framework improves correct-CoT synthesis success by over 30% and enhances structural diversity with markedly improved efficiency. |
RRAtention: Dynamic Block Sparse Attention via Per-Head Round-Robin Shifts for Long-Context Inference (2026.acl-long)
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Siran Liu, Guoxia Wang, Sa Wang, Jinle Zeng, Haoyang Xie, Siyu Lou, Jiabin Yang, Dianhai Yu, Haifeng Wang, Chao Yang
| Challenge: | Existing approaches to dynamic sparse attention require preprocessing, lack global evaluation, violate query independence, or incur high computational overhead. |
| Approach: | They propose a dynamic sparse attention method that achieves all desirable properties through a head **r**ound-**r**obin (RR) sampling strategy. |
| Outcome: | Experiments on natural language understanding and multimodal video comprehension show that the proposed method achieves 2.4 speedup at 128K context length outperforming existing methods. |
ExpanRL: Hierarchical Reinforcement Learning for Course Concept Expansion in MOOCs (2020.aacl-main)
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| Challenge: | Existing methods for concept expansion in MOOCs are inefficient because of the diversity of MOOC courses and rapid updates. |
| Approach: | They propose an end-to-end hierarchical reinforcement learning (HRL) model for concept expansion in MOOCs that employs a two-level mechanism of seed selection and concept expansion. |
| Outcome: | The proposed model improves on nine real MOOC datasets and maintains competitive performance under different settings. |
CLUE: A Chinese Language Understanding Evaluation Benchmark (2020.coling-main)
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Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, Zhenzhong Lan
| Challenge: | Existing language evaluation benchmarks for English are limited to English . lack of such benchmarks makes it difficult to replicate success in other languages . |
| Approach: | They introduce a large-scale Chinese language understanding evaluation benchmark . the benchmark uses a set of current state-of-the-art pre-trained Chinese models . |
| Outcome: | The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is released . the benchmark evaluates models across a wide range of tasks on original Chinese text . existing language evaluation benchmarks are mostly limited to English . |
Inference-Time Language Model Alignment via Integrated Value Guidance (2024.findings-emnlp)
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| Challenge: | Large language models are fine-tuned to align with human preferences, but tuning large models is computationally intensive and complex. |
| Approach: | They propose a method that uses implicit and explicit value functions to guide language model decoding at token and chunk-level respectively. |
| Outcome: | The proposed method outperforms traditional methods and circumvents the complexities of fine-tuning. |
Generating Hashtags for Short-form Videos with Guided Signals (2023.acl-long)
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Tiezheng Yu, Hanchao Yu, Davis Liang, Yuning Mao, Shaoliang Nie, Po-Yao Huang, Madian Khabsa, Pascale Fung, Yi-Chia Wang
| Challenge: | Short-form video hashtag recommendation (SVHR) is a classification or ranking problem that selects hashtags from a set of limited candidates. |
| Approach: | They propose a short-form video hashtag recommendation task that better represents how hashtags are created naturally by retrieving relevant hashtags from a large-scale hashtag pool as extra guidance signals. |
| Outcome: | The proposed model outperforms strong classification baselines on two short-form video datasets and the guidance signals boost the performance by 8.11 and 2.17 absolute ROUGE-1 scores on average. |
AutoPKG: An Automated Framework for Dynamic E-commerce Product-Attribute Knowledge Graph Construction (2026.findings-acl)
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Pollawat Hongwimol, Haoning Shang, Chutong Wang, Zhichao Wan, Yi Gao, Yuanming Li, Lin Gui, Wenhao Sun, Cheng Yu
| Challenge: | Product attribute extraction in e-commerce is bottlenecked by ontologies that are inconsistent, incomplete, and costly to maintain. |
| Approach: | They propose a multi-agent Large Language Model framework that constructs a Product-attribute Knowledge Graph from multimodal product content. |
| Outcome: | The proposed framework achieves 0.953 WKE for product types, 0.724 WKEs for attribute keys, and 0.531 edge-level accuracy for value assertions after canonicalization on a large real-world marketplace catalog dataset from Lazada (Alibaba). |
Clip-Tuning: Towards Derivative-free Prompt Learning with a Mixture of Rewards (2022.findings-emnlp)
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| Challenge: | Existing work does not take full advantage of over-parameterized characteristics of large pre-trained language models. |
| Approach: | They propose a method that uses frozen "thinned" networks to obtain a mixture of rewards and advance the derivative-free prompt learning. |
| Outcome: | The proposed method outperforms previous gradient-free prompt learning methods and achieves parity with gradient-based counterparts on seven language understanding benchmarks under few-shot settings. |
ALERT: Adapt Language Models to Reasoning Tasks (2023.acl-long)
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Ping Yu, Tianlu Wang, Olga Golovneva, Badr AlKhamissi, Siddharth Verma, Zhijing Jin, Gargi Ghosh, Mona Diab, Asli Celikyilmaz
| Challenge: | Large language models have shown increasing in-context learning capabilities with scaling up the model and data sizes. |
| Approach: | They propose a benchmark and suite of analyses to evaluate reasoning skills of large language models. |
| Outcome: | The proposed model compares pre-trained and fine-tuned models on tasks that require reasoning skills to solve. |
Verifier-Free RL for LLMs via Intrinsic Gradient-Norm Reward (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning tasks and general tasks. |
| Approach: | They propose a "Verifier-free Intrinsic Gradient-Norm Reward" that uses only the policy model itself. |
| Outcome: | The proposed reward outperforms the state-of-the-art RLIF baseline INTUITOR on math benchmarks and shows cross-domain transfer to code benchmarks when trained only on math data. |
Covariance Matrix-Driven Image Channel Allocation for Multimodal Fake News Detection (2026.findings-acl)
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| Challenge: | Existing methods focus on designing efficient multimodal fusion frameworks to bridge the semantic gap between images and texts. |
| Approach: | They propose a covariance matrix-driven image channel allocation method that expands the number of original channel maps and assigns importance scores to the expanded channel maps. |
| Outcome: | The proposed method achieves state-of-the-art on three public multimodal fake news detection benchmark datasets. |
Vision-Language Models Mistake Head Orientation for Gaze Direction: Nonverbal Conversation Cues (2026.findings-acl)
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Zory Zhang, Pinyuan Feng, Bingyang Wang, Tianwei Zhao, Suyang Yu, Qingying Gao, Hokin Deng, Ziqiao Ma, Yijiang Li, Dezhi Luo
| Challenge: | Where someone looks is a nonverbal communication cue that children and adults readily use. |
| Approach: | They used 1,360 real-world photos to construct evaluation stimuli for Vision-Language Models (VLMs) they found a substantial performance gap between VLMs and humans . |
| Outcome: | The proposed model outperforms existing models in predicting gaze direction using head orientation rather than eye appearance. |
DAGN: Discourse-Aware Graph Network for Logical Reasoning (2021.naacl-main)
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| Challenge: | Recent QA with logical reasoning questions requires passage-level relations among the sentences. |
| Approach: | They propose a discourse-aware graph network that aggregates passage-level clues for QA by using discourse-based information. |
| Outcome: | The proposed model achieves competitive results on two logical reasoning QA datasets. |
Autoregressive Semantic Visual Reconstruction Helps VLMs Understand Better (2026.findings-acl)
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| Challenge: | Typical large vision-language models emphasize vision-to-language alignment while overlooking fine-grained visual information. |
| Approach: | They introduce autoregressive semantic visual reconstruction (ASVR) that enables joint learning of visual and textual modalities within a unified autoregression framework. |
| Outcome: | The proposed model improves baselines and multimodal understanding benchmarks by 2-3%. |
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. |
RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System (2021.naacl-demos)
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Haoyang Wen, Ying Lin, Tuan Lai, Xiaoman Pan, Sha Li, Xudong Lin, Ben Zhou, Manling Li, Haoyu Wang, Hongming Zhang, Xiaodong Yu, Alexander Dong, Zhenhailong Wang, Yi Fung, Piyush Mishra, Qing Lyu, Dídac Surís, Brian Chen, Susan Windisch Brown, Martha Palmer, Chris Callison-Burch, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Heng Ji
| Challenge: | We present a new information extraction system that can construct temporal event graphs from news documents. |
| Approach: | They propose a temporal event graph extraction system that can extract news documents . they extend the system from sentence-level event extraction to cross-document cross-media event extraction . |
| Outcome: | The proposed system can extract temporal event graphs from news documents in multiple languages and multiple data modalities. |
Boost, Disentangle, and Customize: A Robust System2-to-System1 Pipeline for Code Generation (2025.findings-acl)
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Kounianhua Du, Hanjing Wang, Jianxing Liu, Jizheng Chen, Xinyi Dai, Yasheng Wang, Ruiming Tang, Yong Yu, Jun Wang, Weinan Zhang
| Challenge: | Existing systems 2 methods for code generation are difficult to implement due to the complex hidden reasoning process and heterogeneous data distribution. |
| Approach: | They propose a framework that Boosts reasoning exploration via multi-agent collaboration and Disentangles heterogeneous data into specialized experts. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on APPS and CodeContest benchmarks and achieves 73.8% accuracy on hard problems. |
Discontinuous Named Entity Recognition as Maximal Clique Discovery (2021.acl-long)
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| Challenge: | Existing methods for named entity recognition break the recognition process into several sequential steps. |
| Approach: | They propose a method that breaks the recognition process into several sequential steps . they construct a segment graph for each sentence and a grid tagging scheme to learn it . |
| Outcome: | Experiments show that the proposed method outperforms the state-of-the-art model and achieves 5x speedup over the SOTA model. |
APOLLO: A Simple Approach for Adaptive Pretraining of Language Models for Logical Reasoning (2023.acl-long)
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Soumya Sanyal, Yichong Xu, Shuohang Wang, Ziyi Yang, Reid Pryzant, Wenhao Yu, Chenguang Zhu, Xiang Ren
| Challenge: | Existing methods to improve logical reasoning skills require complex data processing. |
| Approach: | They propose an adaptive pretraining approach to improve logical reasoning over text . they use a subset of Wikipedia sentences for pretraining and a sentence-level classification loss . |
| Outcome: | The proposed model outperforms baselines on LogiQA and ReClor. |
A Bounding Box is Worth One Token - Interleaving Layout and Text in a Large Language Model for Document Understanding (2025.findings-acl)
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Jinghui Lu, Haiyang Yu, Yanjie Wang, Yongjie Ye, Jingqun Tang, Ziwei Yang, Binghong Wu, Qi Liu, Hao Feng, Han Wang, Hao Liu, Can Huang
| Challenge: | Existing methods for integrating spatial layouts with text have limitations . existing methods produce overly long text sequences or lack autoregressive traits of LLMs . |
| Approach: | They introduce Interleaving Layout and Text in a Large Language Model (LayTextLLM) they use OCR-derived text and spatial layouts to integrate with LLMs for document understanding . |
| Outcome: | The proposed model shows an increase in performance in KIE and VQA tasks. |
Word Sense Disambiguation with Knowledge-Enhanced and Local Self-Attention-based Extractive Sense Comprehension (2022.coling-1)
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| Challenge: | Word sense disambiguation (WSD) is one of the most challenging tasks in natural language processing. |
| Approach: | They propose a method to extract the right sense from a sentence context . they propose to incorporate additional examples and definitions of related senses in WordNet . |
| Outcome: | The proposed method achieves better performance than baseline models on public benchmark datasets. |
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. |
Recurrent Knowledge Identification and Fusion for Language Model Continual Learning (2025.acl-long)
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Yujie Feng, Xujia Wang, Zexin Lu, Shenghong Fu, Guangyuan Shi, Yongxin Xu, Yasha Wang, Philip S. Yu, Xu Chu, Xiao-Ming Wu
| Challenge: | Continual learning (CL) is crucial for large language models without costly retraining. |
| Approach: | They propose a framework for recurrent knowledge identification and fusion that enables dynamic estimation of parameter importance distributions to enhance knowledge transfer. |
| Outcome: | The proposed framework mitigates catastrophic forgetting and enhances knowledge transfer. |
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI (2024.findings-eacl)
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Jianguo Zhang, Kun Qian, Zhiwei Liu, Shelby Heinecke, Rui Meng, Ye Liu, Zhou Yu, Huan Wang, Silvio Savarese, Caiming Xiong
| Challenge: | DialogStudio is the largest and most diverse collection of dialogue datasets . existing datasets lack diversity and comprehensiveness, authors say . |
| Approach: | They introduce DialogStudio: the largest and most diverse collection of dialogue datasets . DialogStuio aggregates more than 80 diverse dialogue dataset . |
| Outcome: | a new dataset is created to improve the quality and diversity of dialogue datasets . DialogStudio is the largest and most diverse collection of dialogue data . |
Towards Provably Secure Generative AI: Reliable Consensus Sampling (2026.findings-acl)
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Yu Cui, Hang Fu, Sicheng Pan, Zhuoyu Sun, Yifei Liu, Yuhong Nie, Bo Ran, Baohan Huang, Xufeng Zhang, Haibin Zhang, Cong Zuo, Licheng Wang
| Challenge: | Existing research on generative AI security is driven by mutually reinforcing attack and defense methodologies grounded in empirical experience. |
| Approach: | They propose a new algorithm that uses a random sampling algorithm to control risk. |
| Outcome: | The proposed algorithm improves robustness and utility while maintaining latency comparable to existing algorithms. |
Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey (2025.findings-naacl)
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Xiaoyu Liu, Paiheng Xu, Junda Wu, Jiaxin Yuan, Yifan Yang, Yuhang Zhou, Fuxiao Liu, Tianrui Guan, Haoliang Wang, Tong Yu, Julian McAuley, Wei Ai, Furong Huang
| Challenge: | Large Language Models (LLMs) have shown great potential to enhance Natural Language Processing (NLP) models in areas such as predictive accuracy, fairness, robustness, and explainability. |
| Approach: | They evaluate or improve generative Large Language Models from a causal perspective in areas such as reasoning capacity, fairness and safety issues, explainability, and handling multimodality. |
| Outcome: | The proposed models can be used to perform causal relationship discovery and causal effect estimation tasks. |
Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have shown impressive reasoning abilities when prompted with Chain-of-Thought (CoT). |
| Approach: | They propose to categorize Chain-of-X methods by taxonomies of nodes, i.e., the X in CoX, and application tasks, and then categorise them by taxanomies and discuss potential future directions. |
| Outcome: | The proposed methods are categorised by taxonomies of nodes, i.e., the X in CoX, and application tasks. |
CE-VDG: Counterfactual Entropy-based Bias Reduction for Video-grounded Dialogue Generation (2024.lrec-main)
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| Challenge: | Existing methods to reduce question-related bias in video-grounded dialogue generation (VDG) however, the dataset often contains inherent bias, which can cause VDG models to learn spurious correlations between questions and answers. |
| Approach: | They propose to extend the counterfactual reasoning from the information entropy perspective to the generative task, which can effectively reduce the question-related bias in the auto-regressive generation task. |
| Outcome: | The proposed method can reduce question-related bias in the auto-regressive generation task by using counterfactual entropy as an external loss. |
A Knowledge-driven Generative Model for Multi-implication Chinese Medical Procedure Entity Normalization (2020.emnlp-main)
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| Challenge: | Medical entity normalization (NEN) is a task that links medical mentions to entities in knowledge bases. |
| Approach: | They propose a sequence generative framework to generate Chinese medical procedure entity normalization by constraint decoding and category-based model refining. |
| Outcome: | The proposed model improves on baselines especially in the case of multi-implication Chinese medical procedures. |
CAPE: A Chinese Dataset for Appraisal-based Emotional Generation in Large Language Models (2025.findings-naacl)
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| Challenge: | Existing LLMs fail to capture the nuances of human emotions, making their interactions seem impersonal or inadequate. |
| Approach: | They propose a two-stage automatic data generation framework to generate a Chinese dataset called CAPE . their data is a cognitive appraisal theory-based Emotional corpus that accounts for personal and situational factors. |
| Outcome: | The proposed framework can generate human-like responses in conversation with large language models. |
Detecting Health Advice in Medical Research Literature (2021.emnlp-main)
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| Challenge: | Health and medical researchers often give clinical and policy recommendations to inform health practice and public health policy. |
| Approach: | They developed a BERT-based prediction model that can predict whether a sentence gives strong advice, weak advice, or not. |
| Outcome: | The proposed model can predict whether a sentence gives strong advice, weak advice, or not with a macro-averaged F1 score of 0.93. |
Attribution-Based Analysis and Optimization of Modular Agentic Workflows (2026.findings-acl)
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Yingxuan Yang, Bo Huang, Siyuan Qi, Chao Feng, Haoyi Hu, Yuxuan Zhu, Jinbo Hu, Haoran Zhao, Ziyi He, Xiao Liu, ZongYu Wang, Muning Wen, Lin Qiu, Xuezhi Cao, Xunliang Cai, Yong Yu, Weinan Zhang
| Challenge: | Large Language Models (LLMs) have driven the rise of agentic workflows . yet, how can we attribute performance gains to individual upgrades and their interactions? |
| Approach: | They propose a game-theoretic framework that models component upgrades as players and evaluates component coalitions to compute Shapley values. |
| Outcome: | The proposed framework provides interaction-aware attribution and recommendation for model allocation under a fixed workflow structure. |
Does Listener Gaze in Face-to-Face Interaction Follow the Entropy Rate Constancy Principle: An Empirical Study (2023.findings-emnlp)
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| Challenge: | Existing studies have shown that nonverbal behaviours are rich in communicative functions, such as gaze, head movements, and speech-accompanying manual gestures. |
| Approach: | They train a transformer-based neural sequence model to process gaze data extracted from video-recorded conversations and compute its information density. |
| Outcome: | The proposed model computes listeners’ gaze behaviour and the information density of speech using a pre-trained language model. |
FedPETuning: When Federated Learning Meets the Parameter-Efficient Tuning Methods of Pre-trained Language Models (2023.findings-acl)
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| Challenge: | Existing research on federated learning (FL) for pre-trained language models (PLMs) with increasing concerns about data privacy, enterprises or institutions are not allowed to collect data from end devices or local clients to a centralized server for fine-tuning PLMs. |
| Approach: | They investigate the parameter-efficient tuning of pre-trained language models (PLMs) and develop a federated benchmark for four representative PETuning methods . |
| Outcome: | The proposed method can defend against privacy attacks and maintain acceptable performance with reducing heavy resource consumption. |
Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning (2025.findings-emnlp)
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| Challenge: | Existing methods for dynamic spatial reasoning are limited to text or static visual domains . |
| Approach: | They propose a framework that augments textual reasoning chains with dynamic visual drafts . |
| Outcome: | The proposed framework outperforms existing methods in dynamic spatial reasoning tasks. |
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. |
Better Document-Level Machine Translation with Bayes’ Rule (2020.tacl-1)
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| Challenge: | Existing document translation models are based on autoregressive language models, but they are not able to be learned from monolingual documents. |
| Approach: | They propose to use Bayes' rule to create document translation models that can be learned from only parallel sentences and monolingual documents. |
| Outcome: | The proposed model outperforms existing document translation approaches and is based on a novel left-to-right beam-search algorithm. |
Bypassing Neural Evaluations for Fast Audio Editing via Adaptive Trajectory Extrapolation (2026.findings-acl)
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Xiaoqian Liu, Zhengkun Ge, Jianjin Wang, Haoran Zhang, Yuan Ge, Kaiyan Chang, Chen Xu, Tong Xiao, Zhengtao Yu, Linfeng Zhang, JingBo Zhu
| Challenge: | Recent advances in audio diffusion models have significantly improved text-to-audio editing via inversion techniques, but these models typically rely on dense, fixed-step sampling trajectories to maintain structural integrity. |
| Approach: | They propose a model-agnostic Adaptive Trajectory Extrapolation framework that accelerates inversion-based editing process by dynamically evaluating only the most critical generative phases. |
| Outcome: | The proposed framework achieves a 3.9 speedup with negligible loss in fidelity. |
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. |
MultiSQL: A Schema-Integrated Context-Dependent Text2SQL Dataset with Diverse SQL Operations (2024.findings-acl)
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| Challenge: | Text2SQL is a task that translates natural language into SQL statements. |
| Approach: | They propose a task that translates natural language into SQL statements. |
| Outcome: | The proposed task enables users to convert natural language into SQL statements. |
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios (2022.naacl-demo)
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Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, Heng Ji
| Challenge: | Existing methods for event prediction are incomplete and noisy. |
| Approach: | They propose to use news-related event schemas to extract newsworthy events . they build a demo website and include a video demonstrating the framework . |
| Outcome: | The proposed framework can be applied to a wide variety of newsworthy scenarios. |
Personalized Federated Learning for Text Classification with Gradient-Free Prompt Tuning (2024.findings-naacl)
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Rui Wang, Tong Yu, Ruiyi Zhang, Sungchul Kim, Ryan Rossi, Handong Zhao, Junda Wu, Subrata Mitra, Lina Yao, Ricardo Henao
| Challenge: | Pretrained language models (PLMs) are used for personalized federated learning . communication costs are high with large PLMs, and local training is expensive . |
| Approach: | They propose a framework for federated learning with pretrained language models . they propose 'discrete local search' and compression mechanism for local training . |
| Outcome: | The proposed framework achieves superior performance compared with baselines. |
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation (D18-1)
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| Challenge: | Empirical results show that even the most competitive few-shot learning models struggle on this task, especially as compared with humans. |
| Approach: | They propose a Few-Shot Relation Classification Dataset consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers. |
| Outcome: | The proposed methods perform well on the most competitive few-shot learning models, especially as compared with humans. |
Beyond Unimodal Shortcuts: MLLMs as Cross-Modal Reasoners for Grounded Named Entity Recognition (2026.findings-acl)
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| Challenge: | Existing approaches to GMNER use MLLMs as auxiliary tools, causing cumulative error propagation and a lack of rigorous cross-modal verification. |
| Approach: | They propose a model that enforces structured cross-modal reasoning through Multi-style Reasoning Schema Injection and Constraint-guided Verifiable Optimization. |
| Outcome: | The proposed model enforces structured cross-modal reasoning through multi-style Reasoning Schema Injection and Constraint-guided Verifiable Optimization. |
Improving Neural Machine Translation by Multi-Knowledge Integration with Prompting (2023.findings-emnlp)
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| Challenge: | Existing methods focus on how to integrate multiple types of knowledge into NMT models . |
| Approach: | They propose a framework that integrates multiple types of knowledge into NMT models . they use multiple types as prefix-prompts of input for the encoder and decoder . |
| Outcome: | The proposed framework outperforms baselines on English-Chinese and English-German translation. |
KeFVP: Knowledge-enhanced Financial Volatility Prediction (2023.findings-emnlp)
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| Challenge: | Current studies ignore the role of financial metrics knowledge in earnings calls and little consideration is given to integrating text and price information. |
| Approach: | They propose to integrate financial metrics knowledge into text comprehension by knowledge-enhanced adaptive pre-training and effectively incorporating text and price information by introducing a conditional time series prediction module. |
| Outcome: | The proposed method outperforms state-of-the-art methods on three real-world datasets and is effective and reliable. |
Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue StateTracking (2021.naacl-main)
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Zhaojiang Lin, Bing Liu, Seungwhan Moon, Paul Crook, Zhenpeng Zhou, Zhiguang Wang, Zhou Yu, Andrea Madotto, Eunjoon Cho, Rajen Subba
| Challenge: | Existing models for zero-shot cross-domain dialogue state tracking require in-domain data to model a new domain. |
| Approach: | They propose a slot descriptions enhanced generative approach for zero-shot cross-domain DST by encoding a dialogue context and a slots with a pre-trained encoder and generating slot value in auto-regressive manner. |
| Outcome: | The proposed model significantly improves state-of-the-art results in zero-shot cross-domain setting. |
Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader (P19-1)
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| Challenge: | Existing models that use incomplete knowledge bases and text data to answer open-domain questions are insufficient to cover full evidence. |
| Approach: | They propose a model which learns to aggregate answer evidence from incomplete knowledge bases and text snippets. |
| Outcome: | The proposed model improves on the widely-used KBQA benchmark WebQSP across settings with different extents of incompleteness. |
SAD: A Large-Scale Strategic Argumentative Dialogue Dataset (2026.acl-long)
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YongKang Liu, Jiayang Yu, Mingyang Wang, Yiqun Zhang, Ercong Nie, Shi Feng, Daling Wang, Kaisong Song, Hinrich Schuetze
| Challenge: | Argumentation is a key part of human reasoning and decision-making . existing argumentative corpora focus on single-turn settings, but multi-turn dialogues are often realized as multi-turned dialogues . |
| Approach: | They present a dataset for strategic multi-turn argumentation dialogues . they annotate each utterance with five strategy types, allowing multiple strategies per utterrance . |
| Outcome: | The proposed dataset shows that explicit prompting improves fluency, stylistic coherence and persuasiveness. |
Training Language Model to Critique for Better Refinement (2025.findings-acl)
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Tianshu Yu, Chao Xiang, Mingchuan Yang, Pei Ke, Bosi Wen, Cunxiang Wang, Jiale Cheng, Li Zhang, Xinyu Mu, Chuxiong Sun, Minlie Huang
| Challenge: | Large language models (LLMs) have remarkable evaluation and critique capabilities, providing insightful feedback and identifying flaws in various tasks. |
| Approach: | They propose a framework to train critic models using refinement signals to generate feedback loops where critiques guide the model in refining its responses. |
| Outcome: | The proposed framework outperforms traditional methods and open-source models in terms of critique quality and refinement outcomes. |
From Specific-MLLMs to Omni-MLLMs: A Survey on MLLMs Aligned with Multi-modalities (2025.findings-acl)
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Shixin Jiang, Jiafeng Liang, Jiyuan Wang, Xuan Dong, Heng Chang, Weijiang Yu, Jinhua Du, Ming Liu, Bing Qin
| Challenge: | MLLMs are able to integrate multiple modalities into a single model to tackle complex tasks in real-world scenarios. |
| Approach: | They propose a comprehensive survey of Omni-MLLMs to address the challenges and opportunities of multimodal modeling. |
| Outcome: | The proposed model can integrate multiple modalities into a single model and provide novel perspectives. |
Don’t Tell the Answer, Truly Guide the Reasoning During RL Rollouts (2026.findings-acl)
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Xinyi Wang, Jinyi Han, Zishang Jiang, Tingyun li, Jiaqing Liang, Sihang Jiang, Zhaoqian Dai, Ma Shuguang, Fei Yu, Yanghua Xiao
| Challenge: | Existing methods such as GRPO often break down when task difficulty exceeds the model’s capacity, resulting in sparse rewards and inefficient training. |
| Approach: | They propose to measure the compatibility between external guidance and a model's intrinsic policy by introducing an adaptive framework to enhance reasoning performance while explicitly preserving high Affinity. |
| Outcome: | The proposed framework outperforms baseline models while maintaining high Affinity. |
On the Emotion Understanding of Synthesized Speech (2026.acl-long)
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Yuan Ge, Haishu Zhao, AoKai Hao, Junxiang Zhang, Bei Li, Xiaoqian Liu, Chenglong Wang, Jianjin Wang, Bingsen Zhou, Bingyu Liu, JingBo Zhu, Zhengtao Yu, Tong Xiao
| Challenge: | Existing models for emotion understanding do not capture fundamental features of synthesized speech. |
| Approach: | They evaluate emotion recognition models on synthesized speech using SER models and generative models. |
| Outcome: | The proposed model can't generalize to synthesized speech because of speech token prediction . generative models tend to infer emotion from textual semantics while ignoring paralinguistic cues. |
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. |
Internalizing Multi-Agent Reasoning for Accurate and Efficient LLM-based Recommendation (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are reshaping recommender systems by leveraging extensive world knowledge and semantic reasoning to interpret user intent. |
| Approach: | They propose a single-agent Trajectory-Aligned Recommender to integrate reasoning capabilities into a model by a multi-agend teacher system. |
| Outcome: | The proposed model surpasses its teacher by 8.7% to 39.5% while eliminating iterative latency. |
Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applications (2025.acl-long)
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| Challenge: | Existing approaches to source planning fail to achieve this due to misalignment between the model’s expectation of the sources and their actual content. |
| Approach: | They propose a method to optimise large-scale medical knowledge models by combining multiple medical knowledge sources into one query. |
| Outcome: | The proposed method significantly improves multi-source planning performance while training a smaller model to learn source alignment. |
Double-Edged Sword or Sharp Tool? Designing and Evaluating Triadic LLM-Teacher Collaboration for K-12 Writing at Scale (2026.findings-acl)
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| Challenge: | a large-scale empirical dataset involving 57,954 essays from 10,195 students across 120 schools over two years is presented in this paper. |
| Approach: | They propose a triadic collaboration system that supports K-12 writing learning . they propose linguistic expansion as a pedagogical gatekeeper and bridge . |
| Outcome: | The proposed system improves writing quality through a strategic labor division . authors find that excessive linguistic expansion yields diminishing marginal utility . |
Reason Only When Needed: Efficient Generative Reward Modeling via Model-Internal Uncertainty (2026.findings-acl)
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Chao Xue, Yao Wang, Mengqiao Liu, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Chenyao Lu, Lei Jiang, Yu Lu, Haibo Shi, Shuang Liang, Minlong Peng, Flora D. Salim
| Challenge: | Existing approaches to generating reward models rely on voting-based mechanisms to evaluate CoT outputs. |
| Approach: | They propose an efficient generative reward modeling framework grounded in model-internal uncertainty. |
| Outcome: | The proposed framework reduces inference cost while improving answer accuracy. |
Training LLMs to be Better Text Embedders through Bidirectional Reconstruction (2025.emnlp-main)
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Chang Su, Dengliang Shi, Siyuan Huang, Jintao Du, Changhua Meng, Yu Cheng, Weiqiang Wang, Zhouhan Lin
| Challenge: | Existing text embedding approaches often leverage the embeddment of the final token, typically a reserved special token such as ‘[EOS]‘. |
| Approach: | They propose to add a new training stage before contrastive learning to enrich the semantics of the final token embedding. |
| Outcome: | The proposed training stage improves performance on the Massive Text Embedding Benchmark (MTEB), achieving new state-of-the-art results across different LLM base models and scales. |
Improving Empathetic Response Generation by Recognizing Emotion Cause in Conversations (2021.findings-emnlp)
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| Challenge: | Existing approaches to empathetic response generation ignore the emotion cause . existing dialogue systems lack emotion understanding and empathy . |
| Approach: | They propose a framework that integrates emotion cause information into empathetic response generation by predicting context emotion labels and sequence of emotion cause-oriented labels. |
| Outcome: | The proposed framework improves empathetic response generation by incorporating emotion cause information into the model. |
COPR: Continual Human Preference Learning via Optimal Policy Regularization (2025.findings-acl)
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Han Zhang, Lin Gui, Yu Lei, Yuanzhao Zhai, Yehong Zhang, Zhuo Zhang, Yulan He, Hui Wang, Yue Yu, Kam-Fai Wong, Bin Liang, Ruifeng Xu
| Challenge: | Reinforcement Learning from Human Feedback (RLHF) is effective for aligning Large Language Models with human preferences, but its complex process limits its ability to continually learn human feedback. |
| Approach: | They propose a non-RL offline method to convert historical optimal policies into optimization constraints when continually learning new preferences. |
| Outcome: | The proposed method outperforms strong CL baselines in terms of reward-based evaluations and human assessment. |
MathCanvas: Intrinsic Visual Chain-of-Thought for Multimodal Mathematical Reasoning (2026.acl-long)
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Weikang Shi, Aldrich Yu, Rongyao Fang, Houxing Ren, Ke Wang, Aojun Zhou, Changyao Tian, Xinyu Fu, Yuxuan Hu, Zimu Lu, Linjiang Huang, Si Liu, Rui Liu, Hongsheng Li
| Challenge: | Existing approaches to visual chain-of-thought are limited by external tools or fail to generate high-fidelity diagrams. |
| Approach: | They propose a framework to enable large multimodal models with VCoT capabilities . they pre-train a model on a 15.2M-pair corpus and teach it how to leverage visual aids . |
| Outcome: | The proposed framework unlocks complex, human-like visual reasoning in large language models . it pre-trains the model on a 15.2M-pair corpus and fine-tunes it on MathCanvas-Instruct . |
Speech-Text Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment (2023.acl-long)
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Tianshu Yu, Haoyu Gao, Ting-En Lin, Min Yang, Yuchuan Wu, Wentao Ma, Chao Wang, Fei Huang, Yongbin Li
| Challenge: | Existing speech-text pre-training methods are limited to one or two specific tasks, despite their success in speech-language processing tasks. |
| Approach: | They propose a temporal position prediction task to capture the speech-text alignment . they use a textual dialog pre-training task to generalize a response selection task . |
| Outcome: | The proposed model is superior in learning speech-text alignment and multi-turn dialog context. |
WebAggregator: Enhancing Compositional Reasoning Capabilities of Deep Research Agent Foundation Models (2026.acl-long)
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Rui Wang, Ce Zhang, Jun-Yu Ma, Jianshu Zhang, Hongru Wang, Yi Chen, Boyang Xue, Tianqing Fang, Zhisong Zhang, Hongming Zhang, Haitao Mi, Dong Yu, Kam-Fai Wong
| Challenge: | Existing agentic systems are retrieval-heavy but reasoning-light . current systems lack compositional reasoning, a key component of deep research . |
| Approach: | They propose a data synthesis pipeline WebAggregator to shift agentic paradigm . they use Proactive Explorer to collect interconnected knowledge and Compositional Logic Proposer to weave knowledge into complex questions . |
| Outcome: | The proposed pipeline surpasses GPT-4.1 and matches Claude-3.7-Sonnet on GAIA, WebWalkerQA, and XBench. |
START: Self-taught Reasoner with Tools (2025.emnlp-main)
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Chengpeng Li, Mingfeng Xue, Zhenru Zhang, Jiaxi Yang, Beichen Zhang, Bowen Yu, Binyuan Hui, Junyang Lin, Xiang Wang, Dayiheng Liu
| Challenge: | Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in complex reasoning through long chain-of-thought, yet they struggle with precise computations and algorithmic operations. |
| Approach: | They propose a training-free approach that activates LRMs’ latent tool-use capabilities through artificial hints and a framework that enables models to learn effective tool utilization through diverse hint patterns and rejection-based data synthesis. |
| Outcome: | Experiments show that START significantly improves state-of-the-art LRMs across challenging benchmarks, including competition-level mathematics (AMC23: 95.0%, AIME24: 75.6%) and graduate-level science questions (GPQA: 64.6%). |
CF-TCIR: A Compositor-Free Framework for Hierarchical Text-Conditioned Image Retrieval (2024.findings-acl)
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| Challenge: | Existing approaches to text-conditioned image retrieval use attention-driven compositors . instead, we reformulate the retrieval process as a cross-modal interaction between a synthesized image feature and its corresponding text descriptor. |
| Approach: | They propose a compositor-free framework for text-conditioned image retrieval . they propose combining a reference image and modification text to form a query tuple . |
| Outcome: | The proposed framework offers advantages in terms of computational efficiency, scalability, and performance. |
Coarse-to-Fine Pre-training for Named Entity Recognition (2020.emnlp-main)
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| Challenge: | Named Entity Recognition (NER) is a task of discovering information entities and identifying their corresponding categories. |
| Approach: | They propose a NER-specific framework to inject coarse-to-fine named entity knowledge into pre-trained models by using a remote supervision strategy. |
| Outcome: | The proposed framework achieves significant improvements against several pre-trained base-lines, demonstrating its effectiveness in label-few and low-resource scenarios. |
HIT: Nested Named Entity Recognition via Head-Tail Pair and Token Interaction (2020.emnlp-main)
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| Challenge: | Named Entity Recognition (NER) is a fundamental task in natural language processing due to the nature of the named entity. |
| Approach: | They propose a nested NER model that leverages two key properties pertaining to the named entity, including explicit boundary tokens and tight internal connection between tokens within the boundary. |
| Outcome: | The proposed model achieves state-of-the-art on three public NER datasets. |
Temperature-Centric Investigation of Speculative Decoding with Knowledge Distillation (2024.findings-emnlp)
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| Challenge: | Speculative decoding is a novel method to expedite inference in autoregressive (large) language models. |
| Approach: | They propose to use a smaller model as a draft model to speculate a block of tokens, which the target model then evaluates for acceptance. |
| Outcome: | The proposed method can be used to accelerate inference in autoregressive (large) language models by using smaller models as draft models to speculate tokens for multiple inference steps. |
Hyperbolic Geometry is Not Necessary: Lightweight Euclidean-Based Models for Low-Dimensional Knowledge Graph Embeddings (2021.findings-emnlp)
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| Challenge: | Recent knowledge graph embedding models based on hyperbolic geometry are complicated than Euclidean operations. |
| Approach: | They propose to use hyperbolic geometry to generate high-fidelity and parsimonious representations of hierarchical patterns in knowledge graphs. |
| Outcome: | The proposed models achieve state-of-the-art performance on two widely-used datasets and cost less than RotH. |
Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation (2020.findings-emnlp)
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| Challenge: | Event detection (ED) is a key subtask of information extraction. |
| Approach: | They propose an architecture that exploits syntactic structure and typed dependency label information to perform event detection. |
| Outcome: | The proposed architecture exploits syntactic structure and typed dependency label information to perform ED. |
DeepNote: Note-Centric Deep Retrieval-Augmented Generation (2025.findings-emnlp)
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Ruobing Wang, Qingfei Zhao, Yukun Yan, Daren Zha, Yuxuan Chen, Shi Yu, Zhenghao Liu, Yixuan Wang, Shuo Wang, Xu Han, Zhiyuan Liu, Maosong Sun
| Challenge: | . - (EN) |
| Approach: | . - (EN) |
| Outcome: | . - (EN) |
Learning to Prune Dependency Trees with Rethinking for Neural Relation Extraction (2020.coling-main)
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| Challenge: | Existing approaches to remove noise from dependency trees are not optimal due to complexity and variability of natural language. |
| Approach: | They propose a dynamically pruned Graph Convolutional Network (DP-GCN) that prunes the dependency tree with rethinking in an end-to-end scheme. |
| Outcome: | The proposed model achieves impressive results compared to strong competitors. |
MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization (2024.findings-acl)
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Zhiyu Yang, Zihan Zhou, Shuo Wang, Xin Cong, Xu Han, Yukun Yan, Zhenghao Liu, Zhixing Tan, Pengyuan Liu, Dong Yu, Zhiyuan Liu, Xiaodong Shi, Maosong Sun
| Challenge: | Scientific data visualization is an essential process in research, but its use of large language models remains unexplored. |
| Approach: | They propose a model-agnostic LLM agent framework to automate scientific data visualization tasks. |
| Outcome: | The proposed framework improves performance of commercial and open-source models. |
SimPBL: A Multi-Agent Framework for Project-Based Learning (2026.acl-long)
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Daniel Zhang-Li, Joy Jia Yin Lim, Binglin Liu, Shangqing Tu, Zijun Yao, Hao Peng, Jifan Yu, Haoxuan Li, Zhanxin Hao, Ye He, Zekun Li, Jiangyi Wang, Lei Hou, Bin Xu, Xin Cong, Zhiyuan Liu, Huiqin Liu, Yu Zhang, Juanzi Li
| Challenge: | Existing LLMs provide partial assistance without modeling these roles, and overly comprehensive help can reduce learner autonomy. |
| Approach: | They propose a multi-agent framework with an orchestrator agent that provides adaptive scaffolding from interaction logs and collaborator agents that support project work through boundary-aware collaboration. |
| Outcome: | The proposed framework improves learner examination scores by 14% . it is based on a multi-agent framework with an orchestrator agent . |
OpenFact: Factuality Enhanced Open Knowledge Extraction (2023.tacl-1)
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Linfeng Song, Ante Wang, Xiaoman Pan, Hongming Zhang, Dian Yu, Lifeng Jin, Haitao Mi, Jinsong Su, Yue Zhang, Dong Yu
| Challenge: | Existing OIE systems organize knowledge into subject-relation-object (SRO) triplets, and they use templates to extract such knowledge triplet. |
| Approach: | They propose a framework to handle expressiveness and groundedness in OpenFact . they propose to use templates, extra constraints, and adopt human efforts to ensure that most triplets contain enough details. |
| Outcome: | The proposed framework improves expressiveness and groundedness of OpenFact . it is more accurate and denser than OPIEC-Linked, which is grounded to Wikidata . |
Mixture-of-LoRAs: An Efficient Multitask Tuning Method for Large Language Models (2024.lrec-main)
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| Challenge: | Instruction Tuning has the potential to stimulate or enhance specific capabilities of large language models. |
| Approach: | They propose a mixture-of-LoRAs architecture which is a parameter-efficient tuning method designed for multi-task learning with LLMs. |
| Outcome: | The proposed method can be iteratively adapted to a new domain, enabling quick domain-specific adaptation. |
A Simple LLM Framework for Long-Range Video Question-Answering (2024.emnlp-main)
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| Challenge: | a recent study has shown that short video understanding is not trivial due to the need for long-range temporal reasoning capabilities. |
| Approach: | They propose a language-based short- and long-range question-answering framework LLoVi . they propose 'multi-round summarization prompt' that asks the LLM to summarize the captions . |
| Outcome: | The proposed framework outperforms the state-of-the-art on the EgoSchema dataset and to grounded VideoQA. |
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 . |
Adaptive Detoxification: Safeguarding General Capabilities of LLMs through Toxicity-Aware Knowledge Editing (2025.findings-acl)
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Yifan Lu, Jing Li, Yigeng Zhou, Yihui Zhang, Wenya Wang, Xiucheng Li, Meishan Zhang, Fangming Liu, Jun Yu, Min Zhang
| Challenge: | Existing knowledge editing methods for large language models (LLMs) suffer from over-editing, where detoxified models reject legitimate queries, compromising overall performance. |
| Approach: | They propose a toxicity-aware knowledge editing approach that dynamically detects toxic activation patterns during forward propagation and then routes computations through adaptive inter-layer pathways to mitigate toxicity effectively. |
| Outcome: | The proposed method outperforms existing methods on large language models and enhances the SafeEdit benchmark. |
CuMA: Aligning LLMs with Sparse Cultural Values via Demographic-Aware Mixture of Adapters (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) have a global audience, so alignment must extend to cultural resonance. |
| Approach: | They propose a framework that frames alignment as a conditional capacity separation problem. |
| Outcome: | The proposed framework outperforms both dense baselines and semantic-only MoEs on three large language models. |
ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora (2021.emnlp-main)
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| Challenge: | Existing methods for pretraining cross-lingual models are limited in their size due to the limited amount of parallel corpora. |
| Approach: | They propose a method that encourages the model to align multiple languages with monolingual corpora to overcome the constraint of the parallel corpus size. |
| Outcome: | The proposed method outperforms existing cross-lingual models and delivers new state-of-the-art results in various cross-linguistic downstream tasks. |
A Facial Expression-Aware Multimodal Multi-task Learning Framework for Emotion Recognition in Multi-party Conversations (2023.acl-long)
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| Challenge: | Recent studies have shown the importance of visual information in multi-party conversations due to the complexity of visual scenes. |
| Approach: | They propose a framework to extract face sequences as visual features from a real speaker's utterance and a pipeline method to extract the face sequence. |
| Outcome: | The proposed framework extracts face sequences of the real speaker of each utterance and improves emotion prediction on the MELD dataset. |
FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation (2022.lrec-1)
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Wenhao Zhu, Shujian Huang, Tong Pu, Pingxuan Huang, Xu Zhang, Jian Yu, Wei Chen, Yanfeng Wang, Jiajun Chen
| Challenge: | Recent research on domain adaptation neglects diversity in translation within a domain . current research on NMT models considers very broad target domains . |
| Approach: | They propose a fine-grained domain adaptation task for autonomous vehicles, AI education, real-time networks, and smart phone. |
| Outcome: | The proposed task is compared with a dataset of Chinese-English translation tasks for four sub-domains of information technology: autonomous vehicles, AI education, real-time networks, and smart phone. |
Comprehensive Benchmarking of Long-Form Speech Generation in Diverse Scenarios (2026.findings-acl)
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Changhao Pan, Rui Yang, Han Wang, Zhuan Zhou, Xuming He, Wenxiang Guo, Ziyue Jiang, Ruiqi Li, Yu Zhang, Chenyuhao Wen, Ke Lei, Xiang Yin, Jingyu Lu, Zhiyuan Zhu, Zhou Zhao
| Challenge: | Existing evaluation benchmarks for long-form speech are limited to limited domains, creating a significant gap with the diverse downstream applications. |
| Approach: | They propose a benchmark that decomposes "long-form speech quality" into specific, disentangled dimensions. |
| Outcome: | The proposed benchmark decomposes “long-form speech quality” into specific, disentangled dimensions. |
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. |
How to Build User Simulators to Train RL-based Dialog Systems (D19-1)
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| Challenge: | RL-based dialog systems require interaction with the environment and obtaining real human users to interact with the system is time-consuming and labor-intensive. |
| Approach: | They propose a method to standardize user simulator building to compare dialog system quality using the same set of user simulators. |
| Outcome: | The proposed method can be used by the community to compare dialog system quality using the same set of user simulators fairly. |
Connecting Embeddings for Knowledge Graph Entity Typing (2020.acl-main)
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| Challenge: | Existing knowledge graphs suffer from incompleteness and miss important facts, jeopardizing their usefulness in downstream tasks such as question answering. |
| Approach: | They propose a method which is trained by utilizing local typing knowledge from existing entity type assertions and global triple knowledge in KGs. |
| Outcome: | The proposed model favors inferences that agree with both entity type instances and triple knowledge in KGs. |
INTERVENOR: Prompting the Coding Ability of Large Language Models with the Interactive Chain of Repair (2024.findings-acl)
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| Challenge: | Experimental results show that INTERVENOR surpasses baseline models, exhibiting improvements of approximately 18% and 4.3% over GPT-3.5 in code generation and code translation tasks. |
| Approach: | They propose a system that prompts Large Language Models to play distinct roles during the code repair process, functioning as both a Code Learner and a code teacher. |
| Outcome: | The proposed system surpasses baseline models in code generation and code translation tasks and improves on syntax errors and assertion errors. |
Dual Contrastive Learning Framework for Incremental Text Classification (2023.findings-emnlp)
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| Challenge: | In incremental learning, large models learn and refresh knowledge continuously . many approaches have been proposed to preserve knowledge from previous tasks while learning new concepts in online NLP applications. |
| Approach: | They propose a dual contrastive learning framework that fosters transferability across different tasks . they use global contrastive and task-specific learning to promote a generalized embedding space . |
| Outcome: | The proposed framework outperforms the current state-of-the-art methods on text datasets. |
Adaptive Structure Induction for Aspect-based Sentiment Analysis with Spectral Perspective (2023.findings-emnlp)
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| Challenge: | incorporating structure information can enhance the performance of aspect-based sentiment analysis. |
| Approach: | They propose to use pre-trained language models to induct latent structures from a spectrum perspective. |
| Outcome: | The proposed model shortens Aspects-sentiment Distance and improves structure induction ability. |
E2LLM: Encoder Elongated Large Language Models for Long-Context Understanding and Reasoning (2025.emnlp-main)
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| Challenge: | Considerable efforts have been and are still being put into increasing the context length of Large Language Models (LLMs) |
| Approach: | They propose an approach that divides long contexts into chunks, compresses each into soft prompts using a pretrained text encoder, and aligns these representations with a decoder-only LLM via an adapter. |
| Outcome: | The proposed approach outperforms 8 state-of-the-art methods in effectiveness and efficiency for document summarization and question answering, and achieves the best performance on LongBench v2 among models of comparable size. |
MetaMem: Evolving Meta-Memory for Knowledge Utilization through Self-Reflective Symbolic Optimization (2026.findings-acl)
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| Challenge: | Existing memory systems can support long-horizon human-LLM interactions by persisting historical interactions beyond limited context windows. |
| Approach: | They propose a framework that augments memory systems with a self-evolving meta-memory . meta-meso is iteratively distilling transferable knowledge utilization experiences . results show MetaMem outperforms strong baselines by over 3.6% . |
| Outcome: | The proposed framework outperforms baselines by over 3.6% in the long-horizon human-LLM interaction. |
MOOCCube: A Large-scale Data Repository for NLP Applications in MOOCs (2020.acl-main)
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Jifan Yu, Gan Luo, Tong Xiao, Qingyang Zhong, Yuquan Wang, Wenzheng Feng, Junyi Luo, Chenyu Wang, Lei Hou, Juanzi Li, Zhiyuan Liu, Jie Tang
| Challenge: | Massive open online courses (MOOCs) are a popular educational platform for advanced research. |
| Approach: | They propose to use MOOCCube to build a large-scale data repository of over 700 MOOC courses, 100k concepts, 8 million student behaviors with an external resource. |
| Outcome: | The proposed datasets show that they can facilitate research in MOOCs. |
V-MAGE: A Game Evaluation Framework for Assessing Vision-Centric Capabilities in Multimodal Large Language Models (2026.findings-acl)
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Xiangxi Zheng, Linjie Li, Zhengyuan Yang, Ping Yu, Alex Jinpeng Wang, Rui Yan, Yuan Yao, Lijuan Wang
| Challenge: | Existing static image-text benchmarks are insufficient for evaluating multimodal large language models’ dynamic perception and interactive reasoning abilities. |
| Approach: | They propose a game-based evaluation framework to assess multimodal large language models’ visual reasoning in dynamic, continuous-space environments. |
| Outcome: | The proposed framework systematically assesses MLLMs’ visual reasoning in dynamic, continuous-space environments. |
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. |
Investigating BERT’s Knowledge of Language: Five Analysis Methods with NPIs (D19-1)
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Alex Warstadt, Yu Cao, Ioana Grosu, Wei Peng, Hagen Blix, Yining Nie, Anna Alsop, Shikha Bordia, Haokun Liu, Alicia Parrish, Sheng-Fu Wang, Jason Phang, Anhad Mohananey, Phu Mon Htut, Paloma Jeretic, Samuel R. Bowman
| Challenge: | Recent work evaluating sentence representation models' knowledge of grammar has been slower to emerge. |
| Approach: | They propose five experimental methods inspired by prior work evaluating pretrained sentence representation models to examine their grammatical knowledge. |
| Outcome: | The proposed methods show that the model has significant knowledge of the licensing environment but its success varies widely across different methods. |
Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers? (2025.coling-main)
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Mingyu Jin, Qinkai Yu, Jingyuan Huang, Qingcheng Zeng, Zhenting Wang, Wenyue Hua, Haiyan Zhao, Kai Mei, Yanda Meng, Kaize Ding, Fan Yang, Mengnan Du, Yongfeng Zhang
| Challenge: | Large language models have shown remarkable performances across a wide range of tasks, but mechanisms by which they encode tasks of varying complexity remain poorly understood. |
| Approach: | They propose to explore the possibility that LLMs process concepts in different layers . they propose to categorize concepts based on their level of abstraction . |
| Outcome: | The proposed model can process complex concepts in shallow layers, the authors show . the proposed model could be used to prob complex tasks in shallow ones . |
Improving LLM Generations via Fine-Grained Self-Endorsement (2024.findings-acl)
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| Challenge: | Recent large language models (LLMs) have demonstrated remarkable capabilities but can still fail frequently on knowledge-intensive tasks. |
| Approach: | They propose a self-endorsement framework that leverages fine-grained fact-level comparisons across multiple sampled responses. |
| Outcome: | The proposed framework can improve factuality of generations with simple prompts across scales of LLMs. |
UniGeM: Unifying Data Selection and Mixing via Geometric Exploration and Mining (2026.findings-acl)
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Changhao Wang, null Yunfeiyu, Xinhao Yao, Jiaolong Yang, Lu Yu, Junpeng Fang, Chaobo Li, Riccardo Cantoro, Qing Cui, Jun Zhou
| Challenge: | Large Language Models (LLMs) scaling is limited by data quality and domain mixing and instance selection are two separate problems. |
| Approach: | They propose a framework that unifies mixing and selection without training proxy models or relying on external reference datasets. |
| Outcome: | The proposed framework achieves 2.0 data efficiency over a random baseline and further improves overall performance compared to SOTA methods in reasoning-heavy evaluations and multilingual generalization. |
Selective Prompting Tuning for Personalized Conversations with LLMs (2024.findings-acl)
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| Challenge: | Personalization in conversational AI requires persona profiles and contextual understanding to create meaningful conversations. |
| Approach: | They propose a method that softly prompts LLMs for personalized conversations in a selective way. |
| Outcome: | The proposed approach improves response diversity by up to 90% on the CONVAI2 dataset. |
MedCare: Advancing Medical LLMs through Decoupling Clinical Alignment and Knowledge Aggregation (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have made significant progress in natural language understanding and generation, proving valuable especially in the medical field. |
| Approach: | They propose a medical LLM through decoupling Clinical Alignment and Knowledge Aggregation which uses a and a to encode diverse knowledge in the first stage and filter out detrimental information. |
| Outcome: | The proposed model achieves promising performance on over 20 medical tasks and specific medical alignment tasks. |
Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language Models (2025.tacl-1)
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| Challenge: | a recent study revisits six core challenges that have influenced the evolution of Neural Machine Translation (NMT) domain mismatch, amount of parallel data, rare word prediction, translation of long sentences and sub-optimal beam search remain challenges in LLMs. |
| Approach: | They revisit core challenges that have acted as benchmarks for progress in NMT . they propose to revisit these challenges and offer insights into their relevance . |
| Outcome: | The proposed models significantly improve translation of sentences containing approximately 80 words, even translating documents up to 512 words. |
Cautious Next Token Prediction (2025.findings-acl)
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Yizhou Wang, Lingzhi Zhang, Yue Bai, Mang Tik Chiu, Zhengmian Hu, Mingyuan Zhang, Qihua Dong, Yu Yin, Sohrab Amirghodsi, Yun Fu
| Challenge: | Existing methods for decoding autoregressive models are temperature scaling and nucleus sampling to balance diversity and coherence. |
| Approach: | They propose a training-free decoding strategy that uses a model with a low perplexity score to select the trial with the lowest perplexities as the most probable and reliable path. |
| Outcome: | The proposed approach outperforms existing standard decoding strategies consistently by a clear margin. |
ADaPT: Token-Level Decoupling for Efficient Large Reasoning Models (2026.findings-acl)
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Tingyun li, Zishang Jiang, Jinyi Han, Xinyi Wang, Sihang Jiang, Han Xia, Zhaoqian Dai, Ma Shuguang, Fei Yu, Jiaqing Liang, Yanghua Xiao
| Challenge: | Existing efficiency-oriented methods attempt to shorten or mix reasoning strategies, yet often degrade reasoning capability. |
| Approach: | They propose a token-level dual-process framework that explicitly decouples efficiency and correctness signals during training. |
| Outcome: | The proposed framework reduces inference cost while maintaining strong reasoning ability across multiple benchmarks. |
A New Concept of Deep Reinforcement Learning based Augmented General Tagging System (C18-1)
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| Challenge: | Existing systems for general sequence tagging/labeling are based on neural network architectures. |
| Approach: | They propose a deep neural network based sequence labeling model and a augmented tagger to improve system performance by modeling the data with minority tags. |
| Outcome: | The proposed system outperforms the current state-of-the-art model on ATIS and CoNLL-2003 datasets by 1.9% and 1.4%. |
MdEval: Massively Multilingual Code Debugging (2026.findings-acl)
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Shukai Liu, Linzheng Chai, Jian Yang, Jiajun Shi, He Zhu, Liran Wang, Jin Ke, Wei Zhang, Hualei Zhu, Shuyue Guo, Tao Sun, Jiaheng Liu, Yunlong Duan, Yu Hao, Liqun Yang, Guanglin Niu, Ge Zhang, Zhoujun Li
| Challenge: | Existing benchmarks primarily focus on Python and are limited in terms of language diversity. |
| Approach: | They propose a multilingual debugging benchmark that includes 3.9K test samples of 20 programming languages and introduces the debug instruction corpora MdEval-Instruct by injecting bugs into the correct multilingual queries and solutions. |
| Outcome: | The proposed benchmark includes 3.9K test samples of 20 programming languages and covers the automated program repair task, bug localization task, and bug identification task. |
SLoRA: Balancing Plasticity and Forgetting in Large Language Models for Continual Learning (2026.acl-long)
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| Challenge: | Large language models (LLMs) have achieved remarkable success across diverse tasks through large-scale pretraining. |
| Approach: | They propose a framework that filters noisy components from LoRA updates via subspace similarity with the base model. |
| Outcome: | The proposed framework improves accuracy by 12%, reduces forgetting by 29%, and filters out over 30% of LoRA parameters identified as noisy. |
Using Subtext to Enhance Generative IDRR (2025.acl-short)
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| Challenge: | Arguments contain subtexts, but they are connotative and need prompts to be recognized . a lightweight subtext generator is helpful when the prompt doesn't raise a complex CoT. |
| Approach: | They leverage LLaMA to generate subtexts for argument pairs and verify their effectiveness . they construct a baseline IDRR using the decoder-only backbone LLama . |
| Outcome: | The proposed approach achieves higher F1 scores on two benchmarks than previous models. |
RFiD: Towards Rational Fusion-in-Decoder for Open-Domain Question Answering (2023.findings-acl)
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| Challenge: | Open-domain Question Answering (ODQA) systems rely on spurious features instead of genuine causal relationships to generate answers. |
| Approach: | They propose a model that leverages the encoders of FiD to distinguish between causal relationships and spurious features and guides the decoder to generate answers informed by this discernment. |
| Outcome: | The proposed model improves on two ODQA datasets and shows that it can identify causal relationships and identify spurious features. |
How Much Does Nonverbal Communication Conform to Entropy Rate Constancy?: A Case Study on Listener Gaze in Interaction (2024.findings-acl)
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| Challenge: | Whether the Entropy Rate Constancy principle applies to nonverbal communication signals is still under investigation. |
| Approach: | They perform empirical analyses of video-recorded dialogue data and investigate whether listener gaze adheres to the Entropy Rate Constancy principle. |
| Outcome: | The results show that the ERC principle holds for listener gaze, and that linguistic factors syntactic complexity and turn transition potential are weakly correlated with local entropy of listener gaze. |
FedMABench: Benchmarking Mobile GUI Agents on Decentralized Heterogeneous User Data (2025.emnlp-main)
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| Challenge: | Mobile GUI agents have attracted tremendous research participation recently. traditional approaches to mobile agent training rely on centralized data collection. |
| Approach: | They propose a benchmark for federated training and evaluation of mobile GUI agents . they find that federation algorithms consistently outperform local training . |
| Outcome: | The first benchmark for federated training and evaluation of mobile GUI agents is released . it features 6 datasets with 30+ subsets, 8 federation algorithms, 10+ base models, and over 800 apps across 5 categories . |
NetSafe: Exploring the Topological Safety of Multi-agent System (2025.findings-acl)
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Miao Yu, Shilong Wang, Guibin Zhang, Junyuan Mao, Chenlong Yin, Qijiong Liu, Kun Wang, Qingsong Wen, Yang Wang
| Challenge: | Large language models (LLMs) have fueled significant progress in intelligent Multi-agent Systems (MAS), with expanding academic and industrial applications. |
| Approach: | They propose a framework that unifies diverse MAS workflows via iterative RelCom interactions to enable generalized analysis. |
| Outcome: | The proposed framework unifies diverse MAS workflows via iterative RelCom interactions to enable generalized analysis. |
On the (In-)Security of the Shuffling Defense in the Transformer Secure Inference (2026.acl-long)
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| Challenge: | Existing work reveals only randomly permuted activations to the client, allowing adversaries to extract model weights. |
| Approach: | They propose an attack that aligns differently shuffled activations to a common permutation and exploits them to extract model weights. |
| Outcome: | The proposed attack can align shuffled activations to a common permutation and exploit them to extract model weights with a query cost of approximately $1. |
GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement (2025.acl-long)
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Yifan Yang, Zheshu Song, Jianheng Zhuo, Mingyu Cui, Jinpeng Li, Bo Yang, Yexing Du, Ziyang Ma, Xunying Liu, Ziyuan Wang, Ke Li, Shuai Fan, Kai Yu, Wei-Qiang Zhang, Guoguo Chen, Xie Chen
| Challenge: | GigaSpeech 2 is a large-scale, multi-domain, multilingual speech recognition corpus for low-resource languages. |
| Approach: | They propose a large-scale, multi-domain, multilingual speech recognition corpus for low-resource languages and an automated pipeline for data crawling, transcription, and label refinement. |
| Outcome: | The proposed corpus reduces the word error rate for Thai, Indonesian, and Vietnamese on a realistic YouTube test set by 25% to 40% compared to Whisper large-v3. |
OmniFlatten: An End-to-end GPT Model for Seamless Voice Conversation (2025.acl-long)
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Qinglin Zhang, Luyao Cheng, Chong Deng, Qian Chen, Wen Wang, Siqi Zheng, Jiaqing Liu, Hai Yu, Chao-Hong Tan, Zhihao Du, ShiLiang Zhang
| Challenge: | Full-duplex spoken dialogue systems allow simultaneous bidirectional communication . low latency and natural interactions in full-duplice systems remains a challenge . |
| Approach: | They propose a multi-stage post-training scheme that adapts a text large language model into a speech-text dialogue LLM. |
| Outcome: | The proposed model can model human conversation behaviors with low latency and natural interactions with low delay. |
DEMO: Reframing Dialogue Interaction with Fine-grained Element Modeling (2025.findings-acl)
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| Challenge: | Large language models (LLMs) enabled dialogue systems are one of the central modes in human-machine interaction. |
| Approach: | They propose a benchmark task for dialogue element MOdeling and Element Awareness and a new benchmark for dialogue agent interaction that allows the agent to model dialogue elements via imitation learning. |
| Outcome: | The proposed agent performs well in both dialogue element modeling and out-of-domain tasks. |
HopWeaver: Cross-Document Synthesis of High-Quality and Authentic Multi-Hop Questions (2026.acl-long)
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| Challenge: | Multi-Hop Question Answering (MHQA) is a critical benchmark for evaluating the model’s ability to integrate information from diverse sources. |
| Approach: | They propose a framework that synthesizes authentic multi-hop questions without manual annotation without the need for manual guidance. |
| Outcome: | The proposed framework synthesizes bridge and comparison questions without human intervention and achieves comparable or superior quality to human-annotated datasets at a lower cost. |
SportsMetrics: Blending Text and Numerical Data to Understand Information Fusion in LLMs (2024.acl-long)
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| Challenge: | Large language models can handle text and data, but blending text and numerical data presents significant challenges. |
| Approach: | They propose four tasks to evaluate the numerical reasoning and information fusion capabilities of large language models in sports data analytics. |
| Outcome: | The proposed tasks evaluate the numerical reasoning and information fusion capabilities of large language models in sports data analytics. |
Document-Level Machine Translation with Large Language Models (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) such as ChatGPT can produce coherent, cohesive, relevant, and fluent answers for various natural language processing tasks. |
| Approach: | They examine the impact of different prompts on document-level translation quality and discourse phenomena using figures and lines, which are invisible to GPT-4. |
| Outcome: | The proposed models outperform commercial MT systems and advanced document-level MT methods on a number of benchmarks and show potential to become a new paradigm for document- level translation. |
Natural-Language Policies to Executable Decisions: An Interpretable Large Language Model Framework (2026.acl-industry)
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Ziqiang Zhang, Jing Ma, Zilong Wang, Jiayuan Chen, Yi Qiao, Yu He, Wei Zhang, Dai Cheng, Xiaoyu Shen
| Challenge: | a production-grade pricing system for tourism is challenging due to unstructured nature of travel orders and ever-evolving pricing policies. |
| Approach: | They propose a production-grade pricing system with a strict decision boundary . they propose to combine structured extraction and bounded policy/path selection with interpretable condition trees . |
| Outcome: | The proposed system processed 3,960 orders in six months and reduced the order management team from 15-20 to 3 . the system reduced the per-order handling time from 10 minutes to 2 minutes. |
Zemi: Learning Zero-Shot Semi-Parametric Language Models from Multiple Tasks (2023.findings-acl)
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| Challenge: | Existing semi-parametric language models lack the capacity to perform zero-shot tasks . large language models have shown impressive zero-shoot ability, but huge model size costs . semi-parametric language model can be used to augment a smaller language model with retrieved background knowledge . |
| Approach: | They propose a semi-parametric language model for zero-shot task generalization that augments a smaller language model with retrieved related background knowledge. |
| Outcome: | The proposed model outperforms T0-3B by 16% across seven diverse evaluation tasks while being 3.8x smaller in scale. |
DimABSA: Building Multilingual and Multidomain Datasets for Dimensional Aspect-Based Sentiment Analysis (2026.acl-long)
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Lung-Hao Lee, Liang-Chih Yu, Natalia V Loukachevitch, Ilseyar Alimova, Alexander Panchenko, Tzu-Mi Lin, Zhe-Yu Xu, Jian-Yu Zhou, Guangmin Zheng, Jin Wang, Sharanya Awasthi, Jonas Becker, Jan Philip Wahle, Terry Ruas, Shamsuddeen Hassan Muhammad, Saif M. Mohammad
| Challenge: | Existing ABSA research relies on coarse-grained categorical labels, which limits its ability to capture nuanced affective states. |
| Approach: | They propose a dimensional approach that represents sentiment with continuous valence–arousal (VA) scores, enabling fine-grained analysis at both the aspect and sentiment levels. |
| Outcome: | The proposed approach represents sentiment with continuous valence–arousal (VA) scores, enabling fine-grained analysis at both the aspect and sentiment levels. |
Structural Information Preserving for Graph-to-Text Generation (2020.acl-main)
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| Challenge: | Existing models that mess up or drop the core structural information of input graphs are lacking in graph-to-text generation. |
| Approach: | They propose to leverage richer training signals to guide a graph-to-text generation model by focusing on autoencoding losses and back-propagating the losses to better calibrate the model. |
| Outcome: | Experiments on two benchmarks show the proposed model over a state-of-the-art model . two types of autoencoding losses are used to back-propagate the model based on multitask training . |
BSCodec: A Band-Split Neural Codec for High-Quality Universal Audio Reconstruction (2026.findings-eacl)
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| Challenge: | Neural audio codecs have enabled high-fidelity reconstruction of speech, music and sound . however, speech-optimized codec systems suffer degradation on music or sound if they ignore spectral differences . |
| Approach: | They propose a neural audio codec that splits the spectral dimension into separate bands and compresses each band independently. |
| Outcome: | Experimental results show that BSCodec achieves better reconstruction quality on music and sound compared to existing codecs. |
How Large a Vocabulary Does Text Classification Need? A Variational Approach to Vocabulary Selection (N19-1)
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| Challenge: | Using a pre-defined vocabulary is a common approach to selecting text inputs . however, using a large vocabulary is not economical, as it limits the model's applicability on computation-or memoryconstrained scenarios. |
| Approach: | They propose a more sophisticated variational vocabulary dropout to perform vocabulary selection . they propose two new metrics to measure area under accuracy-vocab curve and Vocab Size under X% accuracy drop . |
| Outcome: | The proposed framework outperforms the baselines on the vocabulary selection problem on multiple NLP classification tasks. |
Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks (D18-1)
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| Challenge: | Existing approaches to align multilingual knowledge graphs with counterparts in different languages are not effective. |
| Approach: | They propose a novel approach for cross-lingual KG alignment via graph convolutional networks . they train GCNs to embed entities of each language into a unified vector space . |
| Outcome: | The proposed approach gets the best performance on real multilingual KGs compared with other embedding-based approaches. |
ODD: A Benchmark Dataset for the Natural Language Processing Based Opioid Related Aberrant Behavior Detection (2024.naacl-long)
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Sunjae Kwon, Xun Wang, Weisong Liu, Emily Druhl, Minhee Sung, Joel Reisman, Wenjun Li, Robert Kerns, William Becker, Hong Yu
| Challenge: | Opioid related aberrant behaviors (ORABs) present novel risk factors for opioid overdose. |
| Approach: | They propose to use a biomedical natural language processing benchmark dataset to classify ORABs from patients’ EHR notes into nine categories: confirmed aberrant behavior, suggested aberrant behaviors, Opioids, indication, diagnosed opioid dependency, Benzodiazepines, medication changes, and Central Nervous System-related. |
| Outcome: | The proposed dataset outperforms two state-of-the-art models in most categories and the gains are especially higher among uncommon classes. |
COVID-19 Claim Radar: A Structured Claim Extraction and Tracking System (2022.acl-demo)
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Manling Li, Revanth Gangi Reddy, Ziqi Wang, Yi-shyuan Chiang, Tuan Lai, Pengfei Yu, Zixuan Zhang, Heng Ji
| Challenge: | a new system extracts supporting and refuting claims from COVID-19 related news . the system is publicly available at GitHub and DockerHub, with complete documentation. |
| Approach: | They propose a COVID-19 Claim Radar system that extracts supporting and refuting claims . the system leverages Wikidata as the hub to consolidate coreferential knowledge elements . |
| Outcome: | The system extracts supporting and refuting claims from COVID-19 pandemic information . it leverages Wikidata as the hub to merge coreferential knowledge elements . |
ThinkNote: Enhancing Knowledge Integration and Utilization of Large Language Models via Constructivist Cognition Modeling (2026.findings-eacl)
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Zhipeng Xu, Zhenghao Liu, Yukun Yan, Shuo Wang, Shi Yu, Zheni Zeng, Chaojun Xiao, Zhiyuan Liu, Ge Yu, Chenyan Xiong
| Challenge: | Large Language Models (LLMs) exhibit suboptimal behaviors and inconsistencies when exposed to unfamiliar external information, underscoring their limitations in effectively leveraging such knowledge. |
| Approach: | They propose a framework that enhances the external knowledge utilization of Large Language Models through a two-stage constructivist cognitive modeling process. |
| Outcome: | The proposed framework achieves a 10% improvement over baseline methods on various question-answering benchmarks. |
Measuring Correlation-to-Causation Exaggeration in Press Releases (2020.coling-main)
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| Challenge: | Recent studies have found that press releases are a major source of exaggeration in science communication, which is later spread to mainstream media. |
| Approach: | They propose an NLP approach to identify exaggerated causal claims in health press releases that report on observational studies. |
| Outcome: | The proposed approach can identify causal claims in press releases that report on observational studies. |
MOCHA: Are Code Language Models Robust Against Multi-Turn Malicious Coding Prompts? (2025.findings-emnlp)
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Muntasir Wahed, Xiaona Zhou, Kiet A. Nguyen, Tianjiao Yu, Nirav Diwan, Gang Wang, Dilek Hakkani-Tür, Ismini Lourentzou
| Challenge: | Recent advances in Large Language Models have significantly enhanced their code generation capabilities, but their robustness against adversarial misuse remains underexplored. |
| Approach: | They introduce a code decomposition attack where a malicious coding task is broken down into subtasks across multiple conversational turns to evade safety filters. |
| Outcome: | The proposed code decomposition attacks exploits multi-turn malicious coding prompts . the proposed model improves rejection rates while preserving coding ability . |
An Intra-Class Relation Guided Approach for Code Comment Generation (2023.findings-eacl)
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| Challenge: | Recent work in code comment generation assumes that all information required to generate comments is encoded in the target function itself, yet in most realistic situations, it is hard to understand a function in isolation from the surrounding context. |
| Approach: | They propose a graph-based learning framework to capture various relations among functions in a class file. |
| Outcome: | The proposed method outperforms baseline models on automatic and human evaluation metrics on a Java dataset collected from real-world projects. |
The Essence of Contextual Understanding in Theory of Mind: A Study on Question Answering with Story Characters (2025.acl-long)
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Chulun Zhou, Qiujing Wang, Mo Yu, Xiaoqian Yue, Rui Lu, Jiangnan Li, Yifan Zhou, Shunchi Zhang, Jie Zhou, Wai Lam
| Challenge: | Theory-of-Mind (ToM) is a psychological capability that allows humans to understand and interpret the mental states of others. |
| Approach: | They propose a CharToM-QA benchmark to assess the importance of comprehensive contextual understanding about personal backgrounds in ToM. |
| Outcome: | The proposed model outperforms existing models on 1,035 ToM questions based on classic novels and shows that educated participants perform better when they have read the novels than non-educated participants. |
Generating Deep Questions with Commonsense Reasoning Ability from the Text by Disentangled Adversarial Inference (2023.findings-acl)
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| Challenge: | Existing methods for commonsense question generation produce shallow questions that can be answered by simple word matching. |
| Approach: | They propose a task of commonsense question generation that aims to yield deep-level questions from the text. |
| Outcome: | The proposed model can yield deep-level and to-the-point questions from the text. |
Dual-Encoder Transformers with Cross-modal Alignment for Multimodal Aspect-based Sentiment Analysis (2022.aacl-main)
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| Challenge: | Multimodal aspect-based sentiment analysis (MABSA) aims to extract aspect terms from text and image pairs, and then analyze their corresponding sentiment. |
| Approach: | They propose a dual-encoder transformer with cross-modal alignment to extract aspect terms from text and image pairs and then analyze their corresponding sentiments. |
| Outcome: | The proposed approach outperforms existing methods on two benchmarks. |
MAViS: A Multi-Agent Framework for Long-Sequence Video Storytelling (2026.eacl-long)
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| Challenge: | Experimental results show that MAViS achieves state-of-the-art performance in assistive capability, visual quality, and video expressiveness. |
| Approach: | They propose an end-to-end multi-agent collaborative framework for long-sequence video storytelling that orchestrates specialized agents across multiple stages. |
| Outcome: | The proposed framework achieves state-of-the-art performance in assistive capability, visual quality, and video expressiveness. |
ARL2: Aligning Retrievers with Black-box Large Language Models via Self-guided Adaptive Relevance Labeling (2024.acl-long)
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| Challenge: | Existing retrievers are misaligned with large language models due to separate training processes and inherent black-box nature of LLMs. |
| Approach: | They propose a retriever learning technique that harnesses LLMs as labelers to annotate and score adaptive relevance evidence. |
| Outcome: | Extensive experiments show that ARL2 improves accuracy and reduces the cost of API calls. |
Improving Semantic Matching through Dependency-Enhanced Pre-trained Model with Adaptive Fusion (2022.findings-emnlp)
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| Challenge: | Existing work on dependency prior structure integration into pre-trained models is still unclear. |
| Approach: | They propose a dependency-based fusion attention paradigm which explicitly introduces dependency prior structure into pre-trained models and adaptively fuses it with semantic information. |
| Outcome: | The proposed model achieves state-of-the-art or competitive performance on 10 public datasets, demonstrating the benefits of adaptively fusing dependency structure in semantic matching task. |
MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark (2025.acl-long)
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Xiang Yue, Tianyu Zheng, Yuansheng Ni, Yubo Wang, Kai Zhang, Shengbang Tong, Yuxuan Sun, Botao Yu, Ge Zhang, Huan Sun, Yu Su, Wenhu Chen, Graham Neubig
| Challenge: | Recent advances in multimodal large language models have led to progress in tackling complex reasoning tasks that combine textual and visual information. |
| Approach: | They introduce a robust version of the Massive Multi-discipline Multimodal Understanding and Reasoning (MMMU) benchmark. |
| Outcome: | The proposed model performs lower on MMMU-Pro than on the previous benchmark, ranging from 16.8% to 26.9%. |
Don’t Get Lost in the Trees: Streamlining LLM Reasoning by Overcoming Tree Search Exploration Pitfalls (2025.acl-long)
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Ante Wang, Linfeng Song, Ye Tian, Dian Yu, Haitao Mi, Xiangyu Duan, Zhaopeng Tu, Jinsong Su, Dong Yu
| Challenge: | Recent advances in tree search algorithms guided by verifiers have significantly enhanced the reasoning capabilities of large language models (LLMs), but at the cost of increased computational resources. |
| Approach: | They propose an e ffici ent tree sear ch framework that is a plug-and-play system compatible with various tree search algorithms. |
| Outcome: | The proposed framework reduces computational costs and prioritizes resource allocation to harder tasks (Levels 3-4) over simpler ones (Level 1-2), addressing both over-exploration in basic problems and under-exploation in complex cases. |
PlanE: Meta Planning of Data, Tuning, and Inference for Extractive-based LLMs (2026.findings-acl)
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| Challenge: | Existing methods for optimizing LLMs for task-specific tasks are limited due to the sheer volume of data. |
| Approach: | They propose a Planning framework for constructing Extractive-based LLMs called PlanE . they propose 'data decomposition', instruction tuning, prompt inference and a 'Data-Tuning-Inference' planner . |
| Outcome: | The proposed framework improves performance across different datasets and on different dataset. |
Conditional Language Policy: A General Framework For Steerable Multi-Objective Finetuning (2024.findings-emnlp)
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Kaiwen Wang, Rahul Kidambi, Ryan Sullivan, Alekh Agarwal, Christoph Dann, Andrea Michi, Marco Gelmi, Yunxuan Li, Raghav Gupta, Kumar Dubey, Alexandre Rame, Johan Ferret, Geoffrey Cideron, Le Hou, Hongkun Yu, Amr Ahmed, Aranyak Mehta, Leonard Hussenot, Olivier Bachem, Edouard Leurent
| Challenge: | Existing approaches for multi-objective Reinforcement Learning (RL) are difficult due to plurality of preferences and applications. |
| Approach: | They propose a framework for finetuning language models on multiple objectives using conditional language policy. |
| Outcome: | The proposed framework outperforms and Pareto-dominates existing approaches for multi-objective Reinforcement Learning (RL) it does not require training or maintaining multiple models to achieve different trade-offs between the objectives. |
MixPAVE: Mix-Prompt Tuning for Few-shot Product Attribute Value Extraction (2023.findings-acl)
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Li Yang, Qifan Wang, Jingang Wang, Xiaojun Quan, Fuli Feng, Yu Chen, Madian Khabsa, Sinong Wang, Zenglin Xu, Dongfang Liu
| Challenge: | Existing methods for product attribute value extraction focus on extracting values for a set of known attributes with sufficient training data. |
| Approach: | They propose a prompt tuning approach to extract attributes from product information using mixed prompts. |
| Outcome: | The proposed approach improves on two product benchmarks and shows parameter-efficient training and avoids model overfitting. |
ARCHITECT: Uncertainty-Aware Dynamic Tool Learning via Causal Intervention for Open-World Agents (2026.acl-long)
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| Challenge: | Existing methods treat all generated tools as equally trustworthy, a "blind trust" assumption that is untenable for reliable agent deployment. |
| Approach: | They propose a framework that moves beyond black-box reliability prediction to interpretable failure attribution. |
| Outcome: | The proposed framework achieves state-of-the-art on four benchmarks including StableToolBench, MINT, T-Eval, and SWE-bench Lite. |
Unveiling Factual Recall Behaviors of Large Language Models through Knowledge Neurons (2024.emnlp-main)
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| Challenge: | Recent advances in Large Language Models have underscored their exceptional reasoning prowess with natural language understanding across a broad spectrum of tasks. |
| Approach: | They examine whether Large Language Models actively recall or retrieve their internal repositories of factual knowledge when faced with reasoning tasks. |
| Outcome: | The proposed model improves reasoning performance while suppressing it leads to notable degradation. |
Recent Advances in Speech Language Models: A Survey (2025.acl-long)
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Wenqian Cui, Dianzhi Yu, Xiaoqi Jiao, Ziqiao Meng, Guangyan Zhang, Qichao Wang, Steven Y. Guo, Irwin King
| Challenge: | Text-based Large Language Models (LLMs) are a promising solution for end-to-end speech interaction. |
| Approach: | They propose to build a framework that allows users to input text and translate it into speech . they propose to use a text-only LLM and a "textto-speech" framework to generate a response based on this transcription . |
| Outcome: | The survey offers an overview of recent approaches to building SpeechLMs . it outlines core architectural components, training methodologies, evaluation strategies and challenges . |
NoteChat: A Dataset of Synthetic Patient-Physician Conversations Conditioned on Clinical Notes (2024.findings-acl)
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| Challenge: | NoteChat is a cooperative multi-agent framework for generating patient-physician dialogues . evaluator finds it outperforms state-of-the-art models for generating clinical notes . clinical documentation is largely done by physicians at both steps . |
| Approach: | They propose a cooperative multi-agent framework leveraging Large Language Models to generate patient-physician dialogues. |
| Outcome: | The proposed framework outperforms state-of-the-art models for generating clinical notes . it can engage patients directly and help clinical documentation, a leading cause of physician burnout . |
Re-TASK: Revisiting LLM Tasks from Capability, Skill, and Knowledge Perspectives (2025.findings-acl)
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Zhihu Wang, Shiwan Zhao, Yu Wang, Heyuan Huang, Sitao Xie, Yubo Zhang, Jiaxin Shi, Zhixing Wang, Hongyan Li, Junchi Yan
| Challenge: | Existing approaches to solving complex tasks with large language models (LLMs) fail to decompose tasks accurately or execute subtasks effectively. |
| Approach: | They propose a Chain-of-Learning (CoL) paradigm that highlights task dependencies on specific capability items, further broken down into their constituent knowledge and skill components. |
| Outcome: | The proposed model improves Yi-1.5-9B and Llama3-Chinese-8B for legal tasks by 45.00% and 24.50% on different domains. |
TUNA: Comprehensive Fine-grained Temporal Understanding Evaluation on Dense Dynamic Videos (2025.acl-long)
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Fanheng Kong, Jingyuan Zhang, Hongzhi Zhang, Shi Feng, Daling Wang, Linhao Yu, Xingguang Ji, Yu Tian, V. W., Fuzheng Zhang
| Challenge: | Existing benchmarks for video understanding often focus on specific aspects, overlooking the holistic nature of video content. |
| Approach: | They propose a temporal-oriented benchmark for fine-grained understanding on dense dynamic videos with two complementary tasks: captioning and QA. |
| Outcome: | The proposed model performs well on diverse video scenarios and dynamic videos, with interpretable and robust evaluation criteria. |
Graph Attention Network with Memory Fusion for Aspect-level Sentiment Analysis (2020.aacl-main)
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| Challenge: | Recent studies ignored the syntactic relationship between the aspect and its corresponding context words, leading the model to focus on syntaktically unrelated words mistakenly. |
| Approach: | They propose to extend the graph convolutional network by assigning different weights to edges of connected words. |
| Outcome: | The proposed method can improve on five datasets showing that it learns and exploits multiword relations and draws different weights of words to improve performance. |
Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task (D18-1)
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Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, Zilin Zhang, Dragomir Radev
| Challenge: | Existing datasets for semantic parsing are too small in terms of number of programs for training modern data-intensive models. |
| Approach: | They propose a large-scale complex and cross-domain semantic parsing task for a database . they use a dataset with 10,181 questions and 5,693 unique complex SQL queries . |
| Outcome: | The proposed task is different from previous tasks because it uses the same database and program . the best model achieves only 9.7% exact matching accuracy on a database split setting. |
RethinkMCTS: Refining Erroneous Thoughts in Monte Carlo Tree Search for Code Generation (2025.emnlp-main)
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Qingyao Li, Wei Xia, Xinyi Dai, Kounianhua Du, Weiwen Liu, Yasheng Wang, Ruiming Tang, Yong Yu, Weinan Zhang
| Challenge: | Existing tree search methods neglect the underlying reasoning process, resulting in poor search quality. |
| Approach: | They propose a framework that systematically explores and refines the reasoning process for code generation by using a tree search engine and a reflection mechanism. |
| Outcome: | The proposed framework outperforms existing methods in the code generation domain. |
CITE: Benchmarking Heterogeneous Text-Attributed Graph Models (2026.acl-long)
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| Challenge: | Recent advances in large language models and text-aware graph learning have increased interest in reasoning over text-attributed graphs. |
| Approach: | They propose a large-scale heterogeneous text-attributed graph benchmark for catalytic materials that contains over 438K nodes and 1.2M edges . they establish standardized evaluation protocols for node classification and link prediction and conduct ablation studies to assess the impact of graph heterogenity and textual attributes. |
| Outcome: | The proposed benchmarks are compared to existing methods and provide a baseline for the evaluation of four classes of learning paradigms. |
Sparse Brains are Also Adaptive Brains: Cognitive-Load-Aware Dynamic Activation for LLMs (2026.findings-eacl)
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| Challenge: | Existing sparsity methods lack adaptivity to contextual or model structural demands or incur prohibitive computational overhead. |
| Approach: | They propose a Cognitive-Load-Aware Dynamic Activation framework that synergizes statistical sparsity with semantic adaptability. |
| Outcome: | The proposed framework achieves 20% average speedup with less than 2% accuracy degradation outperforming Griffin and TT. |
VReST: Enhancing Reasoning in Large Vision-Language Models through Tree Search and Self-Reward Mechanism (2025.acl-long)
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| Challenge: | Large Vision-Language Models (LVLMs) have shown exceptional performance in multimodal tasks, but their effectiveness in complex visual reasoning is constrained. |
| Approach: | They propose a training-free approach that enhances Reasoning in Large Vision-Language Models . they propose integrating Monte Carlo Tree Search and Self-Reward mechanisms into the reasoning tree . |
| Outcome: | The proposed approach surpasses current prompting methods and secures state-of-the-art performance across three multimodal reasoning benchmarks. |
Autoregressive Pre-Training on Pixels and Texts (2024.emnlp-main)
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| Challenge: | pixel-based language modeling integrates visual and textual data to improve performance of language models. |
| Approach: | They propose a method that integrates visual and textual data into an autoregressive framework. |
| Outcome: | The proposed method improves performance of pixel-based language models by incorporating visual and textual data. |
MEXA: Towards General Multimodal Reasoning with Dynamic Multi-Expert Aggregation (2025.findings-emnlp)
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| Challenge: | MEXA is a training-free framework that performs modality- and task-aware aggregation of multiple expert models to enable effective multimodal reasoning across diverse domains. |
| Approach: | MEXA is a training-free framework that performs modality- and task-aware aggregation of multiple expert models. |
| Outcome: | MEXA performs modality- and task-aware aggregation of multiple expert models . it generates interpretable textual reasoning outputs and reasons over them using a Large Reasoning Model (LRM) MEX A consistently delivers performance improvements over strong multimodal benchmarks . |
Logic Rules as Explanations for Legal Case Retrieval (2024.lrec-main)
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| Challenge: | Recent efforts to learn explainable legal case retrieval models fail to provide faithful and interpretable explanations for legal cases. |
| Approach: | They propose a framework that uses logic rules to explain legal case retrieval results . they extend benchmarks of LeCaRD and ELAM with manually annotated logic rules . |
| Outcome: | The proposed framework is able to provide faithful explanations for legal case retrieval. |
Instruction Tuning with Retrieval-based Examples Ranking for Aspect-based Sentiment Analysis (2024.findings-acl)
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| Challenge: | Aspect-based sentiment analysis (ABSA) identifies sentiment information related to specific aspects . previous studies have proposed using fixed examples for instruction tuning . |
| Approach: | They propose an instruction learning method with retrieval-based example ranking for ABSA tasks. |
| Outcome: | The proposed method is superior to existing models on three ABSA subtasks. |
DSQG-Syn: Synthesizing High-quality Data for Text-to-SQL Parsing by Domain Specific Question Generation (2025.findings-naacl)
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Shaoming Duan, Youxuan Wu, Chuanyi Liu, Yuhao Zhang, Zirui Wang, Peiyi Han, Shengyuan Yu, Liang Yan, Yingwei Liang
| Challenge: | Existing methods for generating SQL queries using natural language questions produce inconsistent NLQ-SQL pairs. |
| Approach: | They propose a text-to-SQL data synthesis framework that generates domain-relevant questions . they synthesize NLQ-SqL pairs that are domain-specific and intent-consistent . |
| Outcome: | The proposed method outperforms closed-source LLMs on the Text-to-SQL task. |
OS-Symphony: A Holistic Framework for Robust and Generalist Computer-Using Agents (2026.acl-long)
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Bowen Yang, Kaiming Jin, Zhenyu Wu, Zhaoyang Liu, Qiushi Sun, Zehao Li, JingJing Xie, Zhoumianze Liu, Fangzhi Xu, Kanzhi Cheng, Yian Wang, Qingyun Li, Yu Qiao, Zun Wang, Zichen Ding
| Challenge: | Vision-Language Models (VLMs) lack visual-aware tutorial retrieval and historical visual context curation and pruning. |
| Approach: | They propose a framework that integrates an orchestrator and a Reflection-Memory Agent for robust automation. |
| Outcome: | Experimental results show that OS-Symphony delivers substantial performance gains across model scales. |
PUPPET: Neural-Symbolic Standardized Patients for Mental Health (2026.acl-long)
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Chen Xu, Yu ji, Zhenyu Lv, Yang Yi, Yizhe Yang, Luyao Ji, Chaoyi Chen, Xianyang Wang, Tian Lan, Zhihua Wang, Juan Wang, Xunde Dong, Fuze Tian, Qunxi Dong, Bin Hu
| Challenge: | Existing LLM-based training approaches lack faithful responses to clinical errors and explainable feedback. |
| Approach: | They propose a neural-symbolic virtual standardized patient governed by an OBSERVE-THINK-BEHAVE architecture that embeds LLM reasoning into a symbolic system where experts implant causal associations between intervention logic and patient mental states. |
| Outcome: | The proposed model outperforms baselines in faithfulness and pedagogical value. |
Cross-Thought for Sentence Encoder Pre-training (2020.emnlp-main)
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| Challenge: | Existing models to pretrain sentence encoders with large unlabeled corpus are lacking in linguistic information retrieval. |
| Approach: | They propose a novel approach to pre-training sequence encoder using transformers . they propose to train a Transformer-based sequence encoded over a large set of short sequences based on a set of masked words . |
| Outcome: | The proposed approach outperforms state-of-the-art encoders on hotpotQA by improving intermediate information retrieval performance. |
The Art of Prompting: Event Detection based on Type Specific Prompts (2023.acl-short)
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| Challenge: | Experimental results show that a well-defined and comprehensive description of event types can significantly improve event detection performance when the annotations are limited. |
| Approach: | They propose a unified framework to integrate event type specific prompts for supervised, few-shot and zero-shot event detection. |
| Outcome: | The proposed framework shows up to 22.2% gain over the prior state-of-the-art frameworks. |
DatawiseAgent: A Notebook-Centric LLM Agent Framework for Adaptive and Robust Data Science Automation (2025.emnlp-main)
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| Challenge: | Existing large language model (LLM) agents for data science automation are limited by narrow task scopes, limited generalization across tasks and models, and over-reliance on state-of-the-art (SOTA) LLMs. |
| Approach: | They propose a notebook-centric LLM agent framework for adaptive and robust data science automation. |
| Outcome: | The proposed framework surpasses baselines such as AutoGen and TaskWeaver in performance tests across diverse data science scenarios and models. |
Safety in Large Reasoning Models: A Survey (2025.findings-emnlp)
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Cheng Wang, Yue Liu, Baolong Bi, Duzhen Zhang, Zhong-Zhi Li, Yingwei Ma, Yufei He, Shengju Yu, Xinfeng Li, Junfeng Fang, Jiaheng Zhang, Bryan Hooi
| Challenge: | Large Reasoning Models (LRMs) have a high level of advanced reasoning capabilities, but they are vulnerable and vulnerable. |
| Approach: | This paper presents the first comprehensive survey of Large Reasoning Models . it explores the new safety risks, attacks, and defense strategies specific to LRMs based on reasoning . |
| Outcome: | The proposed study examines the safety and security risks of large reasoning models. |
OPeRA: A Dataset of Observation, Persona, Rationale, and Action for Evaluating LLMs on Human Online Shopping Behavior Simulation (2026.acl-long)
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Ziyi Wang, Yuxuan Lu, Wenbo Li, Amirali Amini, Bo Sun, Yakov Bart, Weimin Lyu, Jiri Gesi, Tian Wang, Jing Huang, Yu Su, Upol Ehsan, Malihe Alikhani, Toby Jia-Jun Li, Lydia Chilton, Dakuo Wang
| Challenge: | evaluating LLMs' ability to mimic real user behavior remains an open challenge due to the lack of high-quality, publicly available datasets that capture both the observable actions and the internal reasoning of an actual user. |
| Approach: | They propose a dataset of Observation, Persona, Rationale, and Action collected from real human participants during online shopping sessions. |
| Outcome: | The proposed dataset is the first to evaluate how well current LLMs can accurately simulate the next web action of a specific user. |
Multi-step Problem Solving Through a Verifier: An Empirical Analysis on Model-induced Process Supervision (2024.findings-emnlp)
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| Challenge: | a method for process supervision has shown significant improvements in multi-step problem solving . despite the advances in process supervision, there are still easily observable mistakes in state-of-the-art LLMs. |
| Approach: | They propose a method for automating data curation by using a trained verifier to evaluate intermediate steps generated by a reasoner. |
| Outcome: | The proposed method improves the performance of PaLM 2 on math and coding tasks. |
Ascending the Infinite Ladder: Benchmarking Spatial Deformation Reasoning in Vision-Language Models (2026.acl-long)
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Jiahuan Zhang, Shunwen Bai, Tianheng Wang, KaiWen Guo, Zijia Song, Hanqing WU, Guozheng Rao, Kai Han, Kaicheng Yu
| Challenge: | Existing benchmarks explore aspects of threedimensional spatial reasoning and visual-language reasoning in dynamic environments, but they are unable to perform well on 3D spatial deformation reasoning. |
| Approach: | They propose to use a ladder competition format to assess the model's spatial deformation reasoning abilities to determine its performance. |
| Outcome: | The proposed framework assesses the performance of Vision-Language Models in spatial deformation reasoning tasks. |
Interactive Classification by Asking Informative Questions (2020.acl-main)
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| Challenge: | Existing methods for intent classification rely on a single user input and do not interact with the user to reduce ambiguity and improve the final prediction. |
| Approach: | They propose a limited form of interaction to natural language intent classification . they add binary or multi-choice questions to the system to ask missing information . |
| Outcome: | The proposed method can be bootstrapped without interaction data and is scalable to two domains. |
Do LLMs Capture Embodied Cognition and Cultural Variation? Cross-Linguistic Evidence from Demonstratives (2026.acl-long)
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| Challenge: | a new study examines whether large language models acquire embodied cognition and cultural conventions from training data . demonstratives are a natural lens for evaluating linguistic phenomena that reflect cultural variation . aaron e. duan and j. nà: "the complexity of the language model is a major challenge for LLMs" |
| Approach: | They introduce demonstratives as a probe for grounded knowledge by analyzing 6,400 responses from 320 native speakers. |
| Outcome: | The proposed model fails to understand proximal–distal contrast and shows no cultural differences . the proposed model is a new probe for evaluating embodied cognition and cultural conventions . |
Jailbreak Large Vision-Language Models Through Multi-Modal Linkage (2025.acl-long)
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| Challenge: | Existing methods to jailbreak large vision-language models fail against cutting-edge models such as GPT-4o, despite having undergone safety alignment training. |
| Approach: | They propose a new framework for jailbreaking large vision-language models that uses an encryption-decryption process to mitigate the over-exposure of harmful information. |
| Outcome: | The proposed framework jailbreaks GPT-4o with 99.40% success rates on SafeBench, 98.81% on MM-SafeBench and 99.07% on HADES-Dataset. |
OASum: Large-Scale Open Domain Aspect-based Summarization (2023.findings-acl)
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| Challenge: | Existing generic summarization methods generate only one summary for all different requests which is not optimal for diverse demands. |
| Approach: | They use crowd-sourced knowledge on Wikipedia to create a large-scale open-domain aspect-based summarization dataset with 1 million different aspects on 2 million Wikipedia pages. |
| Outcome: | The proposed model can generate diverse aspect-based summarizations on Wikipedia with zero/few-shot and fine-tuning on seven downstream datasets. |
On the Dimensionality of Sentence Embeddings (2023.findings-emnlp)
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| Challenge: | Existing work focuses on improving the quality of sentence embeddings, but the exploration of sentence dimension is limited. |
| Approach: | They propose a two-step training method where the encoder and pooler are optimized separately to mitigate the overall performance loss in low-dimension scenarios. |
| Outcome: | The proposed method significantly improves the performance of low-dimensional sentence embeddings on seven STS tasks and seven sentence classification tasks. |
Investigating the Representation of Backchannels and Fillers in Fine-tuned Language Models (2026.acl-long)
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| Challenge: | Backchannels and fillers are important linguistic expressions in dialogue, but often ignored in modern transformer-based language models. |
| Approach: | They use clustering analysis to learn backchannels and fillers in dialogues in English and Japanese and use natural language generation metrics to confirm this. |
| Outcome: | The proposed models can learn representations of backchannels and fillers using three fine-tuning strategies. |
Cross-Modal Coreference Alignment: Enabling Reliable Information Transfer in Omni-LLMs (2026.acl-long)
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| Challenge: | Experiments on 13 Omni-LLMs reveal systematic weaknesses in cross-modal coreference . cross-module coreference is a crucial missing piece for advancing robust omni-modal reasoning. |
| Approach: | They propose a cross-modal coreference problem to evaluate and enhance Omni-LLMs' reasoning capabilities. |
| Outcome: | Experiments on 13 Omni-LLMs show they lack coreference-aware thinking patterns . the CROSSOMNI dataset yields significant performance gains and generalizes well to collaborative reasoning tasks. |
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. |
LoRE: Enhancing Search Relevance with Progressive Chain-of-Thought and Preference Alignment (2026.findings-acl)
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Chenji Lu, Zhuo Chen, Hui Zhao, Zhiyuan Zeng, Gang Zhao, Junjie Ren, null Lihaoran, Songyan Liu, Pengjie Wang, Chuan Yu, Jian Xu, Bo Zheng
| Challenge: | E-commerce search relevance is a critical component of retrieval systems. |
| Approach: | They propose a large-generative model for search relevance that trains reasoning knowledge, multi-modal understanding and rule awareness into three core competencies. |
| Outcome: | The proposed model outperforms GPT-5 in Macro-F1 and achieves 27% online gain. |
ReflecTool: Towards Reflection-Aware Tool-Augmented Clinical Agents (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have shown promising potential in the medical domain, assisting with tasks like clinical note generation and patient communication. |
| Approach: | They propose a framework that excels at utilizing domain-specific tools within two stages. |
| Outcome: | The proposed framework surpasses the pure LLMs with more than 10 points and the well-established agent-based methods with 3 points. |
ForestCast: Open-Ended Event Forecasting with Semantic News Forest (2025.findings-emnlp)
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| Challenge: | Existing approaches and datasets overlook the complex relationships among events . current research lacks comprehensive evaluation methods to evaluate OEEF . |
| Approach: | They propose a prediction pipeline that extracts forecast-relevant events from news data . forestcast organizes news events into a story tree and predicts subsequent events along each path . |
| Outcome: | The proposed pipeline extracts forecast-relevant events from news data and predicts subsequent events along each path. |
CoSafe: Evaluating Large Language Model Safety in Multi-Turn Dialogue Coreference (2024.emnlp-main)
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| Challenge: | Existing studies have not noticed the safety risks of large language models . authors evaluated 1,400 questions in multi-turn dialogue coreference . |
| Approach: | They are the first to evaluate LLM safety in multi-turn dialogue coreference . they created a dataset of 1,400 questions and tested five open-source models . |
| Outcome: | The study shows that model safety decreases in multi-turn dialogue coreference scenarios . the highest success rate was with the LLaMA2-Chat-7b model, while the lowest was with mistral-7B-Instruct model . |
Mnemis: Dual-Route Retrieval on Hierarchical Graphs for Long-Term LLM Memory (2026.acl-long)
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Zihao Tang, Xin Yu, Ziyu Xiao, Zengxuan Wen, Zelin Li, Jiaxi Zhou, Hualei Wang, Haohua Wang, Haizhen Huang, Weiwei Deng, Feng Sun, Qi Zhang
| Challenge: | Existing methods for retrieving historical messages are based on similarity-based mechanisms. |
| Approach: | They propose a system that integrates System-1 similarity search with a complementary System-2 mechanism, termed Global Selection. |
| Outcome: | The proposed framework achieves state-of-the-art on long-term memory benchmarks and 93.9 on LoCoMo and 91.6 on LongMemEval-S. |
Beyond Literal Mapping: Benchmarking and Improving Non-Literal Translation Evaluation (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) have advanced machine translation (MT) a meta-evaluation dataset focused on non-literal translations is lacking . experimental results show the inaccuracies of traditional MT metrics and the limitations of LLM-as-a-Judge. |
| Approach: | They propose a meta-evaluation framework that leverages sub-agents to evaluate machine translation metrics. |
| Outcome: | The proposed framework improves on the knowledge cutoff and score inconsistency problem. |
How do autoregressive transformers solve full addition? (2025.emnlp-main)
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| Challenge: | Large pre-trained language models have demonstrated impressive capabilities, but there is still much to learn about how they operate. |
| Approach: | They investigate the ability of the autoregressive transformer to perform basic addition operations by using causal analysis to find that a few different attention heads in the middle layers control the addition carry . they found that due to the lack of global focus on the sequence within these attention heads, the model struggles to handle long-sequence addition tasks. |
| Outcome: | The model performs basic addition tasks, but it still faces challenges with length generalization. |
Revisiting Commonsense Reasoning in Machine Translation: Training, Evaluation and Challenge (2023.acl-long)
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| Challenge: | CR is the ability to understand and navigate the world using basic knowledge and understanding shared by most people. |
| Approach: | They propose to incorporate pretrained knowledge into NMT models and use them as robust testbeds for investigating CR in NMT. |
| Outcome: | The proposed method improves the training of NMT models with high CR abilities and provides accurate evaluation metrics. |
MoE Adapter for Large Audio Language Models: Sparsity, Disentanglement, and Gradient-Conflict-Free (2026.findings-acl)
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Yishu Lei, Shuwei He, Hu Jing, Dan Zhang, Xianlong Luo, Danxiang Zhu, Shikun Feng, Rui Liu, Jingzhou HE, Yu Sun, Hua Wu, Haifeng Wang
| Challenge: | Existing research on Large Language Models (LLMs) limited to textual input modality . acoustic information is intrinsically heterogeneous, entangling attributes such as speech, music, and environmental context. |
| Approach: | They propose a sparse Mixture-of-Experts architecture to decouple acoustic information by routing audio tokens to specialized experts. |
| Outcome: | The proposed architecture outperforms existing models on audio semantic and paralinguistic tasks while retaining shared experts for global context. |
CodePRM: Execution Feedback-enhanced Process Reward Model for Code Generation (2025.findings-acl)
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| Challenge: | Recent advances in code generation focus on optimizing the thought process, but lack effective process supervision, making it difficult to optimize the thoughts. |
| Approach: | They propose a method that leverages the code execution feedback to build a code PRM by collecting a large dataset of thought traces and then training it to take both the reasoning process and code execution as input. |
| Outcome: | The proposed approach outperforms baselines and strong LLMs in the inference stage. |
DivScene: Towards Open-Vocabulary Object Navigation with Large Vision Language Models in Diverse Scenes (2025.findings-emnlp)
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Zhaowei Wang, Hongming Zhang, Tianqing Fang, Ye Tian, Yue Yang, Kaixin Ma, Xiaoman Pan, Yangqiu Song, Dong Yu
| Challenge: | Large Vision-Language Models (LVLMs) have achieved significant progress in tasks like visual question answering and document understanding. |
| Approach: | They introduce DivScene, a large-scale dataset with 4,614 houses across 81 scene types and 5,707 kinds of target objects. |
| Outcome: | The proposed dataset provides a much greater diversity of target objects and scene types than existing datasets, enabling a comprehensive task evaluation. |
IPS: In-Prompt Process Supervision for Short Video Content Moderation (2026.acl-industry)
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| Challenge: | Multimodal large language models (MLLMs) capture semantics of short video content but fail to account for policy-specific details. |
| Approach: | They propose a framework that integrates In-prompt Process Supervision into MLLMs . they propose sequential reasoning over ancillary questions during fine-tuning . |
| Outcome: | IPS outperforms baseline MLLMs on public and proprietary benchmarks . replacing human-annotated ancillary labels with MLML-generated ones results in performance degradation. |
Utility-Oriented Visual Evidence Selection for Multimodal Retrieval-Augmented Generation (2026.acl-long)
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| Challenge: | Existing methods for multimodal retrieval-augmented generation rely on semantic relevance or surface-level similarity, which are often misaligned with the actual utility of visual evidence for downstream reasoning. |
| Approach: | They propose a latent notion of evidence usefulness and propose 'surrogate-accelerated' framework that efficiently estimates evidence utility using lightweight multimodal models. |
| Outcome: | The proposed framework outperforms state-of-the-art models while achieving substantial reductions in computational cost. |
PURE: Aligning LLM via Pluggable Query Reformulation for Enhanced Helpfulness (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) depend on vast amounts of text data sourced from the Internet for their training. |
| Approach: | They propose a new alignment paradigm that reformulates risky queries into highly relevant yet harmless ones before feeding them into LLMs. |
| Outcome: | The proposed approach eliminates the high costs of training base LLMs and achieves a promising balance of harmlessness and helpfulness. |
Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia (2025.acl-long)
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Samuel Cahyawijaya, Holy Lovenia, Joel Ruben Antony Moniz, Tack Hwa Wong, Mohammad Rifqi Farhansyah, Thant Thiri Maung, Frederikus Hudi, David Anugraha, Muhammad Ravi Shulthan Habibi, Muhammad Reza Qorib, Amit Agarwal, Joseph Marvin Imperial, Hitesh Laxmichand Patel, Vicky Feliren, Bahrul Ilmi Nasution, Manuel Antonio Rufino, Genta Indra Winata, Rian Adam Rajagede, Carlos Rafael Catalan, Mohamed Fazli Mohamed Imam, Priyaranjan Pattnayak, Salsabila Zahirah Pranida, Kevin Pratama, Yeshil Bangera, Adisai Na-Thalang, Patricia Nicole Monderin, Yueqi Song, Christian Simon, Lynnette Hui Xian Ng, Richardy Lobo Sapan, Taki Hasan Rafi, Bin Wang, null Supryadi, Kanyakorn Veerakanjana, Piyalitt Ittichaiwong, Matthew Theodore Roque, Karissa Vincentio, Takdanai Kreangphet, Phakphum Artkaew, Kadek Hendrawan Palgunadi, Yanzhi Yu, Rochana Prih Hastuti, William Nixon, Mithil Bangera, Adrian Xuan Wei Lim, Aye Hninn Khine, Hanif Muhammad Zhafran, Teddy Ferdinan, Audra Aurora Izzani, Ayushman Singh, Evan Evan, Jauza Akbar Krito, Michael Anugraha, Fenal Ashokbhai Ilasariya, Haochen Li, John Amadeo Daniswara, Filbert Aurelian Tjiaranata, Eryawan Presma Yulianrifat, Can Udomcharoenchaikit, Fadil Risdian Ansori, Mahardika Krisna Ihsani, Giang Nguyen, Anab Maulana Barik, Dan John Velasco, Rifo Ahmad Genadi, Saptarshi Saha, Chengwei Wei, Isaiah Edri W. Flores, Kenneth Chen Ko Han, Anjela Gail D. Santos, Wan Shen Lim, Kaung Si Phyo, Tim Santos, Meisyarah Dwiastuti, Jiayun Luo, Jan Christian Blaise Cruz, Ming Shan Hee, Ikhlasul Akmal Hanif, M.Alif Al Hakim, Muhammad Rizky Sya’ban, Kun Kerdthaisong, Lester James Validad Miranda, Fajri Koto, Tirana Noor Fatyanosa, Alham Fikri Aji, Jostin Jerico Rosal, Jun Kevin, Robert Wijaya, Onno P. Kampman, Ruochen Zhang, Börje F. Karlsson, Peerat Limkonchotiwat
| Challenge: | Southeast Asia is underrepresented in vision-language research . SEA-VL is an open-source initiative dedicated to developing culturally relevant datasets for SEA languages. |
| Approach: | They propose to use crowdsourced, automated image crawling and synthetic image generation to develop culturally relevant datasets for SEA languages. |
| Outcome: | The proposed datasets capture SEA cultural nuances and contexts better than existing datasets. |
Self-Generated Critiques Boost Reward Modeling for Language Models (2025.naacl-long)
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Yue Yu, Zhengxing Chen, Aston Zhang, Liang Tan, Chenguang Zhu, Richard Yuanzhe Pang, Yundi Qian, Xuewei Wang, Suchin Gururangan, Chao Zhang, Melanie Kambadur, Dhruv Mahajan, Rui Hou
| Challenge: | Existing reward models produce scalar scores and struggle to incorporate critiques in a natural language format. |
| Approach: | They propose a framework that predicts critiques and rewards using self-generated critiques without extra supervision. |
| Outcome: | The proposed framework improves reward modeling accuracy by 3.7%-7.3% compared to standard reward models and LLM judges. |
MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)
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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie
| Challenge: | Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models. |
| Approach: | They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts. |
| Outcome: | The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings . |
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. |
MLeVLM: Improve Multi-level Progressive Capabilities based on Multimodal Large Language Model for Medical Visual Question Answering (2024.findings-acl)
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Dexuan Xu, Yanyuan Chen, Jieyi Wang, Yue Huang, Hanpin Wang, Zhi Jin, Hongxing Wang, Weihua Yue, Jing He, Hang Li, Yu Huang
| Challenge: | Existing MVQA models ignore multi-level progressive capabilities due to unspecific data and plain architecture. |
| Approach: | They propose a multi-level visual language model for medical visual question answering (MVQA) which covers multi- level questions and answers as well as reasoning processes from visual clues to semantic cognition. |
| Outcome: | The proposed model outperforms existing medical multimodal large language models on a multi-level instruction dataset and a feature alignment module. |
Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization (2026.findings-acl)
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| Challenge: | Existing methods for designing and optimizing multi-agent systems are static and do not learn from experience. |
| Approach: | They propose a framework that enables a multi-agent system to learn to evolve . they use "textual gradients" to pinpoint failures and suggest granular modifications . |
| Outcome: | a new framework enables a multi-agent system to learn to evolve . it learns from historical optimization experiences to improve its performance . |
CasEE: A Joint Learning Framework with Cascade Decoding for Overlapping Event Extraction (2021.findings-acl)
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| Challenge: | Existing methods assume that events appear in sentences without overlaps . overlapping event extraction is a challenging task in natural language understanding . |
| Approach: | They propose a joint learning framework with cascade decoding for overlapping event extraction . they sequentially perform type detection, trigger extraction and argument extraction based on the specific former prediction . |
| Outcome: | The proposed framework improves on a public event extraction benchmark . it sequentially performs type detection, trigger extraction and argument extraction . |
Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Tracking (2020.starsem-1)
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| Challenge: | Existing methods for dialog state tracking are ontology-based and ontologie-free . however, it is not clear enough which slots are better handled by either of the two methods . |
| Approach: | They propose a dual-strategy model that integrates both ontology-based and ontological-free methods. |
| Outcome: | The proposed model outperforms the existing model on noisy and cleaner datasets. |
CTPD: Cross-Modal Temporal Pattern Discovery for Enhanced Multimodal Electronic Health Records Analysis (2025.findings-acl)
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| Challenge: | Existing methods for predicting clinical outcomes have focused on capturing temporal interactions within individual samples and fusing multimodal information, overlooking critical temporal patterns across different patients. |
| Approach: | They propose a cross-modal temporal pattern discovery framework to extract temporal patterns from multimodal EHR data. |
| Outcome: | The proposed framework extracts meaningful cross-modal temporal patterns from multimodal EHR data. |
SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-Augmented Generation (2024.findings-emnlp)
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Minda Hu, Licheng Zong, Hongru Wang, Jingyan Zhou, Jingjing Li, Yichen Gao, Kam-Fai Wong, Yu Li, Irwin King
| Challenge: | Existing retrieval-augmented approaches to large language models face performance limitations due to the lack of publicly available training data. |
| Approach: | They propose a plug-and-play LLM-based retrieval method called Self-Rewarding Tree Search based on Monte Carlo Tree Search and a self-rewarding paradigm to address these limitations. |
| Outcome: | The proposed method improves the performance of the BM25 retriever and surpasses the baseline of self-reflection in both efficiency and scalability. |
Generic Temporal Reasoning with Differential Analysis and Explanation (2023.acl-long)
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| Challenge: | Existing temporal reasoning models drop to random guessing on TODAY, suggesting that they heavily rely on spurious information rather than proper reasoning for temporal predictions. |
| Approach: | They propose a task called TODAY that evaluates whether systems can correctly understand the effect of incremental changes in temporal relation distributions. |
| Outcome: | The proposed task outperforms existing models, including GPT-3.5, on in-domain benchmarks while allowing for more appropriate annotations. |
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. |
One Embedder, Any Task: Instruction-Finetuned Text Embeddings (2023.findings-acl)
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Hongjin Su, Weijia Shi, Jungo Kasai, Yizhong Wang, Yushi Hu, Mari Ostendorf, Wen-tau Yih, Noah A. Smith, Luke Zettlemoyer, Tao Yu
| Challenge: | a new method for embedding text is developed for tasks that require specialized encoders . INSTRUCTOR is a single embedder that can generate text embeddables tailored to different tasks and domains based on instruction finetuning . |
| Approach: | They introduce a new method for computing text embeddings given task instructions . they first annotate instructions for 330 diverse tasks and train INSTRUCTOR on this multitask mixture . |
| Outcome: | The proposed method improves on 70 embedding evaluation tasks with fewer parameters than the previous best model. |
TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale (2026.acl-industry)
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| Challenge: | a 5minute downtime for an incident could result in a loss of 40 million dollars and erosion of user trust. |
| Approach: | They propose a multi-stage event unification engine that synergizes efficient indexing techniques with Large Language Models (LLMs) to make informed decisions on event merging. |
| Outcome: | The proposed system outperforms baseline methods in routing accuracy, clustering quality, and Signal-to-Noise Ratio. |
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling (P19-1)
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Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, Berlin Chen, Benjamin Van Durme, Edouard Grave, Ellie Pavlick, Samuel R. Bowman
| Challenge: | State-of-the-art models in natural language processing (NLP) often incorporate sentence encoder functions which generate a sequence of vectors intended to represent the in-context meaning of each word in an input text. |
| Approach: | They conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks as alternatives and complements to language modeling. |
| Outcome: | The proposed model can be used to train sentences on language modeling tasks. |
How do Language Models Reshape Entity Alignment? A Survey of LM-Driven EA Methods: Advances, Benchmarks, and Future (2025.emnlp-main)
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| Challenge: | Entity alignment (EA) is critical for knowledge graph (KG) integration. |
| Approach: | They propose a taxonomy that categorizes methods in three stages: data preparation, feature embedding, and alignment. |
| Outcome: | The proposed taxonomy categorizes methods in three key stages: data preparation, feature embedding, and alignment. |
MMLF: Multi-query Multi-passage Late Fusion Retrieval (2025.findings-naacl)
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| Challenge: | Existing approaches to query expansion are limited in terms of lexical overlap. |
| Approach: | They propose a query expansion pipeline that generates sub-queries, expands them into pseudo-documents, retrieves them individually and aggregates results using reciprocal rank fusion. |
| Outcome: | The proposed pipeline improves on five BEIR benchmark datasets and achieves a maximum gain of up to 8%. |
EvolveBench: A Comprehensive Benchmark for Assessing Temporal Awareness in LLMs on Evolving Knowledge (2025.acl-long)
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| Challenge: | Existing studies have explored how LLMs perceive time, but they often overlook the critical aspect of knowledge utilization. |
| Approach: | They propose a benchmark that evaluates temporal competence along five key dimensions: Cognition, Awareness, Trustworthiness and reasoning. |
| Outcome: | EvolveBench measures temporal competence along five key dimensions: Cognition, Awareness, Trustworthiness, Understanding and reasoning. |
Synchronous Double-channel Recurrent Network for Aspect-Opinion Pair Extraction (2020.acl-main)
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| Challenge: | Existing studies focus on aspect-opinion relation detection, but neglect to recognize the relations between aspects and opinion expressions. |
| Approach: | They propose a Synchronous Double-channel Recurrent Network to deal with AOPE task . they propose an opinion entity extraction unit, a relation detection unit, and a synchronization unit . |
| Outcome: | The proposed system achieves state-of-the-art in opinion entity extraction . it is based on three datasets based upon SemEval 2014 and 2015 benchmarks . |
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. |
EvoCoT: Overcoming the Exploration Bottleneck in Reinforcement Learning for LLMs (2026.findings-acl)
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| Challenge: | Existing approaches to reinforcement learning with verifiable reward (RLVR) are limited by difficulty or lack of exploration. |
| Approach: | They propose a self-evolving curriculum learning framework based on chain-of-thought reasoning optimization that constrains exploration space by self-generating and verifying CoT trajectories. |
| Outcome: | The proposed framework enables LLMs to solve previously unsolved problems without external supervision and is compatible with various RL fine-tuning methods. |
More Than Spoken Words: Nonverbal Message Extraction and Generation (2023.emnlp-main)
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| Challenge: | Existing studies focus on extracting NMs from small-scale well-structured corpora such as movie scripts wherein NM is enclosed in parentheses by scriptwriters, which greatly decreases the difficulty of extraction. |
| Approach: | They propose to extract nonverbal messages (NMs) from written text and NMs from spoken text by using a semi-supervised learning algorithm. |
| Outcome: | The extracted NMs can generate more relevant, valid, and factually consistent NM than the purely supervised generator. |
The UIR Uncertainty Corpus for Chinese: Annotating Chinese Microblog Corpus for Uncertainty Identification from Social Media (L18-1)
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| Challenge: | Uncertainty identification is an important semantic processing task, critical to the quality of information in terms of factuality in many NLP techniques and applications. |
| Approach: | They propose to annotate Chinese microblogs with an open uncertainty corpus . they propose to use contextual uncertain semantics rather than traditional cue-phrases to identify uncertainty . |
| Outcome: | The proposed corpus can be used to identify uncertainty in social media texts. |
Transforming Visual Scene Graphs to Image Captions (2023.acl-long)
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Xu Yang, Jiawei Peng, Zihua Wang, Haiyang Xu, Qinghao Ye, Chenliang Li, Songfang Huang, Fei Huang, Zhangzikang Li, Yu Zhang
| Challenge: | Existing approaches to generate captions using image captioning are based on multi-head attention (MHA) |
| Approach: | They propose to transform scene graphs into more descriptive captions by using multi-head attention to build a Graph Neural Network (GNN) . they construct a Mixture-of-Expert (MOE)-based decoder where each expert is built on MHA for discriminating the graph embeddings to generate different kinds of words. |
| Outcome: | The proposed framework can generate captions from multiple visual features and objects . it is based on a mixture-of-expert (MOE)-based decoder based upon MHA . |
RolePlot: A Systematic Framework for Evaluating and Enhancing the Plot-Progression Capabilities of Role-Playing Agents (2025.acl-long)
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| Challenge: | Existing research has focused on role-playing agents’ ability to portray specified characters, but their ability to advance the plot requires substantial improvements to deliver more engaging interaction. |
| Approach: | They propose a role-playing framework to evaluate and enhance the plot-progression capabilities of role-players. |
| Outcome: | The proposed framework improves RPAs’ ability to time plot developments and yields a significant increase in conversation turns and sustained higher arousal levels. |
Chunks as Arms: Multi-Armed Bandit-Guided Sampling for Long-Context LLM Preference Optimization (2026.acl-long)
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Shaohua Duan, Pengcheng Huang, Xinze Li, Zhenghao Liu, Xiaoyuan Yi, Yukun Yan, Shuo Wang, Yu Gu, Ge Yu, Maosong Sun
| Challenge: | Recent studies have explored fine-tuning Large Language Models with synthetic data to enhance their long-context capabilities. |
| Approach: | They propose a framework that leverages a Multi-Armed Bandit rollout strategy to identify the most informative chunks from the given long context for sampling high-quality and diverse responses. |
| Outcome: | The proposed framework achieves 4% improvement on long-context reasoning benchmarks on Llama and Qwen. |
Self-Reinforcing Controllable Synthesis of Rare Relational Data via Bayesian Calibration (2026.findings-acl)
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Chongsheng Zhang, Hao Wang, Zelong Yu, Esteban Garces Arias, Julian Rodemann, Zhanshuo Zhang, Qilong Li, Gaojuan Fan, Krikamol Muandet, Christian Heumann
| Challenge: | Existing approaches to synthesis of relational/structured tabular data lack effective feedback mechanism to optimize quality of generated data. |
| Approach: | They propose a relational data generator with dynamic guidance framework that uses chain-of-thought steps to generate tabular data for enhancing downstream imbalanced classification performance. |
| Outcome: | The proposed framework outperforms existing approaches in both data fidelity and downstream imbalanced classification performance on real and synthetic datasets. |
AJ-Bench: Benchmarking Agent-as-a-Judge for Environment-Aware Evaluation (2026.findings-acl)
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Wentao Shi, Yu Wang, Yuyang Zhao, Yuxin Chen, Fuli Feng, Xueyuan Hao, Xi Su, Qi GU, Hui Su, Xunliang Cai, Xiangnan He
| Challenge: | Existing approaches to verify agent behaviors in complex environments rely on rule-based verifiers or LLM-as-a-Judge models. |
| Approach: | They propose a benchmark to evaluate Agent-as-a-Judge across three domains . the benchmark covers search, data systems, and graphical user interfaces - with 155 tasks and 516 trajectories . |
| Outcome: | The proposed benchmark outperforms existing benchmarks in search, data systems, and GUI domains while revealing open challenges in agent-based verification. |
InFoBench: Evaluating Instruction Following Ability in Large Language Models (2024.findings-acl)
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Yiwei Qin, Kaiqiang Song, Yebowen Hu, Wenlin Yao, Sangwoo Cho, Xiaoyang Wang, Xuansheng Wu, Fei Liu, Pengfei Liu, Dong Yu
| Challenge: | Existing methods for evaluating Large Language Models (LLMs) ability to follow instructions have not been able to provide a detailed analysis of their compliance with instructions. |
| Approach: | They propose a new metric for evaluating Large Language Models' ability to follow instructions and a benchmark for DRFR. |
| Outcome: | The proposed metric and benchmark compared with traditional scoring methods and explores annotation sources including human experts, crowd-sourced workers, and GPT-4. |
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. |
Generative Emotion Cause Triplet Extraction in Conversations with Commonsense Knowledge (2023.findings-emnlp)
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| Challenge: | Existing studies on ECTEC focus on Causal Emotion Entailment and Emotion-Cause Pair Extraction in Conversations. |
| Approach: | They propose to decompose the ECTEC task into multiple subtasks and solve them in a pipeline manner. |
| Outcome: | The proposed model outperforms competing systems on two benchmark datasets. |
Attribution and Application of Multiple Neurons in Multimodal Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing methods to identify multimodal neurons in MLLMs are insufficiently understood . previous studies focused on identifying neurons corresponding to single-tokens . |
| Approach: | They propose a method to identify multimodal neurons in Transformer-based MLLMs . they introduce fuzzy set theory to model the complex relationship between neurons and semantic concepts . |
| Outcome: | The proposed method improves performance on the Visual Question Answering task. |
Post-decoder Biasing for End-to-End Speech Recognition of Multi-turn Medical Interview (2024.lrec-main)
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| Challenge: | End-to-end (E2E) models are replacing hybrid models for automatic speech recognition tasks. |
| Approach: | They propose a method to optimize E2E models for automatic speech recognition . they propose MED-IT, a multi-turn consultation speech dataset . |
| Outcome: | The proposed method improves on subsets of rare words appearing in training speech. |
Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-centric Summarization (2022.acl-long)
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| Challenge: | Existing methods to generate educational questions of fairytales or storybooks are difficult to implement due to adults lacking the skills or time to integrate such interactive opportunities. |
| Approach: | They propose a question generation method that first learns the question type distribution of an input story paragraph, and then summarizes salient events which can be used to generate high-cognitive-demand questions. |
| Outcome: | The proposed method performs well on automatic and human evaluation metrics on a newly proposed educational question-answering dataset FairytaleQA. |
A Bi-Model Based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling (N18-2)
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| Challenge: | Intent detection and slot filling are two main tasks for building a spoken language understanding system. |
| Approach: | They propose to use a sequence to sequence model to generate both intent and slot filling tasks together to perform the two tasks jointly. |
| Outcome: | The proposed model achieves 0.5% intent accuracy improvement and 0.9 % slot filling improvement on the ATIS benchmark data. |
Causal Document-Grounded Dialogue Pre-training (2023.emnlp-main)
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Yingxiu Zhao, Bowen Yu, Bowen Li, Haiyang Yu, Jinyang Li, Chao Wang, Fei Huang, Yongbin Li, Nevin Zhang
| Challenge: | Existing methods for document-grounded dialogue (DocGD) rely on general pre-trained language models without a tailored pre-training approach that explicitly captures causal relationships. |
| Approach: | They propose a causally-complete dataset construction strategy for developing million-scale DocGD pre-training corpora and a perturbation-based strategy to capture causality. |
| Outcome: | The proposed strategy yields significant and consistent improvements in fully-supervised, low-resource, few-shot, and zero-shot settings. |
Non-Autoregressive Translation by Learning Target Categorical Codes (2021.naacl-main)
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| Challenge: | Existing non-autoregressive text generation models still fall behind in translation quality . authors propose a model that learns implicitly categorical codes as latent variables . |
| Approach: | They propose a non-autoregressive Transformer model that implicitly categorizes latent variables into decoding . they find it improves translation quality by introducing more informative decoder inputs . |
| Outcome: | The proposed model achieves comparable or better performance in machine translation tasks than strong baselines. |
CoT-Valve: Length-Compressible Chain-of-Thought Tuning (2025.acl-long)
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| Challenge: | Wei et al., 2022) have developed a powerful method for enhancing the reasoning capabilities of large language models. |
| Approach: | They propose to use a tuning and inference strategy to control the length of reasoning chains by a parameter space direction to control their length. |
| Outcome: | The proposed method reduces reasoning chains on GSM8K from 741 to 225 tokens with a minor performance drop (95.07% to 94.92%) and on AIME from 6827 to 4629 tokens, with only one additional incorrect answer. |
InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration (2024.findings-emnlp)
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| Challenge: | Large Language Models have exceptional capabilities in open generation, yet they encounter difficulties with tasks that require intensive knowledge. |
| Approach: | They propose a framework that integrates unknown knowledge into LLMs without overlap . they propose integrating domain-specific knowledge graphs into Llms to reduce knowledge forgetting . |
| Outcome: | The proposed framework outperforms state-of-the-art baselines in integrating new knowledge into LLMs. |
ReflectRM: Boosting Generative Reward Models via Self-Reflection within a Unified Judgment Framework (2026.acl-long)
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Kai Qin, Liangxin Liu, Yu Liang, Longzheng Wang, null Wangyan, Zhang Yueyang, Long Xia, Zhiyuan Sun, Houde Liu, Daiting Shi
| Challenge: | Existing methods for generating reward models focus on outcome-level supervision, neglecting analytical process quality, which constrains their potential. |
| Approach: | They propose a novel reward model that leverages self-reflection to assess analytical quality and enhance preference modeling. |
| Outcome: | The proposed model improves performance on four benchmarks and significantly mitigates positional bias. |
GUI-Bee: Align GUI Action Grounding to Novel Environments via Autonomous Exploration (2025.emnlp-main)
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| Challenge: | Recent work of GUI action grounding fine-tunes data from pre-trained MLLMs, but data is limited to specific GUI environments. |
| Approach: | They propose to use a GUI-based agent to collect environment-specific data and fine-tune GUI grounding models with the collected data. |
| Outcome: | The proposed model can be extended to other GUI environments to improve performance. |
Defining a New NLP Playground (2023.findings-emnlp)
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Sha Li, Chi Han, Pengfei Yu, Carl Edwards, Manling Li, Xingyao Wang, Yi Fung, Charles Yu, Joel Tetreault, Eduard Hovy, Heng Ji
| Challenge: | Recent explosion of performance of large language models (LLMs) has changed the field more abruptly and seismically than any other shift in the field’s 80 year history. |
| Approach: | They propose 20+ PhD-dissertation-worthy research directions to define a new NLP playground by combining theoretical analysis, new and challenging problems, learning paradigms and interdisciplinary applications. |
| Outcome: | The proposed research will cover theoretical analysis, new and challenging problems, learning paradigms and interdisciplinary applications. |
Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL (2021.acl-long)
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| Challenge: | XDTS is a cross-database context-dependent text-to-sql problem with wide range of applications. |
| Approach: | They present a large-scale Chinese dataset for cross-database context-dependent Text-to-SQL . they find that only 35% of questions are context-independent and 28% of SQL queries are easy . |
| Outcome: | The proposed approach achieves an exact match accuracy of 40% over all questions and 16% over all question sequences. |
We Need to Talk About Reproducibility in NLP Model Comparison (2023.emnlp-main)
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| Challenge: | Existing studies show that standard splits produce low reproducible and unreliable conclusions . reproducibility of empirical experimental conclusions is a problem in NLP domain . |
| Approach: | They propose to transform the reproducibility of a model comparison into a probabilistic function . they propose to use a regularized corpus splitting strategy to estimate the model's performance . |
| Outcome: | The proposed estimator achieves a high SNR and significantly increases reproducibility. |
ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization (2025.findings-emnlp)
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Zhensheng Jin, Xinze Li, Yifan Ji, Chunyi Peng, Zhenghao Liu, Qi Shi, Yukun Yan, Shuo Wang, Furong Peng, Ge Yu
| Challenge: | Existing methods to train LLMs suffer from overthinking, leading to lengthy reasoning traces . Existing approaches to train large language models suffer from this problem . |
| Approach: | They propose a method to combine multiple reasoning chains for training LLMs . they use stepwise exploration and long-short switched sampling to evaluate reasoning paths . |
| Outcome: | The proposed method reduces reasoning lengths by approximately 30-50% . it also maintains or improves reasoning accuracy compared to baselines . |
Integrating Representation Subspace Mapping with Unimodal Auxiliary Loss for Attention-based Multimodal Emotion Recognition (2024.lrec-main)
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Xulong Du, Xingnan Zhang, Dandan Wang, Yingying Xu, Zhiyuan Wu, Shiqing Zhang, Xiaoming Zhao, Jun Yu, Liangliang Lou
| Challenge: | Existing methods to identify emotions rely on a large modality gap in their representations . |
| Approach: | They propose a representation subspace mapping module that maps each modality into two distinct subspaces and a cross-modality attention module that leverages auxiliary loss to remove the noise unrelated to emotion classification. |
| Outcome: | The proposed approach achieves superior performance to state-of-the-art MER methods on the IEMOCAP and MSP-Improv datasets. |
Sequential-NIAH: A Needle-In-A-Haystack Benchmark for Extracting Sequential Needles from Long Contexts (2025.emnlp-main)
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Yifei Yu, Qian-Wen Zhang, Lingfeng Qiao, Di Yin, Fang Li, Jie Wang, Chen Zeng Xi, Suncong Zheng, Xiaolong Liang, Xing Sun
| Challenge: | Recent models have extended Corresponding Author. context lengths to millions of tokens while maintaining reasoning and comprehension capabilities. |
| Approach: | They propose a benchmark to evaluate the ability of large language models to extract sequential information items from long contexts. |
| Outcome: | The proposed model achieves maximum accuracy of 63.50% on six well-known LLMs. |
F-Eval: Asssessing Fundamental Abilities with Refined Evaluation Methods (2024.acl-long)
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Yu Sun, Keyuchen Keyuchen, Shujie Wang, Peiji Li, Qipeng Guo, Hang Yan, Xipeng Qiu, Xuanjing Huang, Dahua Lin
| Challenge: | Large language models (LLMs) have been evaluated for their instruction-following capabilities but lack references to their fundamental abilities. |
| Approach: | They propose a bilingual evaluation benchmark to evaluate the fundamental abilities of large language models including expression, commonsense and logic. |
| Outcome: | The proposed evaluation methods show higher correlation coefficients and larger distinction than other evaluators. |
Learning Retrieval Augmentation for Personalized Dialogue Generation (2023.emnlp-main)
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| Challenge: | Personalized dialogue generation is a popular approach for conversational AI applications . however, persona profiles may not provide comprehensive descriptions of the persona . |
| Approach: | They propose a method that leverages persona profiles and dialogue context to generate personalized dialogues by leveraging personas and persona profile. |
| Outcome: | The proposed method outperforms baselines on the CONVAI2 dataset . it is expected to generate personalized dialogues based on persona profiles and dialogue context . |
Context-Aware Conversation Thread Detection in Multi-Party Chat (D19-1)
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| Challenge: | In multi-party chat, it is common for multiple conversations to occur concurrently . a new model that automatically disentangles conversation threads is proposed . |
| Approach: | They propose a Context-Aware Thread Detection model that automatically disentangles conversation threads in chat logs. |
| Outcome: | The proposed model outperforms state-of-the-art models on four real-world chat logs. |
Action Boundary Blindness: When LLM Agents Cannot Tell Where One Action Ends and Another Begins (2026.acl-long)
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| Challenge: | Large language model agents exhibit action boundary blindness, granularity confusion, scope creep and boundary ambiguity . Explicit boundary prompting improves ABS by 0.08–0.13 across all models . |
| Approach: | They propose four automatic metrics that require no human annotation to detect boundary blindness . they propose to use a multi-label attribution framework to validate the models . |
| Outcome: | Experiments with seven large language model agents show that the best model achieves only 0.424 ABS . Explicit Boundary Prompting improves ABS by 0.08–0.13 across all models . |
HqeKV: Towards Hybrid Quantization and Eviction for KV Cache in Long-Context LLM Inference (2026.findings-acl)
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| Challenge: | autoregressive inference requires repeated computation across transformer layers. |
| Approach: | They propose a hybrid compression framework built on both quantization and eviction . they propose varying importance metric and flexible conversion policies to reduce memory overhead . |
| Outcome: | The proposed framework outperforms state-of-the-art methods under memory constraints. |
Explicit Planning Helps Language Models in Logical Reasoning (2023.emnlp-main)
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| Challenge: | Existing systems that use pre-trained large language models to perform multi-step logical reasoning have been unable to perform this task. |
| Approach: | They propose a system that uses language models to perform multi-step logical reasoning and incorporates explicit planning into the inference procedure. |
| Outcome: | The proposed system outperforms other competing methods on multiple datasets and significantly outperformed chain-of-thought prompting on the PrOntoQA dataset. |
Towards Generalized Open Information Extraction (2022.findings-emnlp)
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| Challenge: | Open Information Extraction (OpenIE) models are evaluated on in-domain test sets aside from the training corpus, which violates the initial task principle of domain-independence. |
| Approach: | They propose to generalize OpenIE over unseen target domains with different data distributions from source training domains. |
| Outcome: | The proposed method beats the previous methods in both in- and out-of-domain settings by 6.0% in F1 score absolutely. |
FaD-VLP: Fashion Vision-and-Language Pre-training towards Unified Retrieval and Captioning (2022.emnlp-main)
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| Challenge: | Prior work on multimodal fashion tasks has been limited by the data in individual benchmarks or has leveraged generic vision-and-language pre-training but have not taken advantage of the characteristics of fashion data. |
| Approach: | They propose a fashion-specific pre-training framework based on weakly-supervised triplets constructed from fashion image-text pairs. |
| Outcome: | The proposed framework is based on weakly-supervised triplets constructed from fashion image-text pairs and is competitive on a diverse set of fashion tasks. |
UCFE: A User-Centric Financial Expertise Benchmark for Large Language Models (2025.findings-naacl)
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Yuzhe Yang, Yifei Zhang, Yan Hu, Yilin Guo, Ruoli Gan, Yueru He, Mingcong Lei, Xiao Zhang, Haining Wang, Qianqian Xie, Jimin Huang, Honghai Yu, Benyou Wang
| Challenge: | Recent advances in large language models (LLMs) have expanded their potential applications in finance. |
| Approach: | They propose a framework to evaluate the ability of large language models to handle financial tasks using human expert evaluations and task-specific interactions. |
| Outcome: | The proposed framework evaluates the ability of large language models to handle complex financial tasks and combines human expert evaluations with dynamic, task-specific interactions to simulate the complexities of evolving financial scenarios. |
Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models (2023.findings-emnlp)
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Zhihan Zhang, Shuohang Wang, Wenhao Yu, Yichong Xu, Dan Iter, Qingkai Zeng, Yang Liu, Chenguang Zhu, Meng Jiang
| Challenge: | Large language models can perform a wide range of tasks by following natural language instructions without task-specific fine-tuning. |
| Approach: | They propose a method to automatically improve the quality of LLM instructions . they leverage the generative ability of LMS to generate diverse candidate instructions based on a scoring model trained on 575 existing NLP tasks. |
| Outcome: | The proposed method surpasses human-written and LLM-generated instructions on 118 out-of-domain tasks. |
Scaling Data Diversity for Fine-Tuning Language Models in Human Alignment (2024.lrec-main)
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| Challenge: | Large language models (LLMs) can reveal toxic or offensive content inadvertently or intentionally. |
| Approach: | They propose to control the diversity of both sides according to the number of samples for fine-tuning, which can directly reflect their impact. |
| Outcome: | The proposed approach improves the performance of large language models after fine-tuning. |
Language Model Based Text-to-Audio Generation: Anti-Causally Aligned Collaborative Residual Transformers (2025.emnlp-main)
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| Challenge: | Autoregressive language models excel in text-to-audio generation, but lag behind diffusion models by a non-trivial margin. |
| Approach: | They propose a framework that integrates multiple isolated transformers with causal conditioning and anti-causal alignment via reinforcement learning. |
| Outcome: | The proposed framework outperforms existing LM-based and diffusion-based systems in audio synthesis. |
Long-context Language Models Fail in Basic Retrieval Tasks Without Sufficient Reasoning Steps (2025.findings-emnlp)
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| Challenge: | despite their extensive context window, long-context language models fail in some basic cases . a recent study shows that long-cot methods are not necessary for long-constituency tasks . |
| Approach: | a new study evaluates long-context language models with a large context window . the authors propose a method that can be well addressed with arbitrary reasoning steps . |
| Outcome: | The proposed methods are well addressed with a sufficient number of reasoning steps, guided by specific CoT prompts. |
Embedding-Informed Adaptive Retrieval-Augmented Generation of Large Language Models (2025.coling-main)
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| Challenge: | Retrieval-augmented large language models excel in various NLP tasks but are not always helpful when the knowledge required is absent in the model. |
| Approach: | They propose to determine whether the model is knowledgeable on a query via inspecting the (contextualized) pre-trained token embeddings of LLMs. |
| Outcome: | Experiments show that the proposed approach performs better than previous approaches on various benchmarks. |
GCRC: A New Challenging MRC Dataset from Gaokao Chinese for Explainable Evaluation (2021.findings-acl)
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| Challenge: | Existing machine reading comprehension datasets lack an explainable evaluation of systems' reasoning capabilities. |
| Approach: | They propose a dataset with multi-choice questions that evaluates MRC systems' reasoning process . they use sentence-level relevant supporting facts, error reason of distractors to evaluate MRC . |
| Outcome: | The proposed dataset is more challenging and useful for identifying limitations of existing MRC systems in an explainable way. |
Ditto: A Simple and Efficient Approach to Improve Sentence Embeddings (2023.emnlp-main)
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Qian Chen, Wen Wang, Qinglin Zhang, Siqi Zheng, Chong Deng, Hai Yu, Jiaqing Liu, Yukun Ma, Chong Zhang
| Challenge: | Prior studies diagnose the anisotropy problem in sentence embeddings from pre-trained language models without fine-tuning. |
| Approach: | They propose an unsupervised method that weights words with model-based importance estimations and computes the weighted average of word representations from pre-trained models as sentence embeddings. |
| Outcome: | Empirical evaluations show that the proposed method can alleviate the anisotropy problem and improve various pre-trained models on the STS benchmarks. |
APrompt: Attention Prompt Tuning for Efficient Adaptation of Pre-trained Language Models (2023.emnlp-main)
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Qifan Wang, Yuning Mao, Jingang Wang, Hanchao Yu, Shaoliang Nie, Sinong Wang, Fuli Feng, Lifu Huang, Xiaojun Quan, Zenglin Xu, Dongfang Liu
| Challenge: | Existing prompt tuning methods only introduce prompts at the input layer, limiting performance and leaving large room for improvement. |
| Approach: | They propose a method that involves tuning a small set of soft prompts for pre-trained language models. |
| Outcome: | The proposed method outperforms state-of-the-art methods with pre-trained models on the SuperGLUE benchmark. |
Explain the Synth: Interpretable Evaluation of LLM Data Synthesis (2026.acl-long)
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| Challenge: | Large language models (LLMs) are increasingly used to generate tabular data. |
| Approach: | They propose a framework that uses a rule-based model as a shared explanatory language to examine the explanation of real versus synthetic data. |
| Outcome: | The proposed framework compares the explanatory structure induced by real versus synthetic data. |
EIFBENCH: Extremely Complex Instruction Following Benchmark for Large Language Models (2025.emnlp-main)
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| Challenge: | Existing benchmarks focusing on single-task environments with limited constraints lack the complexity required to fully reflect the evolution of large language models (LLMs). |
| Approach: | They propose to use a Segment Policy Optimization algorithm to enhance the LLM's ability to accurately fulfill multi-task workflows. |
| Outcome: | The proposed benchmarks show that existing benchmarks lack the complexity required to fully reflect the evolution of large language models. |
TRUE-UIE: Two Universal Relations Unify Information Extraction Tasks (2024.naacl-long)
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| Challenge: | Information extraction (IE) tasks have a variety of schemas and objectives that differ across tasks. |
| Approach: | They propose a paradigm where all IE tasks are aligned to learn the same goals . they use two universal relations to extract mention spans and type recognition . |
| Outcome: | The proposed model achieves state-of-the-art on established benchmarks spanning 16 datasets, spanning 7 diverse IE tasks. |
JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs (2021.findings-acl)
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| Challenge: | Existing pre-trained models for knowledgegraph-to-text generation ignore graph structure during encoding and lack elaborate pre-training tasks to explicitly model graph-text alignments. |
| Approach: | They propose a graph-text joint representation learning model called JointGT which incorporates a structure-aware semantic aggregation module into each Transformer layer to preserve the graph structure. |
| Outcome: | The proposed model achieves state-of-the-art performance on various KG-to-text datasets. |
VocalNet: Speech LLMs with Multi-Token Prediction for Faster and High-Quality Generation (2025.emnlp-main)
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| Challenge: | Experimental results show VocalNet outperforms existing open-source speech LLMs despite limited training data. |
| Approach: | They propose a scalable and model-agnostic training framework and a novel multi-token prediction paradigm for speech generation. |
| Outcome: | The proposed model outperforms open-source speech LLMs while outperforming existing open-sourced models. |
HFT: Half Fine-Tuning for Large Language Models (2025.acl-long)
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| Challenge: | Large language models (LLMs) with one or more fine-tuning phases can unlock various capabilities, but can be catastrophic forgetting during sequential training. |
| Approach: | They propose a method to regularly reset partial parameters to mitigate forgetting issues by using half fine-tuning instead of full fine-uning. |
| Outcome: | The proposed approach reduces the risk of catastrophic forgetting during training and the parametric knowledge lost during training may be overwhelmed by incoming training data. |
Porous Lattice Transformer Encoder for Chinese NER (2020.coling-main)
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| Challenge: | Existing methods to integrate word boundary information into character-level Chinese NER are inefficient and lack semantic interaction. |
| Approach: | They propose an extension of transformer encoder that is tailored for ChineseNER to incorporate lexicons into character-level Chinese NER by lattices. |
| Outcome: | The proposed extension performs 11.4 times faster than state-of-the-art methods while retaining the rich long-term dependencies. |
Enabling Agents to Communicate Entirely in Latent Space (2026.acl-long)
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Zhuoyun Du, Runze Wang, Huiyu Bai, Zouying Cao, Xiaoyong Zhu, Yu Cheng, Bo Zheng, Wei Chen, Haochao Ying
| Challenge: | Natural language is the de facto communication medium for LLM-based agents, but it presents a fundamental constraint . natural language downsampling limits the depth and nuance of information that can be transmitted . et al.: inter-agent latent space communication is a promising paradigm for solving complex tasks . |
| Approach: | They propose a paradigm that leverages the last hidden states of an LLM as a representation of its thought for direct communication. |
| Outcome: | The proposed paradigm outperforms fine-tuned chain-of-thought prompting and single-agent baselines even across heterogeneous models. |
DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models (2023.acl-long)
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| Challenge: | Conventional fine-tuning works through updating all of the parameters in the pre-trained model, but as the size of pre-train models grows, it can be time-consuming and computationally expensive. |
| Approach: | They propose a framework for resource- and parameter-efficient fine-tuning by leveraging the sparsity prior in both weight updates and the final model weights. |
| Outcome: | The proposed framework saves 25% inference FLOPs while maintaining competitive downstream performance. |
Beyond Modality Collapse: Taming Guided Modality Entropy for Omni-modal Emotion Reasoning (2026.findings-acl)
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| Challenge: | EmoOmni is a data paradigm for omni-modal large language models that can be used for emotion reasoning. |
| Approach: | They propose a data paradigm that interleaves guided tokens into reasoning traces to enforce structured evidence extraction. |
| Outcome: | The proposed paradigm over-relys on a dominant modality while neglecting complementary cues. |
The CRECIL Corpus: a New Dataset for Extraction of Relations between Characters in Chinese Multi-party Dialogues (2022.lrec-1)
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Yuru Jiang, Yang Xu, Yuhang Zhan, Weikai He, Yilin Wang, Zixuan Xi, Meiyun Wang, Xinyu Li, Yu Li, Yanchao Yu
| Challenge: | Existing datasets focus on relation extraction between two entities in one sentence, and some focus on cross-sentence relationships. |
| Approach: | They propose to use a Chinese multi-party dialogue dataset for automatic extraction of dialogue-based character relationships. |
| Outcome: | The proposed dataset extracts relationships between 140 entities on the CRECIL corpus and another existing relation extraction corpus. |
XL-NBT: A Cross-lingual Neural Belief Tracking Framework (D18-1)
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| Challenge: | a multi-lingual approach to training dialog systems is expensive and tedious, but it can be useful for cross-lingual support. |
| Approach: | They propose to annotate data for multiple languages and train a multi-lingual dialog system for each language. |
| Outcome: | The proposed framework bypasses the expensive human annotation and achieves promising results. |
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. |
Attention Entropy is a Key Factor: An Analysis of Parallel Context Encoding with Full-attention-based Pre-trained Language Models (2025.acl-long)
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Zhisong Zhang, Yan Wang, Xinting Huang, Tianqing Fang, Hongming Zhang, Chenlong Deng, Shuaiyi Li, Dong Yu
| Challenge: | Large language models have demonstrated remarkable performance across a wide range of language tasks due to their remarkable ability in context modeling. |
| Approach: | They propose to use parallel context encoding to reduce attention entropy by incorporating attention sinks and selective mechanisms to reduce irregular attention . they also propose to incorporate attention sink mechanisms into the parallel encoded context to reduce the irregular attention. |
| Outcome: | The proposed methods lower irregular attention entropy and narrow performance gaps. |
The Security Threat of Compressed Projectors in Large Vision-Language Models (2025.findings-emnlp)
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| Challenge: | Mainstream VLPs have significant security implications, but their security implications have not been thoroughly examined. |
| Approach: | a study evaluates the security of visual language projectors by comparing them to uncompressed projector. |
| Outcome: | The evaluation reveals significant differences in security profiles between compressed and uncompressed projectors. |
Universal Information Extraction with Meta-Pretrained Self-Retrieval (2023.findings-acl)
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Xin Cong, Bowen Yu, Mengcheng Fang, Tingwen Liu, Haiyang Yu, Zhongkai Hu, Fei Huang, Yongbin Li, Bin Wang
| Challenge: | Existing methods for IE are task-specific, resulting in specialized and isolated approaches for different tasks. |
| Approach: | They propose a method to retrieve task-specific knowledge from pretrained language models to enhance universal IE by using a Meta-Pretraining Algorithm. |
| Outcome: | The proposed method achieves the new state-of-the-art on 4 IE tasks, 12 datasets under fully-supervised, low-resource and few-shot scenarios. |
AwarenessBench: Assessing Cognitive Capabilities of Language Models (2026.acl-long)
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Xiaojian Li, Rongwu Xu, Tianyun Zhang, Yue Wang, Shuo Chen, Qiner Lyu, Briana Zhang, Peiran Yang, Kyle Xue Chen, Haoyuan Shi, Yu Wang, Wei Xu
| Challenge: | Language models exhibit increasingly consciousness-like behaviors, requiring a baseline to evaluate their cognitive abilities. |
| Approach: | They propose a benchmark to assess the cognitive abilities of language models (LMs) they compare 18 state-of-the-art LMs to human models in metacognition, self-awareness, social awareness and situational awareness . |
| Outcome: | Evaluating 18 state-of-the-art LMs, they find they consistently surpass baselines . but most models fall short in metacognition and self-awareness, the study finds . |
ConsistRM: Improving Generative Reward Models via Consistency-Aware Self-Training (2026.acl-long)
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Yu Liang, Liangxin Liu, Longzheng Wang, null Wangyan, Zhang Yueyang, Long Xia, Zhiyuan Sun, Daiting Shi
| Challenge: | ConsistRM is a self-training framework that enables effective and stable GRM training without human annotations. |
| Approach: | They propose a self-training framework that enables effective and stable GRM training without human annotations. |
| Outcome: | The proposed framework outperforms vanilla Reinforcement Fine-Tuning (RFT) by 1.5% on five benchmark datasets. |
TCSinger 2: Customizable Multilingual Zero-shot Singing Voice Synthesis (2025.findings-acl)
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Yu Zhang, Wenxiang Guo, Changhao Pan, Dongyu Yao, Zhiyuan Zhu, Ziyue Jiang, Yuhan Wang, Tao Jin, Zhou Zhao
| Challenge: | Existing zero-shot singing voice synthesis models depend on phoneme and note boundary annotations, limiting their robustness and producing poor transitions between phonemes and notes. |
| Approach: | They propose a multi-task multilingual zero-shot SVS model with style transfer and style control based on various prompts. |
| Outcome: | Experimental results show that TCSinger 2 outperforms baseline models in subjective and objective metrics across multiple related tasks. |
RiskLab: A Controlled Toolkit for Probing Emergent Risks in LLM-Based Multi-Agent Systems (2026.acl-demo)
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Yu Jiang, Wenjie Wang, Yue Huang, Yanbo Wang, Zhenhong Zhou, Xiuying Chen, Yang Liu, Pin-Yu Chen, Wei Wang, Xiangliang Zhang
| Challenge: | Recent advances in large language model (LLM) agents have accelerated deployment of multi-agent systems for complex tasks. |
| Approach: | They propose an open-source toolkit for instantiating, probing, and measuring emergent risks in LLM-based multi-agent systems under controlled conditions. |
| Outcome: | The proposed toolkit is based on a structured topology–environment–protocol–agent–task quintuple enabling reproducible studies of how communication structure, coordination mechanisms, and incentives shape system-level risks. |
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. |
How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers? (2020.findings-emnlp)
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| Challenge: | Task-agnostic data augmentations have proven widely effective in computer vision, even on pretrained models. |
| Approach: | They examine the effects of two types of task-agnostic data augmentation on pretrained transformers using 5 classification tasks and 6 datasets. |
| Outcome: | The proposed techniques improve performance on 5 classification tasks, 6 datasets, and 3 variants of modern pretrained transformers. |
SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model (2025.naacl-long)
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| Challenge: | Fine-tuning requires substantial computational resources and is prone to overfitting when applied to small datasets. |
| Approach: | They propose a parameter-efficient fine-tuning method that integrates a State Space Model (SSM) to interconnect low-rank matrices. |
| Outcome: | The proposed method achieves comparable performance to LoRA on the general language understanding evaluation (GLUE) benchmark while using only half the parameters. |
On Training Data Influence of GPT Models (2024.emnlp-main)
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| Challenge: | generative language models have redefined performance standards across tasks . current research on the influence of training data on autoregressivity remains underexplored . |
| Approach: | They propose a parameterized simulation to assess the impact of training examples on the training dynamics of GPT models. |
| Outcome: | The proposed approach compares existing methods with existing methods across training scenarios in generative language models, spanning tasks across 14 million to 2.8 billion parameters. |
MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation (2025.emnlp-main)
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Weihao Xuan, Rui Yang, Heli Qi, Qingcheng Zeng, Yunze Xiao, Aosong Feng, Dairui Liu, Yun Xing, Junjue Wang, Fan Gao, Jinghui Lu, Yuang Jiang, Huitao Li, Xin Li, Kunyu Yu, Ruihai Dong, Shangding Gu, Yuekang Li, Xiaofei Xie, Felix Juefei-Xu, Foutse Khomh, Osamu Yoshie, Qingyu Chen, Douglas Teodoro, Nan Liu, Randy Goebel, Lei Ma, Edison Marrese-Taylor, Shijian Lu, Yusuke Iwasawa, Yutaka Matsuo, Irene Li
| Challenge: | Existing large language model evaluation benchmarks focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-lingual reasoning abilities. |
| Approach: | They propose a comprehensive benchmark covering 29 languages, built on an English benchmark. |
| Outcome: | The MMLU-ProX is a comprehensive benchmark covering 29 languages, built on an English benchmark. |
Few-Shot Event Detection with Prototypical Amortized Conditional Random Field (2021.findings-acl)
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| Challenge: | Existing approaches to event detection ignore the trigger discrepancy and cause errors. |
| Approach: | They propose a unified model which converts a few-shot tagging problem into a single-shot model by using a Gaussian distribution. |
| Outcome: | The proposed model performs better than existing identifythen-classify models on a few-shot tagging problem with a double-part taging scheme. |
Towards Abstractive Grounded Summarization of Podcast Transcripts (2022.acl-long)
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| Challenge: | Podcast summarization is of practical benefit to content providers and consumers . however, podcast summarizing faces significant challenges including factual inconsistencies . speech recognizers induce transcription errors and abstractive summarisation models may hallucinate . |
| Approach: | They propose a method to generate podcast summaries while grounding segments in specific regions of the transcript to allow full inspection of summary details. |
| Outcome: | The proposed method can produce an abstractive summary while grounding segments in specific regions of the transcript to allow full inspection of summary details. |
CodeRAG-Bench: Can Retrieval Augment Code Generation? (2025.findings-naacl)
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Zora Zhiruo Wang, Akari Asai, Xinyan Velocity Yu, Frank F. Xu, Yiqing Xie, Graham Neubig, Daniel Fried
| Challenge: | Language models excel at generating code, but many programs are difficult to generate using only parametric knowledge. |
| Approach: | They propose a retrieval-augmented code generation benchmark that provides reproducible evaluations on retrieval and end-to-end code generation performance. |
| Outcome: | The proposed benchmark covers programming, open-domain, and repository-level tasks and provides reproducible evaluations on retrieval and end-to-end code generation performance. |
Multimodal Instruction Tuning with Conditional Mixture of LoRA (2024.acl-long)
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| Challenge: | Multimodal Large Language Models (MLLMs) have demonstrated proficiency in diverse tasks across different domains. |
| Approach: | They propose a method that integrates multimodal instruction tuning with Conditional Mixture-of-LoRA. |
| Outcome: | Experimental results show that MixLoRA outperforms LoRA with the same or higher ranks . MLLMs have demonstrated remarkable proficiency in diverse tasks across domains . |
Me-Agent: A Personalized Mobile Agent with Two-Level User Habit Learning for Enhanced Interaction (2026.findings-acl)
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Shuoxin Wang, Chang Liu, Gowen Loo, Lifan Zheng, Kaiwen Wei, Huanqian Yan, Xinyi Zeng, Jingyuan Zhang, Yu Tian
| Challenge: | Existing Large Language Model (LLM)-based mobile agents follow explicit user instructions without personalized needs. |
| Approach: | They propose a user preference learning strategy enhanced with a Personal Reward Model to improve personalization performance. |
| Outcome: | The proposed agent achieves state-of-the-art performance while maintaining competitive instruction execution performance. |
COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values (2026.findings-eacl)
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Siwei Wu, JinCheng Ren, Xeron Du, Shuyue Guo, Xingwei Qu, Yiming Liang, Jie Liu, Yunwen Li, Tyler Loakman, Tianyu Zheng, Boyu Feng, Huaqing Yuan, Zili Wang, Jiaheng Liu, Wenhao Huang, Chenglin Cai, Haoran Que, Jian Yang, Yuelin Bai, Zekun Moore Wang, Zhouliang Yu, Qunshu Lin, Ding Pan, Yuchen Eleanor Jiang, Tiannan Wang, Wangchunshu Zhou, Shenzhi Wang, Xingyuan Bu, Minghao Liu, Guoyin Wang, Ge Zhang, Chenghua Lin
| Challenge: | Existing Chinese preference datasets suffer from limited scale, restricted domain coverage, and insufficiently rigorous data validation. |
| Approach: | They propose an LLM-based data annotation pipeline with no human intervention to annotate Chinese preference datasets. |
| Outcome: | The proposed pipeline outperforms existing Chinese preference datasets on AlignBench and Chinese Reward Benchmark. |
Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good (P19-1)
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| Challenge: | Persuasion agents are a form of communication that can be used to change people's opinions and actions for social good. |
| Approach: | They designed an online persuasion task where one participant was asked to persult the other to donate to a specific charity. |
| Outcome: | The proposed system could change people's opinions and actions for social good. |
Dict-BERT: Enhancing Language Model Pre-training with Dictionary (2022.findings-acl)
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Wenhao Yu, Chenguang Zhu, Yuwei Fang, Donghan Yu, Shuohang Wang, Yichong Xu, Michael Zeng, Meng Jiang
| Challenge: | Pre-trained language models (PLMs) capture word semantics in different contexts, hence the embeddings of rare words on the tail are poorly optimized. |
| Approach: | They propose to leverage definitions of rare words in dictionaries to enhance language model pre-training by leveraging dictionary definitions. |
| Outcome: | The proposed model improves understanding of rare words and boosts performance on various NLP downstream tasks. |
Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs (2024.findings-acl)
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Bowen Jin, Chulin Xie, Jiawei Zhang, Kashob Kumar Roy, Yu Zhang, Zheng Li, Ruirui Li, Xianfeng Tang, Suhang Wang, Yu Meng, Jiawei Han
| Challenge: | Existing studies suggest augmenting LLMs with external text corpora to alleviate hallucination problems. |
| Approach: | They propose to augment large language models with text units retrieved from external knowledge corpora to alleviate the issue. |
| Outcome: | The proposed framework outperforms baselines on GRBench with three LLMs and shows that iterative reasoning outperformed the baselines. |
Single Training Dimension Selection for Word Embedding with PCA (D19-1)
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| Challenge: | Existing methods for word embeddings are limited by the definition of 'low' dimensionality, which is often used to train word embeds into low dimensional continuous vector space. |
| Approach: | They propose a method to select the number of dimensions for word embeddings using PCA. |
| Outcome: | The proposed method trains one embedding with a generous upper bound (e.g. 1,000) of dimensions and then removes the lesser dimensions one at a time while recording the embeddables’ performance on language tasks. |
Fin-STAR: Structure-as-Semantics to Resolve Implicitness in Financial Retrieval (2026.findings-acl)
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Yu Zou, Yan Chen, Lida He, Qi Zhou, Xiaorui Zhou, Aixi Zhong, Yi Wang, Wei Li, Qingyu Wang, Jiatao Li, Wei Gong, Jialei Zeng, Jingmei Zhao, Ke Jiang, Qing Li
| Challenge: | Existing Retrieval-Augmented Generation systems treat structure as a physical navigational skeleton rather than intrinsic semantic knowledge. |
| Approach: | They propose a framework that redefining hierarchy as intrinsic semantics and uses snippets to enrich hierarchical lineage. |
| Outcome: | The proposed framework outperforms state-of-the-art hierarchical and graph-based benchmarks on FinTierQA Gold. |
Long-tailed Extreme Multi-label Text Classification by the Retrieval of Generated Pseudo Label Descriptions (2023.findings-eacl)
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| Challenge: | Extreme Multi-label text classification (XMTC) is a tough challenge due to the sheer size of the label spaces and the severe data scarcity problem associated with the long tail of rare labels in highly skewed distributions. |
| Approach: | They propose to use a trained bag-of-words classifier to generate pseudo label descriptions from a training bag- of-word classifier. |
| Outcome: | The proposed approach outperforms the existing models in the tail label prediction problem and achieves state-of-the-art (SOTA) performance on XMTC benchmark datasets. |
Improving Autoformalization Using Direct Dependency Retrieval (2026.acl-long)
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| Challenge: | Existing methods for hallucinate formal dependencies lack scalability and precision to leverage ever-growing public datasets. |
| Approach: | They propose a retrieval-augmented framework based on Direct Dependency Retrieval to generate formal dependencies from natural-language mathematical descriptions and verify their existence via an efficient Suffix Array Check (SAC). |
| Outcome: | The proposed framework outperforms state-of-the-art methods in retrieval precision and recall and can be used to validate formal representations in a public dataset. |
SPS: Steering Probability Squeezing for Better Exploration in Reinforcement Learning for Large Language Models (2026.findings-acl)
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Yifu Huo, Chenglong Wang, Ziming Zhu, Shunjie Xing, Peinan Feng, Tongran Liu, Qiaozhi He, Tian Hua Zhou, null Changxiaojia, JingBo Zhu, Zhengtao Yu, Tong Xiao
| Challenge: | Reinforcement learning (RL) training typically improves single-sample success rates but limited exploration of diverse reasoning trajectories. |
| Approach: | They propose a training paradigm that interleaves conventional RL with inverse reinforcement learning (IRL) they propose 'Steering Probability Squeezing' to enhance exploration without external supervision . |
| Outcome: | The proposed training paradigm improves Pass@k and improves exploration of diverse reasoning trajectories without external supervision. |
Correcting Chinese Spelling Errors with Phonetic Pre-training (2021.findings-acl)
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Ruiqing Zhang, Chao Pang, Chuanqiang Zhang, Shuohuan Wang, Zhongjun He, Yu Sun, Hua Wu, Haifeng Wang
| Challenge: | Existing methods for Chinese spelling correction only use pre-trained language model or incorporate phonological information as external knowledge. |
| Approach: | They propose a phonetic Chinese spelling correction model that integrates phonetic features into language model by leveraging pre-training and fine-tuning methods. |
| Outcome: | The proposed model outperforms existing methods on SIGHAN datasets and improves on other datasets. |
Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation (2025.findings-naacl)
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| Challenge: | Existing methods to enhance credibility and verifiability of large language models (LLMs) mainly focus on passage-level or paragraph-level references or citations, which fall short in verifikatability. |
| Approach: | They propose a method that provides sentence-level citations in LLM-generated responses. |
| Outcome: | The proposed method achieves 90% accuracy in long-form question-answering tasks. |
Efficient Inference for Large Language Models –Algorithm, Model, and System (2025.emnlp-tutorials)
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| Challenge: | Inference of LLMs incurs high computational costs, memory access overhead, and memory usage, leading to inefficiencies in terms of latency, throughput, power consumption, and storage. |
| Approach: | This tutorial introduces the basics of efficient inference for LLMs and explains how to diagnose efficiency bottlenecks for a given workload on specific hardware. |
| Outcome: | The tutorial introduces the basic concepts of modern LLMs, software and hardware. |
Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data (2024.findings-acl)
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Yanda Li, Chi Zhang, Gang Yu, Wanqi Yang, Zhibin Wang, Bin Fu, Guosheng Lin, Chunhua Shen, Ling Chen, Yunchao Wei
| Challenge: | OpenAI's GPT-4 has demonstrated remarkable multimodal capabilities, but specific mechanics of GPT4 remain unknown. |
| Approach: | They propose a data collection methodology that synchronously synthesizes images and dialogues for visual instruction tuning. |
| Outcome: | The proposed method improves on ten commonly assessed models and provides greater flexibility compared to existing methods. |
CENTAUR: Bridging the Impossible Trinity of Privacy, Efficiency, and Performance in Privacy-Preserving Transformer Inference (2025.acl-long)
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Jinglong Luo, Guanzhong Chen, Yehong Zhang, Shiyu Liu, Hui Wang, Yue Yu, Xun Zhou, Yuan Qi, Zenglin Xu
| Challenge: | Existing privacy-preserving Transformer Inference frameworks suffer from high computational overhead and performance losses. |
| Approach: | They propose a framework that integrates random permutations and SMPC to address the "impossible trinity" CENTAUR resists diverse data reconstruction attacks and boosts inference speed by 5.030.4 times . |
| Outcome: | CENTAUR achieves an unprecedented balance between privacy, efficiency, and performance. |
Can Reasoning Path still be Effective as Input? Bridging Post-Reasoning to Chain-of-Thought Compression (2026.acl-long)
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Chengzhengxu Li, Xiaoming Liu, Zhaohan Zhang, Shengchao Liu, Guoxin Ma, Yu Lan, Cong Wang, Chao Shen
| Challenge: | Existing work on reducing CoT generation in reasoning impairs the necessary information for deriving the correct answer. |
| Approach: | They propose a reasoning paradigm that takes CoT as a part of context to simplify the reasoning task for Large Language Models (LLMs). |
| Outcome: | The proposed framework reduces the generation length of LLMs, but its effectiveness hinges on the efficiency and reliability of the contextual CoT generation. |
EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association (2025.acl-long)
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Weiqi Wang, Limeng Cui, Xin Liu, Sreyashi Nag, Wenju Xu, Chen Luo, Sheikh Muhammad Sarwar, Yang Li, Hansu Gu, Hui Liu, Changlong Yu, Jiaxin Bai, Yifan Gao, Haiyang Zhang, Qi He, Shuiwang Ji, Yangqiu Song
| Challenge: | Goal-oriented script planning is used by humans to plan for typical activities . however, this capability remains underexplored due to several challenges . |
| Approach: | They propose a framework that enables product-enriched scripts by associating products with each step based on the semantic similarity between the actions and their purchase intentions. |
| Outcome: | The proposed framework can generate product-enriched scripts from 2.4 million scripts . human annotations are conducted to provide gold labels for a sampled subset . |
Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Coding (2022.findings-emnlp)
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| Challenge: | Existing methods for ICD coding are limited due to the high-dimensional space of multi-label assignment and the long-tail challenge. |
| Approach: | They propose a prompt-based fine-tuning technique with label semantics to solve this challenge. |
| Outcome: | The proposed method outperforms state-of-the-art methods on a benchmark dataset of code assignment in 14.5% of cases. |
RIVAL: Reinforcement Learning with Iterative and Adversarial Optimization for Machine Translation (2025.findings-emnlp)
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Tianjiao Li, Mengran Yu, Chenyu Shi, Yanjun Zhao, Xiaojing Liu, Qi Zhang, Xuanjing Huang, Qiang Zhang, Jiayin Wang
| Challenge: | Using reinforcement learning from human feedback, large language models perform poorly when applied to colloquial subtitle translation tasks. |
| Approach: | They propose an adversarial training framework that iteratively updates the offline reward model and the online LLM to improve training outcomes. |
| Outcome: | The proposed training framework significantly improves upon translation baselines. |
UniCorn: Towards Self-Improving Unified Multimodal Models through Self-Generated Supervision (2026.acl-long)
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Zhen Fang, Ruiyan Han, XinYu Sun, Yuchen Ma, Ziheng Wang, Yu Zeng, Zehui Chen, Lin Chen, Wenxuan Huang, Wei-Jie Xu, Yi Cao, Feng Zhao
| Challenge: | Unified Multimodal Models have achieved remarkable success in cross-modal comprehension, but a gap persists in their ability to translate internal knowledge into faithful and controllable synthesis. |
| Approach: | They propose a self-improvement framework that partitions a single UMM into three collaborative roles: Proposer, Solver, and Judge. |
| Outcome: | The proposed framework improves on TIIF, DPG, CompBench and UniCycle benchmarks. |
Rethinking the Reversal Curse of LLMs: a Prescription from Human Knowledge Reversal (2024.emnlp-main)
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Zhicong Lu, Li Jin, Peiguang Li, Yu Tian, Linhao Zhang, Sirui Wang, Guangluan Xu, Changyuan Tian, Xunliang Cai
| Challenge: | Existing methods for large language models (LLMs) are limited by their aggressive sample permutation and lack a detailed understanding of the underlying reasons for the reversal curse. |
| Approach: | They propose a method which enhances bidirectional entity correlation modeling and pairwise relationship reasoning to overcome the reversal curse. |
| Outcome: | The proposed method overcomes the reversal curse by augmenting the samples with entity order-reversals and semantically preserved question-answer pairs. |
CheckRLM: Effective Knowledge–Thought Coherence Checking in Retrieval-Augmented Reasoning (2026.acl-long)
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Dingling Xu, Ruobing Wang, Qingfei Zhao, Yukun Yan, Zhichun Wang, Daren Zha, Shi Yu, Zhenghao Liu, Shuo Wang, Xu Han, Maosong Sun
| Challenge: | Reasoning Language Models (RLMs) have improved performance on complex tasks by extending the reasoning chain, but they are prone to factual errors, especially in knowledge-intensive tasks. |
| Approach: | They propose a framework that improves the reliability of the reasoning process by timely checking and correcting factual errors. |
| Outcome: | The proposed framework outperforms baselines and shows that it mitigates error accumulation with lower costs. |
FocalOrder: Focal Preference Optimization for Reading Order Detection (2026.acl-long)
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Fuyuan Liu, Dianyu Yu, He Ren, Nayu Liu, Xiaomian Kang, Delai Qiu, Fa Zhang, Genpeng Zhen, Shengping Liu, Liang Jiaen, null Weihuang, Yining Wang, Junnan Zhu
| Challenge: | Existing methods for document comprehension rely on uniform supervision, resulting in a performance degradation in the intermediate sections. |
| Approach: | They propose a framework driven by Focal Preference Optimization to detect reading order in document layouts. |
| Outcome: | The proposed framework outperforms competing baselines and surpasses large-scale general VLMs. |
Measure Twice, Click Once: Co-evolving Proposer and Visual Critic via Reinforcement Learning for GUI Grounding (2026.acl-long)
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| Challenge: | Graphical User Interface (GUI) grounding requires mapping natural language instructions to precise pixel coordinates due to visually homogeneous elements and dense layouts. |
| Approach: | They propose to replace static consistency strategies with a learnable selection mechanism that selects the optimal target by critiquing its own proposals rendered on the screenshot. |
| Outcome: | The proposed model significantly improves both grounding and critiquing capabilities over 6 benchmarks. |
Protein Large Language Models: A Comprehensive Survey (2025.findings-emnlp)
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Yijia Xiao, Wanjia Zhao, Junkai Zhang, Yiqiao Jin, Han Zhang, Zhicheng Ren, Renliang Sun, Haixin Wang, Guancheng Wan, Pan Lu, Xiao Luo, Yu Zhang, James Zou, Yizhou Sun, Wei Wang
| Challenge: | Existing studies focus on specific aspects or applications, but this study provides a comprehensive overview of Protein-specific large language models. |
| Approach: | This paper proposes a structured taxonomy of state-of-the-art ProteinLLMs . they analyze how they leverage large-scale protein sequence data for improved accuracy . |
| Outcome: | The proposed model covers their architectures, training datasets, evaluation metrics, and diverse applications. |
Question-Interlocutor Scope Realized Graph Modeling over Key Utterances for Dialogue Reading Comprehension (2023.findings-acl)
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| Challenge: | Compared to standard RC tasks, dialogue reading comprehension (DRC) has raised challenges because of the complex speaker information and noisy dialogue context. |
| Approach: | They propose a new method for dialogue reading comprehension that extracts answers from dialogues by using key-utterances-extracting methods and a Question-Interlocutor Scope Realized Graph. |
| Outcome: | The proposed method achieves state-of-the-art performance against previous works. |
SYNFAC-EDIT: Synthetic Imitation Edit Feedback for Factual Alignment in Clinical Summarization (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) struggle with factual inaccuracies, a critical issue in clinical NLP applications where errors could lead to serious consequences. |
| Approach: | They propose a pipeline that leverages >100B parameter GPT variants to act as synthetic experts to generate edit feedback without additional human annotations. |
| Outcome: | The proposed pipeline aims to improve the quality of clinical note summarizations by generating edit feedback without human annotations. |
MM-SAP: A Comprehensive Benchmark for Assessing Self-Awareness of Multimodal Large Language Models in Perception (2024.acl-long)
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| Challenge: | Recent advances in multimodal large language models (MLLMs) have demonstrated exceptional capabilities in visual perception and understanding, but they also suffer from hallucinations, which limit their reliability as AI systems. |
| Approach: | They propose a benchmark to evaluate self-awareness in perception for multimodal large language models (MLLMs) by integrating image information with knowledge quadrants, and propose MM-SAP to evaluate this capability. |
| Outcome: | The proposed benchmark offers detailed analysis of MLLMs with self-awareness in perception. |
Detecting AI-Generated Content on Social Media with Multi-modal Language Models (2026.acl-industry)
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Chenyang Yang, Shen Yan, Yibo Yang, Litao Hu, Yuchen Liu, Yuan Zeng, Hanchao Yu, Yinan Zhu, Sumedha Singla, Brian Vanover, Huijun Qian, Zihao Wang, Fujun Liu, Aashu Singh, Jianyu Wang, Xuewen Zhang
| Challenge: | Existing methods for AI-generated content detection face poor generalization to newer models, reliance on single modalities, and lack of interpretable explanations. |
| Approach: | They propose a model that curates diverse social media data and trains a vision-language model for detection and explanation. |
| Outcome: | The proposed model achieves state-of-the-art detection performance on public benchmarks and observes positive downstream impacts on user engagement. |
TLUE: A Tibetan Language Understanding Evaluation Benchmark (2025.emnlp-main)
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Fan Gao, Cheng Huang, Yutong Liu, Nyima Tashi, Xiangxiang Wang, Thupten Tsering, Ban Ma-bao, Renzeng Duojie, Gadeng Luosang, Rinchen Dongrub, Dorje Tashi, Xiao Feng Cd, Yongbin Yu, Hao Wang
| Challenge: | Low-resource languages, like Tibetan, remain underrepresented in large language models' evaluations. |
| Approach: | They propose a Tibetan Language Understanding Evaluation Benchmark to assess LLMs' proficiency in Tibetan . they use a multi-task understanding benchmark and a safety benchmark to evaluate models . |
| Outcome: | The proposed benchmark shows that most large language models perform below the random baseline, especially in Tibetan language processing. |
SR-LLM: Rethinking the Structured Representation in Large Language Model (2025.acl-long)
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Jiahuan Zhang, Tianheng Wang, Ziyi Huang, Yulong Wu, Hanqing Wu, DongbaiChen DongbaiChen, Linfeng Song, Yue Zhang, Guozheng Rao, Kaicheng Yu
| Challenge: | Structured representations have long been pivotal in computational linguistics, but their role remains ambiguous in the Large Language Models (LLMs) era. |
| Approach: | They propose a framework that integrates structured representations into LLMs from training-free and training-dependent perspectives. |
| Outcome: | The proposed framework integrates structured representations through natural language descriptions in LLM prompts while augmenting the model’s inference capability through fine-tuning on linguistically described structured representation. |
Language Models over Large-Scale Knowledge Base: on Capacity, Flexibility and Reasoning for New Facts (2025.coling-main)
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| Challenge: | Existing studies on LMs lack systematic studies on their structured reasoning capabilities over the infused knowledge. |
| Approach: | They investigate how LMs of different sizes can store world knowledge of different frequencies in a large-scale KB after training on the abundant world knowledge triplets. |
| Outcome: | The proposed models can store and respond to natural language queries with flexibility and reasoning abilities, but they need to be enhanced to fully realize their potential. |
You Only Need One Single Token to Refine Safety Alignment (2026.findings-acl)
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| Challenge: | Excessive safety can lead to over-refusal, where models reject harmful-looking yet benign queries, severely limiting utility. |
| Approach: | They propose a lightweight training-based approach that reshapes the distributions of harmful and benign samples within the model’s decision space by using a single-token prefix. |
| Outcome: | The proposed approach can distinguish between harmful and benign samples while keeping the model frozen. |
Harnessing Pre-Trained Neural Networks with Rules for Formality Style Transfer (D19-1)
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| Challenge: | Existing studies normalize informal sentences with rules, but they introduce noise if we use them in a naive way. |
| Approach: | They propose to harness rules into a state-of-the-art neural network that is typically pretrained on massive corpora. |
| Outcome: | The proposed method can be used to generate a state-of-the-art on a small dataset. |
Serial Lifelong Editing via Mixture of Knowledge Experts (2025.acl-long)
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| Challenge: | Existing Lifelong Knowledge Editing methods struggle to overwrite outdated knowledge with the latest one. |
| Approach: | They propose a new Mixture-of-Knowledge-Experts scheme with an ARM . ARM ensures that each update completely overwrites old information with the latest one . Experimental results show that ARM performs favorably against SOTA knowledge editing methods . |
| Outcome: | The proposed scheme overwrites old knowledge with the latest data on a benchmark . it performs favorably against existing knowledge editing methods on the same concept . |
NCLS: Neural Cross-Lingual Summarization (D19-1)
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| Challenge: | Existing approaches to cross-lingual summarization divide the task into two steps: summarizing and translation. |
| Approach: | They propose to integrate two related tasks into the training process of CLS under multi-task learning to improve cross-lingual summarization. |
| Outcome: | The proposed framework improves on English-to-Chinese and Chinese-to English CLS human-corrected test sets. |
Table-Critic: A Multi-Agent Framework for Collaborative Criticism and Refinement in Table Reasoning (2025.acl-long)
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| Challenge: | Existing approaches to decompose large language models (LLMs) lack effective mechanisms to identify and correct errors in intermediate reasoning steps, leading to cascading error propagation. |
| Approach: | They propose a multi-agent framework that facilitates collaborative criticism and iterative refinement of the reasoning process until convergence to correct solutions. |
| Outcome: | The proposed framework achieves superior accuracy and error correction rates while maintaining computational efficiency and lower solution degradation rate. |
Generating Multiple-Length Summaries via Reinforcement Learning for Unsupervised Sentence Summarization (2022.findings-emnlp)
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| Challenge: | Existing models to summarize texts without ground-truth summaries are extractive, which remove words from texts and thus are less flexible than abstractive models. |
| Approach: | They propose an unsupervised model that extracts words from texts and makes them mutually enhance each other. |
| Outcome: | The proposed model outperforms both abstractive and extractive models, while generating new words not contained in input texts. |
SafeToolBench: Pioneering a Prospective Benchmark to Evaluating Tool Utilization Safety in LLMs (2025.findings-emnlp)
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| Challenge: | Existing approaches fail to fully capture all risks in tool utilization, resulting in financial loss or privacy leaking. |
| Approach: | They propose a framework to assess the safety of LLM tool utilization in a prospective manner, covering malicious user instructions and diverse practical toolsets. |
| Outcome: | The proposed framework significantly enhances LLMs’ self-awareness, enabling a more safer and trustworthy tool utilization. |
DRIFT: Transferring Reasoning Priors for Efficient MLLM Fine-Tuning (2026.findings-acl)
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Chao Huang, Zeliang Zhang, Jiang Liu, Ximeng Sun, Jialian Wu, Xiaodong Yu, Ze Wang, Chenliang Xu, Emad Barsoum, Zicheng Liu
| Challenge: | Multimodal large language models (MLLMs) have made rapid progress in perception and alignment, but their reasoning ability often lags behind strong text-only LLMs. |
| Approach: | They propose a method that transfers reasoning knowledge in the gradient space while preserving multimodal alignment. |
| Outcome: | Experiments on multimodal reasoning benchmarks show that DRIFT outperforms naive merging and standard SFT. |
Knowledge Distillation with Reptile Meta-Learning for Pretrained Language Model Compression (2022.coling-1)
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| Challenge: | Knowledge distillation (KD) can transfer knowledge from the original model into a compact model to achieve model compression. |
| Approach: | They propose a knowledge distillation method with reptile meta-learning to facilitate the transfer of knowledge from the teacher to the student. |
| Outcome: | Extensive experiments on the GLUE benchmark show the proposed method performs better than previous methods. |
FlowEval: A Consensus-Based Dialogue Evaluation Framework Using Segment Act Flows (2022.emnlp-main)
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| Challenge: | Despite recent progress in dialogue evaluation, how to develop automatic metrics remains an open problem. |
| Approach: | They propose a consensus-based framework for dialog evaluation using segment act flows . they propose to crowdsource a large-scale dataset for it to be evaluated . |
| Outcome: | The proposed framework can reach the best or comparable correlation with human evaluation. |
Medico: Towards Hallucination Detection and Correction with Multi-source Evidence Fusion (2024.emnlp-demo)
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| Challenge: | Existing studies show that LLMs can confidently state non-existent facts rather than answering "I don't know". |
| Approach: | They propose a multi-source evidence fusion enhanced hallucination detection and correction framework that fuses evidence from multiple sources and iteratively revises the hallucinous content. |
| Outcome: | The proposed framework detects whether the generated content contains factual errors, provides the rationale behind the judgment, and iteratively revises the hallucinated content. |
Exploring Fine-Grained Human Motion Video Captioning (2025.coling-main)
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Bingchan Zhao, Xinyi Liu, Zhuocheng Yu, Tongchen Yang, Yifan Song, Mingyu Jin, Sujian Li, Yizhou Wang
| Challenge: | Existing video captioning models fail to capture nuanced semantics of videos . existing models generate coarse descriptions of human motions, resulting in poor quality . |
| Approach: | They construct a fine-grained human motion video captioning dataset named BoFiT and a model that generates fine-grain descriptions of human motions via prompting. |
| Outcome: | The proposed model outperforms existing models on comprehensive metrics. |
InfiAgent: An Infinite-Horizon Framework for General-Purpose Autonomous Agents (2026.findings-acl)
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| Challenge: | Existing LLMs break down on long-horizon tasks due to unbounded context growth and accumulated errors. |
| Approach: | They propose a framework that externalizes persistent state into a file-centric state abstraction and keeps the agent’s reasoning context strictly bounded regardless of task duration. |
| Outcome: | Experiments on DeepResearch and an 80-paper literature review show that the proposed framework maintains higher long-horizon coverage than baseline models without task-specific fine-tuning. |
Beyond the Safety Tax: Mitigating Unsafe Text-to-Image Generation via External Safety Rectification (2026.findings-acl)
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| Challenge: | Existing safety defenses typically intervene internally within the generative model, but suffer from severe concept entanglement, leading to degradation of benign generation quality. |
| Approach: | They propose a structurally isolated safety module that performs external, interpretable rectification without modifying the base model. |
| Outcome: | The proposed module performs external, interpretable rectification without modifying the base model. |
Formality Style Transfer with Shared Latent Space (2020.coling-main)
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| Challenge: | Existing approaches for formality style transfer use neural networks for sentence generation, but the dataset for formal style transfer is considerably smaller than translation corpora. |
| Approach: | They propose a new approach for formality style transfer using shared latent space and two auxiliary losses. |
| Outcome: | The proposed approach outperforms baselines in various settings, especially when limited data is available. |
Glancing Transformer for Non-Autoregressive Neural Machine Translation (2021.acl-long)
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| Challenge: | Existing non-autoregressive neural machine translation methods are either inferior to Transformer or require multiple decoding passes, leading to reduced speedup. |
| Approach: | They propose a Glancing Language Model (GLM) for single-pass parallel generation models and Glancing Transformer (GLAT) with only single- pass decoding, GLAT is able to generate high-quality translation with 8-15 speedup. |
| Outcome: | The proposed model outperforms all previous non-autoregressive methods on multiple language directions and is nearly comparable to Transformer. |
RARE: Retrieval-Augmented Reasoning Enhancement for Large Language Models (2025.acl-long)
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Hieu Tran, Zonghai Yao, Zhichao Yang, Junda Wang, Yifan Zhang, Shuo Han, null Feiyun Ouyang, Hong Yu
| Challenge: | Existing work aims to improve reasoning accuracy and factual integrity across large language models for knowledge-intensive tasks such as medical and commonsense reasoning. |
| Approach: | They propose a versatile extension to the mutual reasoning framework (rStar) that enhances reasoning accuracy and factual integrity across large language models. |
| Outcome: | The proposed extension to the mutual reasoning framework improves reasoning accuracy and factual integrity across large language models for complex, knowledge-intensive tasks. |
Rethinking Entropy Interventions in RLVR: An Entropy Change Perspective (2026.acl-long)
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Zhezheng Hao, Hong Wang, Haoyang Liu, Jian Luo, Jiarui Yu, Hande Dong, Qiang Lin, Can Wang, Jiawei Chen
| Challenge: | Large Language Models (LLMs) have remarkable reasoning capabilities in complex tasks such as mathematics and coding. |
| Approach: | They propose an entropy-modulation method that adaptively reweighs tokens based on theoretically-estimated entropic variations. |
| Outcome: | The proposed method outperforms state-of-the-art methods in six mathematical reasoning and three coding benchmarks. |
A Co-Matching Model for Multi-choice Reading Comprehension (P18-2)
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| Challenge: | Existing approaches to machine comprehension are based on pairwise sequence matching, but this approach is not suitable for multi-choice reading comprehension since questions and answers are often equally important. |
| Approach: | They propose a co-matching approach that models whether a passage can match both a question and a candidate answer using a dataset from Chinese exams. |
| Outcome: | The proposed approach achieves state-of-the-art on the RACE dataset from Chinese middle and high school English examinations. |
PersonaAgent: Bridging Memory and Action for Personalized LLM Agents (2026.findings-acl)
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Weizhi Zhang, Xinyang Zhang, Chenwei Zhang, Liangwei Yang, Jingbo Shang, Zhepei Wei, Henry Peng Zou, Zijie Huang, Zhengyang Wang, Yifan Gao, Xiaoman Pan, Lian Xiong, Jingguo Liu, Philip S. Yu, Xian Li
| Challenge: | Existing Large Language Model (LLM) enabled agents lack flexibility to respond to users’ varying needs and preferences. |
| Approach: | They propose a test-time user-preference alignment strategy that optimizes the persona prompt, ensuring real-time preference alignment through textual loss feedback between simulated and ground-truth responses. |
| Outcome: | The proposed framework outperforms baseline methods in real-time and in real applications. |
Automatic Evaluation of Attribution by Large Language Models (2023.findings-emnlp)
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| Challenge: | Generative large language models (LLMs) incorporate external references to generate and support claims. however, evaluating the attribution remains an open problem. |
| Approach: | They investigate automatic evaluation of attribution given by large language models . they define different types of attributed errors and then explore two approaches . |
| Outcome: | The proposed methods highlight promising signals and challenges. |
Unsupervised Slot Schema Induction for Task-oriented Dialog (2022.naacl-main)
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| Challenge: | Defining task-specific schemas is the first step of building a task-oriented dialog system. |
| Approach: | They propose an unsupervised approach for slot schema induction from unlabeled dialog corpora using in-domain language models and unsupervised parsing structures. |
| Outcome: | The proposed method shows significant performance improvement on multi-domain and SGD datasets. |
Joint Optimization of Training Data and Policy in RLHF (2026.findings-acl)
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Zhuohao Yu, Jiali Zeng, Weizheng Gu, Mengyuan Sun, Yidong Wang, Fandong Meng, Jie Zhou, Shikun Zhang, Wei Ye
| Challenge: | JODP optimizes policies on fixed training inputs, limiting the diversity of learning signals. |
| Approach: | They propose a framework where policy generates improved variants of training problems to enhance its own learning. |
| Outcome: | The proposed framework improves on safety alignment tasks by allowing 4B models to reach 8B model performance with less than 1% additional computational overhead. |
When Compression Meets Model Compression: Memory-Efficient Double Compression for Large Language Models (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) exhibit excellent performance in various tasks, but memory requirements present a challenge when deploying on memory-limited devices. |
| Approach: | They propose a framework to compress LLM after quantization further, achieving about 2.2x compression ratio. |
| Outcome: | The proposed model can achieve 40% reduction in memory size with negligible loss in accuracy and inference speed. |
DA3: A Distribution-Aware Adversarial Attack against Language Models (2024.emnlp-main)
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| Challenge: | Recent attacks have shown that adversarial examples have a different data distribution than the original examples, reducing their effectiveness under detection methods. |
| Approach: | They propose a distribution-aware adversarial attack method that considers the distribution shifts of adversarials to improve attacks’ effectiveness under detection methods. |
| Outcome: | The proposed method improves the effectiveness of adversarial examples under detection methods and integrates both ASR and detectability. |
Salience Allocation as Guidance for Abstractive Summarization (2022.emnlp-main)
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Fei Wang, Kaiqiang Song, Hongming Zhang, Lifeng Jin, Sangwoo Cho, Wenlin Yao, Xiaoyang Wang, Muhao Chen, Dong Yu
| Challenge: | Abstractive summarization models implicitly learn to capture the salient information from scratch. |
| Approach: | They propose a method that uses salience expectation to guide abstractive summarization by averaging salient content to a fixed threshold. |
| Outcome: | The proposed method can be easily adapted to documents with various abstractiveness and achieves high performance. |
WXImpactBench: A Disruptive Weather Impact Understanding Benchmark for Evaluating Large Language Models (2025.findings-acl)
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| Challenge: | Climate change adaptation requires the understanding of disruptive weather impacts on society. |
| Approach: | They propose a large language model to evaluate the capacity of LLMs on disruptive weather impacts by using a four-stage construction pipeline. |
| Outcome: | The proposed model is based on a four-stage well-crafted construction pipeline and requires two evaluation tasks, multi-label classification and ranking-based question answering. |
GALLa: Graph Aligned Large Language Models for Improved Source Code Understanding (2025.acl-long)
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| Challenge: | Programming languages have rich semantics that are represented by graphs and not available from the surface form of source code. |
| Approach: | They propose to use graph neural networks and cross-modal alignment technologies to inject structural information of code into LLMs as an auxiliary task during finetuning. |
| Outcome: | The proposed framework improves on five code tasks with six different baseline LLMs, while incurring no cost at inference time. |
MathAgent: Leveraging a Mixture-of-Math-Agent Framework for Real-World Multimodal Mathematical Error Detection (2025.acl-industry)
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| Challenge: | Multimodal Large Language Models (MLLMs) struggle with identifying and categorizing student errors in multimodal mathematical contexts. |
| Approach: | They propose a new framework that decomposes error detection into three phases with specialized agents. |
| Outcome: | The proposed framework shows higher accuracy in error step identification and 3% improvement in error categorization on real-world educational data. |
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. |
LEPO: Latent Reasoning Policy Optimization for Large Language Models (2026.findings-acl)
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| Challenge: | Existing latent reasoning methods that use chain of thought (CoT) are limited to selecting one discrete token at each reasoning step, which potentially induces information loss. |
| Approach: | They propose a framework that injects controllable stochasticity into latent reasoning via Gumbel-Softmax, restoring LLMs' exploratory capacity and enhancing their compatibility with Reinforcement Learning (RL). |
| Outcome: | The proposed framework preserves richer information for more comprehensive reasoning and is compatible with Reinforcement Learning (RL). |
Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models (2024.findings-acl)
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Ran Xu, Hejie Cui, Yue Yu, Xuan Kan, Wenqi Shi, Yuchen Zhuang, May Dongmei Wang, Wei Jin, Joyce Ho, Carl Yang
| Challenge: | Clinical natural language processing (NLP) is a subfield that requires the extraction, analysis, and interpretation of unstructured clinical text. |
| Approach: | They propose a model which infuses knowledge into clinical text generation with LLMs for clinical NLP tasks. |
| Outcome: | The proposed model improves performance across 8 clinical NLP tasks and 18 datasets by 7.7%-8.7% on average. |
Development of Community-Oriented Text-to-Speech Models for Māori ‘Avaiki Nui (Cook Islands Māori) (2024.lrec-main)
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Jesin James, Rolando Coto-Solano, Sally Akevai Nicholas, Joshua Zhu, Bovey Yu, Fuki Babasaki, Jenny Tyler Wang, Nicholas Derby
| Challenge: | Text-to-speech synthesis is used to transform text into a synthesized voice for a specific language. |
| Approach: | They describe the development of a text-to-speech system for Mori ‘Avaiki Nui (Cook Islands Mi) they used two approaches to train the system, the HMM-system MaryTTS and the deep learning system FastSpeech2 . |
| Outcome: | The proposed system is based on the HMM-system MaryTTS and the deep learning system FastSpeech2 . the ground truth voice had the highest quality, but the fastspeech 2 voice had a significantly higher quality than the MaryTTs synthesized recordings. |
RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Records (2024.acl-short)
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| Challenge: | Existing deep learning models for EHRs rely on knowledge from a single source and do not capture the semantic information for medical codes. |
| Approach: | They propose a Retrieval AugMentation pipeline to augment clinical prediction on EHRs . they use multiple knowledge sources to convert them into text and use consistency regularization to capture complementary information from patient visits and summarized knowledge. |
| Outcome: | Experiments on two EHR datasets show that RAM-EHR improves clinical prediction tasks. |
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. |
MIND: Multimodal Shopping Intention Distillation from Large Vision-language Models for E-commerce Purchase Understanding (2024.emnlp-main)
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Baixuan Xu, Weiqi Wang, Haochen Shi, Wenxuan Ding, Huihao Jing, Tianqing Fang, Jiaxin Bai, Xin Liu, Changlong Yu, Zheng Li, Chen Luo, Qingyu Yin, Bing Yin, Long Chen, Yangqiu Song
| Challenge: | Existing methods for acquiring large-scale intentions generate product-centric intentions without product images and incur high costs for scalability. |
| Approach: | They propose a multimodal framework that allows Large Vision-Language Models to infer purchase intentions from multimodal product metadata and prioritize human-centric ones. |
| Outcome: | The proposed framework shows that it is robust to different prompts and superior to previous methods. |
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%. |
TSPO: Breaking the Double Homogenization Dilemma in Multi-turn Search Policy Optimization (2026.findings-acl)
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Shichao Ma, Zhiyuan Ma, Ming Yang, Xiaofan Li, Xing Wu, Jintao Du, Yu Cheng, Weiqiang Wang, Qiliang Liu, Zhengyang Zhou, Yang Wang
| Challenge: | Large Language Models (LLMs) can solve complex tasks through iterative information retrieval. |
| Approach: | They propose a turn-level stage-aware policy optimization approach to solve this problem . they introduce a first-occurrence latent reward mechanism to allocate partial rewards . |
| Outcome: | Experiments show that TSPO outperforms state-of-the-art models on Qwen2.5-3B and 7B models. |
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models (2022.emnlp-main)
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Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, Tao Yu
| Challenge: | Structured knowledge grounding (SKG) uses structured knowledge to complete user requests . since inputs and outputs of SKG tasks are heterogeneous, they have been studied separately . |
| Approach: | They propose a framework that unifies 21 SKG tasks into a text-to-text format . they use unifiedSKG to benchmark T5 with different sizes . |
| Outcome: | The proposed framework unifies 21 SKG tasks into a text-to-text format . it achieves state-of-the-art performance on almost all of the 21 tasks, the authors show . |
Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing (N19-1)
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| Challenge: | Existing entity typing systems exploit type hierarchy provided by KB schema to model label correlations. |
| Approach: | They propose a graph layer that encodes global label co-occurrence statistics and word-level similarities. |
| Outcome: | The proposed model achieves a 15.3% relative F1 improvement on a large dataset with over 10,000 free-form types. |
AlignMMBench: Evaluating Chinese Multimodal Alignment in Large Vision-Language Models (2025.acl-long)
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| Challenge: | Existing benchmarks focus on basic abilities using nonverbal methods, such as yes-no and multiple-choice questions. |
| Approach: | They propose a benchmark that provides more nuanced evaluations of alignment capabilities for large Vision-Language Models (VLMs) they use a rule-calibrated evaluator that exceeds GPT-4's evaluation ability and a “alignment score” to assess the robustness and stability of models across diverse prompts. |
| Outcome: | The proposed benchmark covers 13 tasks across three categories and includes both single-turn and multi-turn dialogue scenarios. |
Dunhuang-Bench: How Well Do MLLMs Understand Cultural Heritage? (2026.findings-acl)
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| Challenge: | Recent advances in Multimodal Large Language Models (MLLMs) have led to extensive evaluations on Chinese cultural benchmarks. |
| Approach: | They construct a large-scale benchmark comprising 486 images and 22,970 QA pairs to evaluate MLLMs' cultural understanding. |
| Outcome: | The proposed benchmark incorporates three task formats to evaluate MLLMs’ cultural understanding: Question Answering with Text Description, Multi-turn Dialogue, and Question Answers with Choices. |
RAST: Domain-Robust Dialogue Rewriting as Sequence Tagging (2021.emnlp-main)
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| Challenge: | Existing models for dialogue rewriting suffer from the robustness issue, i.e., performances drop dramatically when testing on a different dataset. |
| Approach: | They propose a sequence-tagging-based approach that reduces the search space while preserving the core of the task. |
| Outcome: | The proposed model significantly reduces the search space while still covering the core of the task. |
Knowledge-grounded Dialog State Tracking (2022.findings-emnlp)
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| Challenge: | Structured knowledge is encoded implicitly into model parameters for downstream tasks, making training inefficient. |
| Approach: | They propose to perform dialog state tracking grounded on knowledge encoded externally. |
| Outcome: | The proposed method outperforms baseline models in the few-shot learning setting. |
Flexibly Utilize Memory for Long-Term Conversation via a Fragment-then-Compose Framework (2025.emnlp-main)
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Cai Ke, Yiming Du, Bin Liang, Yifan Xiang, Lin Gui, Zhongyang Li, Baojun Wang, Yue Yu, Hui Wang, Kam-Fai Wong, Ruifeng Xu
| Challenge: | Large language models extract useful information from conversation history to enhance the response in long-term conversations. |
| Approach: | They propose a Fragment-then-Compose framework to optimize memory utilization for long-term open-domain conversation. |
| Outcome: | The proposed framework can be used to extract useful information from conversation history . it can be adapted to different situations and improve response generation . |
Lang2Act: Fine-Grained Visual Reasoning through Self-Emergent Linguistic Toolchains (2026.findings-acl)
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| Challenge: | Existing frameworks depend on rigid, pre-defined external tools to extend perceptual capabilities of VLMs. |
| Approach: | They propose a framework that leverages self-emergent linguistic toolchains to enhance visual perception and reasoning. |
| Outcome: | The proposed framework improves the visual perception capabilities of large language models by incorporating external visual documents to address a given query. |
SemAttack: Natural Textual Attacks via Different Semantic Spaces (2022.findings-naacl)
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| Challenge: | Existing approaches to attack pre-trained language models suffer from low success rates or fail to search efficiently in the exponentially large perturbation space. |
| Approach: | They propose an efficient framework to generate natural adversarial text by constructing different semantic perturbation functions. |
| Outcome: | The proposed framework generates natural adversarial texts for different languages with high success rates. |
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation (2022.findings-emnlp)
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| Challenge: | Existing frameworks that share entity embeddings of knowledge graphs (KGs) would incur a severe privacy leakage. |
| Approach: | They propose a new attack method that aims to recover the original embedding information based on the known entity embeddables of FedE. |
| Outcome: | The proposed framework can be used to infer whether a specific relation exists in a private client. |
latent-GLAT: Glancing at Latent Variables for Parallel Text Generation (2022.acl-long)
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| Challenge: | Recent advances in text generation have limited applications due to multimodality problem. |
| Approach: | They propose a method which uses latent variables to capture word categorical information and invoke an advanced curriculum learning technique to overcome multi-modality problem. |
| Outcome: | The proposed method outperforms strong baselines without an autoregressive model, which further broadens the application scenarios of the parallel decoding paradigm. |
Vision-Flan: Scaling Human-Labeled Tasks in Visual Instruction Tuning (2024.findings-acl)
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Zhiyang Xu, Chao Feng, Rulin Shao, Trevor Ashby, Ying Shen, Di Jin, Yu Cheng, Qifan Wang, Lifu Huang
| Challenge: | Recent vision-language models (VLMs) have shown impressive capabilities as general visual assistants, but there are two challenges to their performance: (1) lacking task diversity in pretraining and visual instruction tuning; (2) annotation error and bias in GPT-4 synthesized instruction tuning data. |
| Approach: | They propose a two-stage instruction tuning framework that fine tunes VLMs firstly and further tuned on GPT-4 synthesized data. |
| Outcome: | The proposed framework outperforms the traditional single-stage visual instruction tuning framework and achieves state-of-the-art performance across a wide range of multi-modal evaluation benchmarks. |
LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error (2024.acl-long)
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| Challenge: | Existing work on tool-augmented LLMs focuses on the broad coverage of tools and the flexibility of adding new tools. |
| Approach: | They propose a biologically inspired method for tool-augmented LLMs that orchestrates three key mechanisms for successful tool use behaviors in the biological system: trial and error, imagination, and memory. |
| Outcome: | The proposed method improves tool learning for LLMs under both in-context learning and fine-tuning settings, bringing a boost of 46.7% to Mistral-Instruct-7B and outperforms GPT-4. |
Does DetectGPT Fully Utilize Perturbation? Bridging Selective Perturbation to Fine-tuned Contrastive Learning Detector would be Better (2024.acl-long)
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Shengchao Liu, Xiaoming Liu, Yichen Wang, Zehua Cheng, Chengzhengxu Li, Zhaohan Zhang, Yu Lan, Chao Shen
| Challenge: | Existing methods to detect MGT from human-written texts are inadequate . existing methods are fine-tuned and zero-shot metric-based, but they can be more accurate. |
| Approach: | They propose a novel fine-tuned detector that can detect MGT from human-written texts by contrastive learning on selective perturbation. |
| Outcome: | The proposed method outperforms the state-of-the-art by 1.20% on four public datasets. |
Large-Scale and Multi-Perspective Opinion Summarization with Diverse Review Subsets (2023.findings-emnlp)
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| Challenge: | Existing methods for opinion summarization are deficient in epitomizing extensive reviews and offering opinion summaries from various angles. |
| Approach: | They propose a supervised opinion summarization framework that takes sentiment orientation into account and trains the summarizer to learn from sub-optimal and optimal review subsets. |
| Outcome: | The proposed framework generates pros, cons, and verdict summaries from hundreds of input reviews. |
CharTide: Data-Centric Chart-to-Code Generation via Tri-Perspective Tuning and Inquiry-Driven Evolution (2026.acl-long)
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Xiangxi Zheng, Kuang He, Jiayi Hu, Ping Yu, Rui Yan, Yuan Yao, Peng Hou, Anxiang Zeng, Alex Jinpeng Wang
| Challenge: | Existing approaches to chart-to-code generation are constrained by data-centric limitations . authors present a new framework that redesigns both training and alignment data . |
| Approach: | They propose a data-centric framework that redesigns both training and alignment data for chart-to-code generation. |
| Outcome: | The proposed framework outperforms open-source baselines and is competitive with GPT-5. |
Exploring Better Text Image Translation with Multimodal Codebook (2023.acl-long)
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| Challenge: | Current studies on text image translation face bottlenecks due to lack of a publicly available dataset and poor optical character recognition. |
| Approach: | They propose a text image translation model with a multimodal codebook and an OCR dataset for Chinese-English translation. |
| Outcome: | The proposed model can associate the image with relevant texts, providing useful supplementary information for translation. |
Skills-in-Context: Unlocking Compositionality in Large Language Models (2024.findings-emnlp)
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| Challenge: | eliciting compositional generalization capabilities in large language models is challenging for advanced LLMs because they lack foundational skills and compositional examples in the same prompt context. |
| Approach: | They propose to use compositional generalization capabilities in large language models to elicit compositional skills in a prompt context. |
| Outcome: | The proposed structure enables LLMs to tackle more challenging problems with as few as two exemplars and unlocks their latent potential. |
Thinking Traps in Long Chain-of-Thought: A Measurable Study and Trap-Aware Adaptive Restart (2026.findings-acl)
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null Chenkang, Fan Yu, Junjie Nian, Sihan Zhao, Zhuoka Feng, Zijun Yao, Wang Heng, Yu Minshen, Yixin Cao
| Challenge: | Experiments show that extended generation does not guarantee correctness . a recurring pattern in Long-CoT failures is a problem for large reasoning models . |
| Approach: | They propose a test-time control framework that truncates the trajectory before the trap segment and adaptively restarts decoding. |
| Outcome: | Experiments show that TAAR improves reasoning performance without fine-tuning model parameters. |
Few-Shot Class-Incremental Learning for Named Entity Recognition (2022.acl-long)
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| Challenge: | Existing models of Named Entity Recognition (NER) are trained on large datasets with predefined entity classes, but data of new classes arrives constantly. Existing work on NER relies on the assumption that there exists abundance of labeled data for the training of new class. |
| Approach: | They propose a few-shot class-incremental learning problem where NER model is trained with only few labeled samples of the new classes without forgetting knowledge of the old ones. |
| Outcome: | The proposed model improves over existing baselines by reconstructing training data of old classes and real data from the training set. |
Unveiling Attractor Cycles in Large Language Models: A Dynamical Systems View of Successive Paraphrasing (2025.acl-long)
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| Challenge: | Dynamical systems theory provides a framework for understanding iterative processes and evolution over time. |
| Approach: | They propose to apply this perspective to large language models which iteratively map input text to output text and re-express meaning with linguistic variation. |
| Outcome: | The proposed model reveals that paraphrases re-express meaning with linguistic variation limiting linguistic diversity . |
Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models (2025.findings-emnlp)
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Haibo Wang, Zhiyang Xu, Yu Cheng, Shizhe Diao, Yufan Zhou, Yixin Cao, Qifan Wang, Weifeng Ge, Lifu Huang
| Challenge: | Video Large Language Models (VLMs) have been praised for their performance in coarse-grained video understanding but still face ineffective temporal grounding and inadequate timestamp representations. |
| Approach: | They propose a novel Video-LLM that senses and reasoned over specific video moments with fine-grained temporal precision. |
| Outcome: | The proposed model surpasses existing models in fine-grained video understanding tasks and exhibits strong potential as a general video understanding assistant. |
MR-ALIGN: Meta-Reasoning Informed Factuality Alignment for Large Reasoning Models (2026.findings-acl)
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Xinming Wang, Jian Xu, Bin Yu, Sheng Lian, yi Chen, Boran Wang, Yingjian Zhu, Hongzhu Yi, Hong-Ming Yang, Han Hu, Cheng-Lin Liu, Xu-Yao Zhang
| Challenge: | Large reasoning models (LRMs) show strong capabilities in complex reasoning, yet their marginal gains on evidence-dependent factual questions are limited. |
| Approach: | They propose a Meta-Reasoning informed alignment framework that quantifies state-transition probabilities along the model’s thinking process and constructs a transition-aware implicit reward that reinforces beneficial reasoning patterns while suppressing defective ones at the atomic thinking segments. |
| Outcome: | Empirical evaluations of four factual QA datasets and one long-form factuality benchmark show that MR-ALIGN consistently improves accuracy and truthfulness while reducing misleading reasoning. |
Document-level Relation Extraction with Dual-tier Heterogeneous Graph (2020.coling-main)
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| Challenge: | Existing methods focus on extracting relations from single sentence . document-level relation extraction requires a comprehension of the whole document . |
| Approach: | They propose a graph-based model with Dual-tier Heterogeneous Graph (DHG) for document-level relation extraction. |
| Outcome: | The proposed model achieves state-of-the-art performance on two widely used datasets. |
Inference-Time Scaling of Verification: Self-Evolving Deep Research Agents via Test-Time Rubric-Guided Verification (2026.findings-acl)
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Yuxuan Wan, Tianqing Fang, Zaitang LI, Yintong Huo, Wenxuan Wang, Haitao Mi, Dong Yu, Michael R. Lyu
| Challenge: | Recent advances in Deep Research Agents (DRAs) are transforming automated knowledge discovery and problem-solving. |
| Approach: | They propose an inference-time scaling of verification wherein an agent self-improves at test time by evaluating its generated answers. |
| Outcome: | The proposed model outperforms vanilla agent-as-judge and LLM judge baselines by 12%–48% in meta-evaluation F1 score. |
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. |
UNO Arena for Evaluating Sequential Decision-Making Capability of Large Language Models (2024.emnlp-main)
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Zhanyue Qin, Haochuan Wang, Deyuan Liu, Ziyang Song, Cunhang Fan, Zhao Lv, Jinlin Wu, Zhen Lei, Zhiying Tu, Dianhui Chu, Xiaoyan Yu, Dianbo Sui
| Challenge: | Existing LLMs demonstrate powerful capabilities between tasks, but can they make sequential decisions? |
| Approach: | They propose to evaluate sequential decision-making capability of large language models (LLMs) using novel metrics based Monte Carlo methods. |
| Outcome: | The proposed benchmark improves sequential decision-making performance compared to the vanilla LLM player. |
Dynamic Collaboration of Multi-Language Models based on Minimal Complete Semantic Units (2025.emnlp-main)
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| Challenge: | Existing methods to enhance reasoning capabilities of language models are expensive and often lack the ability to perform complex reasoning tasks. |
| Approach: | They propose a token-level multi-model collaboration strategy to enhance reasoning capabilities in language models by selecting the optimal tokens from the next token distributions. |
| Outcome: | The proposed method is superior to existing methods and will be released soon. |
MA-BERT: Learning Representation by Incorporating Multi-Attribute Knowledge in Transformers (2021.findings-acl)
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| Challenge: | Existing methods for incorporating external attribute knowledge into deep neural networks are concatenating multiple attributes to word/text representation or treating them as biases to adjust attention distribution. |
| Approach: | They propose a multi-attribute BERT to incorporate external attribute knowledge into deep neural networks. |
| Outcome: | The proposed method outperforms existing models and models on three benchmark datasets. |
RCBSF: A Multi-Agent Framework for Automated Contract Revision via Stackelberg Game (2026.findings-acl)
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| Challenge: | despite the adoption of Large Language Models (LLMs), contract revision remains impeded because generic models treat strict legal constraints as mere suggestions. |
| Approach: | They propose a risk-constrained bilevel Stackelberg framework that models high-stakes revision as a strategic interaction rather than an open-ended conversation. |
| Outcome: | The proposed framework achieves state-of-the-art performance with an average RRR of 84.21% and enhanced token efficiency. |
LEAF: Learning and Evaluation Augmented by Fact-Checking to Improve Factualness in Large Language Models (2025.emnlp-industry)
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| Challenge: | Large language models (LLMs) struggle with factual accuracy in knowledge-intensive domains like healthcare. |
| Approach: | They propose a framework for improving LLM factuality in medical question answering . RAFE, Fact-Check-then-RAG and Learning from Fact Check are components . |
| Outcome: | Experimental results show that LEAF outperforms Factcheck-GPT in detecting inaccuracies and corrects errors without labeling . the framework provides a scalable solution for industrial applications requiring high factuality scores. |
ManCC: A Task-Anchored Benchmark for Manchu–Classical Chinese Cross-Lingual Modeling (2026.findings-acl)
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Meiqi Wang, Xiaoxin Sun, Dongjie Wang, Ruixin Yu, Xiantao Heng, Shuo Wang, Zhen Huang, Peng Zhao, Suhua Wang, Minghao Yin
| Challenge: | Mainstream research in natural language processing has focused on high-resource and modern languages. |
| Approach: | They propose a task-anchored benchmark for Manchu–Classical Chinese translation . they use a parallel corpus of 16,627 sentence pairs to evaluate the model . |
| Outcome: | The proposed benchmarks show that linguistic differences influence performance and broader language coverage facilitate low-resource transfer. |
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. |
How do Visual Attributes Influence Web Agents? A Comprehensive Evaluation of User Interface Design Factors (2026.findings-acl)
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| Challenge: | Existing studies on the effect of environmental variation on web agents have focused on robustness to adversarial attacks with less attention to agents’ preferences in benign scenarios. |
| Approach: | They propose a controlled evaluation pipeline to quantify how visual attributes influence web-agent decision-making by comparing variants and browsing interactions. |
| Outcome: | Extensive experiments on 8 variant families, 5 real-world websites and 4 representative web agents show that background color contrast, item size, position, and card clarity have a strong influence on agents’ actions, whereas font styling, text color, and item image clarity exhibit minor effects. |
RuleArena: A Benchmark for Rule-Guided Reasoning with LLMs in Real-World Scenarios (2025.acl-long)
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| Challenge: | RuleArena assesses the ability of large language models (LLMs) to follow complex, real-world rules in reasoning. |
| Approach: | They propose a benchmark to evaluate the ability of large language models (LLMs) to follow complex, real-world rules in reasoning. |
| Outcome: | The proposed benchmark covers airline baggage fees, NBA transactions, and tax regulations. |
A Framework of Reflective Agents with Adaptive Collaboration for Attributed Summary Generation (2026.findings-acl)
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| Challenge: | Existing prompt-based summarization approaches face limitations such as positional preference, poor citation quality and sensitivity to uninformative documents. |
| Approach: | They propose a framework of Reflective Agents with Adaptive Collaboration for attributed summarization that performs iterative summarizing via reflective agents’ collaboration. |
| Outcome: | The proposed framework outperforms baselines on the ALCE benchmark in factual correctness and citation quality. |
HeteroRAG: A Heterogeneous Retrieval-Augmented Generation Framework for Medical Vision Language Tasks (2026.findings-acl)
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| Challenge: | Medical large vision-language models suffer from factual inaccuracies and unreliable outputs. |
| Approach: | They propose a framework that enhances Med-LVLMs through heterogeneous knowledge sources. |
| Outcome: | The proposed framework improves Med-LVLMs through heterogeneous knowledge sources. |
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. |
AceGPT, Localizing Large Language Models in Arabic (2024.naacl-long)
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Huang Huang, Fei Yu, Jianqing Zhu, Xuening Sun, Hao Cheng, Song Dingjie, Zhihong Chen, Mosen Alharthi, Bang An, Juncai He, Ziche Liu, Junying Chen, Jianquan Li, Benyou Wang, Lian Zhang, Ruoyu Sun, Xiang Wan, Haizhou Li, Jinchao Xu
| Challenge: | Significant concerns emerge when addressing cultural sensitivity and local values. |
| Approach: | They propose a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models. |
| Outcome: | The proposed model sets the state-of-the-art standard for open Arabic LLMs across various benchmarks. |
TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in Large Language Models (2024.acl-long)
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| Challenge: | Grasping the concept of time is a fundamental facet of human cognition. |
| Approach: | They propose a hierarchical temporal reasoning benchmark that covers a broad spectrum of temporal phenomena. |
| Outcome: | The proposed benchmark shows that state-of-the-art LLMs are still far behind humans in temporal reasoning . |
PAR: Political Actor Representation Learning with Social Context and Expert Knowledge (2022.emnlp-main)
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Shangbin Feng, Zhaoxuan Tan, Zilong Chen, Ningnan Wang, Peisheng Yu, Qinghua Zheng, Xiaojun Chang, Minnan Luo
| Challenge: | Existing approaches focus on textual data and voting records to induce political actors' stances. |
| Approach: | They propose a Political Actor Representation learning framework that leverages social context and expert knowledge to model ideological stances. |
| Outcome: | The proposed framework improves political text understanding and improves roll call vote prediction and political perspective detection. |
Theorem-Validated Reverse Chain-of-Thought Problem Generation for Geometric Reasoning (2025.emnlp-main)
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Deng Linger, Linghao Zhu, Yuliang Liu, Yu Wang, Qunyi Xie, Jingjing Wu, Gang Zhang, Yingying Zhu, Xiang Bai
| Challenge: | Existing methods for generating geometric reasoning data through Chain-of-Thought (CoT) frameworks face three fundamental limitations: 1) lack of high-quality annotations and domain-specific expertise to ensure theorem-grounded diagrams. 2) lack of a coherent model; 3) lack of coherent model. |
| Approach: | They propose a two-stage Theorem-Validated Reverse Chain-of-Thought Reasoning Synthesis framework that synthesizes theorematic diagrams with structured descriptions and properties. |
| Outcome: | The proposed framework expands theorem-type coverage, corrects misunderstandings, and enhances geometric reasoning. |
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. |
MBTI Personality Prediction for Fictional Characters Using Movie Scripts (2022.findings-emnlp)
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| Challenge: | Existing NLP models cannot predict character's personality types based on text classifications . character comprehension is the cornerstone of understanding stories in psychology and education. |
| Approach: | They propose a benchmark to predict movie character's MBTI or Big 5 personality types based on the narratives of the character. |
| Outcome: | The proposed model outperforms existing models in the task and is more accurate than random guesses. |
Inference Compute-Optimal Video Vision Language Models (2025.acl-long)
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| Challenge: | Using video vision language models, inference costs are often more expensive than finetuning. |
| Approach: | They investigate the optimal allocation of inference compute across three key scaling factors in video vision language models. |
| Outcome: | The proposed model configurations are based on three key scaling factors . the results can be applied to real-world tasks and tasks with fixed inference budgets. |
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. |
Evaluating Robustness of Generative Search Engine on Adversarial Factoid Questions (2024.findings-acl)
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Xuming Hu, Xiaochuan Li, Junzhe Chen, Yinghui Li, Yangning Li, Xiaoguang Li, Yasheng Wang, Qun Liu, Lijie Wen, Philip Yu, Zhijiang Guo
| Challenge: | Existing large language models (LLMs)-backed generative search engines may not always be accurate. |
| Approach: | They propose to evaluate the robustness of retrieval-augmented generation in a realistic and high-risk setting where adversaries have only black-box system access. |
| Outcome: | The proposed model exhibits higher susceptibility to factual errors compared to LLMs without retrieval. |
QAEncoder: Towards Aligned Representation Learning in Question Answering Systems (2025.acl-long)
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Zhengren Wang, Qinhan Yu, Shida Wei, Zhiyu Li, Feiyu Xiong, Xiaoxing Wang, Simin Niu, Hao Liang, Wentao Zhang
| Challenge: | Modern QA systems entail retrieval-augmented generation (RAG) for accurate and trustworthy responses, but the inherent gap between user queries and relevant documents hinders precise matching. |
| Approach: | They propose a retrieval-augmented generation (RAG)-based approach to bridge this gap by attaching document fingerprints to the embedding to estimate the expectation of potential queries. |
| Outcome: | Experiments across diverse datasets, languages, and embedding models confirm the proposed solution is simple-yet-effective with zero additional index storage, retrieval latency, training costs, or catastrophic forgetting and hallucination issues. |
Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation (2021.findings-emnlp)
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| Challenge: | Quality Estimation (QE) is an essential role in applications of Machine Translation (MT). |
| Approach: | They propose to fuse uncertainty quantification into a pre-trained cross-lingual language model to predict the translation quality. |
| Outcome: | The proposed method achieves state-of-the-art on the datasets of WMT 2020 QE shared task. |
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. |
Large Language Models Can Self-Improve (2023.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have excellent performance in various tasks, but fine-tuning requires extensive supervision. |
| Approach: | They propose to use a pre-trained Large Language Model to generate rationale-augmented answers for unlabeled questions and fine-tune the LLM using those self-generated solutions as target outputs. |
| Outcome: | The proposed approach improves the general reasoning ability of a 540B-parameter LLM without any ground truth label. |
MixLLM: Dynamic Routing in Mixed Large Language Models (2025.naacl-long)
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Xinyuan Wang, Yanchi Liu, Wei Cheng, Xujiang Zhao, Zhengzhang Chen, Wenchao Yu, Yanjie Fu, Haifeng Chen
| Challenge: | Large Language Models (LLMs) exhibit potential artificial generic intelligence, however, their usage is costly with high response latency. |
| Approach: | They develop a dynamic contextual-bandit-based routing system for query-LLM assignment that leverages query tags to enhance query embeddings. |
| Outcome: | The proposed model maximizes response quality and minimizes cost and latency. |
Position: LLMs Can be Good Tutors in English Education (2025.emnlp-main)
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Jingheng Ye, Shen Wang, Deqing Zou, Yibo Yan, Kun Wang, Hai-Tao Zheng, Ruitong Liu, Zenglin Xu, Irwin King, Philip S. Yu, Qingsong Wen
| Challenge: | Recent efforts to integrate large language models into English education lack adaptability to language learning. |
| Approach: | They argue that large language models can be effective tutors in English education . they encourage interdisciplinary research to explore these roles, fostering innovation and risks . |
| Outcome: | The proposed models can play three critical roles: 1) as data enhancers, 2) as task predictors, 3) as agents, enabling personalized and inclusive education. |
IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce (2024.findings-emnlp)
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Wenxuan Ding, Weiqi Wang, Sze Kwok, Minghao Liu, Tianqing Fang, Jiaxin Bai, Xin Liu, Changlong Yu, Zheng Li, Chen Luo, Qingyu Yin, Bing Yin, Junxian He, Yangqiu Song
| Challenge: | Existing approaches that distill intentions from LMs fail to generate meaningful and human-centric intentions applicable in real-world E-commerce contexts. |
| Approach: | They propose a double-task multiple-choice question answering benchmark to evaluate LMs' comprehension of purchase intentions in E-commerce. |
| Outcome: | The proposed benchmark consists of 4,360 carefully curated problems across three difficulty levels, constructed using an automated pipeline to ensure scalability on large E-commerce platforms. |
Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future (2024.acl-long)
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Zheng Chu, Jingchang Chen, Qianglong Chen, Weijiang Yu, Tao He, Haotian Wang, Weihua Peng, Ming Liu, Bing Qin, Ting Liu
| Challenge: | Recent studies have revealed that chain-of-thought prompting significantly enhances LLM’s reasoning capabilities, which attracts widespread attention from both academics and industry. |
| Approach: | They propose to summarize advanced methods through a taxonomy that offers novel perspectives. |
| Outcome: | The proposed method delineates the challenges and future directions, thereby shedding light on future research. |
Going Beyond Sentence Embeddings: A Token-Level Matching Algorithm for Calculating Semantic Textual Similarity (2023.acl-short)
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| Challenge: | Semantic Textual Similarity (STS) measures the degree to which the underlying semantics of paired sentences are equivalent. |
| Approach: | They propose a token-level matching inference algorithm which can be applied on top of any language model to improve its performance on STS task. |
| Outcome: | The proposed method improves the performance of almost all language models, with up to 12.7% gain in Spearman’s correlation. |
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. |
TWEETQA: A Social Media Focused Question Answering Dataset (P19-1)
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Wenhan Xiong, Jiawei Wu, Hong Wang, Vivek Kulkarni, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang
| Challenge: | Social media is becoming an important realtime information source, especially during natural disasters and emergencies. |
| Approach: | They present a large-scale dataset for question answering over social media data . they gather tweets used by journalists and ask human annotators to write questions upon them . |
| Outcome: | The proposed dataset shows that neural models that perform well on formal texts are limited in their performance . the proposed model is still lagging behind human performance with a large margin . |
Maximal Clique Based Non-Autoregressive Open Information Extraction (2021.emnlp-main)
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| Challenge: | Open Information Extraction (OpenIE) aims to discover textual facts from a given sentence. |
| Approach: | They propose a non-autoregressive framework that generates a fact graph and a graph with an edge linking two nodes that belong to the same fact. |
| Outcome: | The proposed framework outperforms current state-of-the-art methods on two benchmark datasets and significantly outperformed the existing ones. |
Deep Reinforcement Learning for Chinese Zero Pronoun Resolution (P18-1)
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| Challenge: | Recent models for zero pronoun resolution in Chinese are short-sighted and do not capture semantic information for zeros and candidate antecedents. |
| Approach: | They propose to integrate a deep reinforcement learning approach to Chinese zero pronoun resolution. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods in three experimental settings. |
UCS-SQL: Uniting Content and Structure for Enhanced Semantic Bridging In Text-to-SQL (2025.findings-acl)
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Zhenhe Wu, Zhongqiu Li, JieZhangChinaTele JieZhangChinaTele, Zhongjiang He, Jian Yang, Yu Zhao, Ruiyu Fang, Bing Wang, Hongyan Xie, Shuangyong Song, Zhoujun Li
| Challenge: | Existing methods overlook the challenge of effectively transforming structure information from NL to SQL. |
| Approach: | They propose a text-to-SQL framework that unites content and structure pipes to bridge the gap between NL and SQL. |
| Outcome: | The proposed framework bridges the gap between natural language questions and SQL by combining content and structure pipes. |
Understanding and Mitigating Spurious Signal Amplification in Test-Time Reinforcement Learning for Math Reasoning (2026.findings-acl)
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| Challenge: | a framework to mitigate spurious optimization signals is proposed for test-time reinforcement learning (TTRL) Reinforcement learning with verifiable rewards (RLVR) is an effective paradigm for improving large language models on structured challenging reasoning tasks. |
| Approach: | They propose a framework to mitigate spurious optimization signals from label noise . they propose to use a frequency-based sampling strategy to exclude ambiguous samples . |
| Outcome: | The proposed framework outperforms existing TTRL baselines on three large language models across multiple mathematical reasoning benchmarks. |
OpenRubrics: Towards Scalable Synthetic Rubric Generation for Reward Modeling and LLM Alignment (2026.acl-long)
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| Challenge: | Existing reward models rely on scalar or pairwise judgments that fail to capture multifaceted nature of human preferences. |
| Approach: | They propose a rubric-based reward model that uses a large collection of prompt, rubric pairs to generate a scalar score or preference label for each response. |
| Outcome: | The proposed model surpasses strong size-matched baselines by 8.4% across multiple benchmarks. |
From Knowing to Teaching: Scaffolding Pedagogical Decisions for LLM Agent (2026.acl-long)
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Yucheng Wang, Shen Yang, Jifan Yu, Haoxuan Li, Joy Jia Yin Lim, Daniel Zhang-Li, Huiqin Liu, Lei Hou, Juanzi Li, Bin Xu
| Challenge: | Large language models produce content lacking pedagogical depth when asked to generate lessons . |
| Approach: | They propose a framework that allows teachers to select content according to pedagogical intent and sequence topics so foundations precede applications. |
| Outcome: | The framework achieves 67.8% win rate in human evaluation and 79.6% in LLM-based evaluation against eight baselines. |
MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning (2024.naacl-long)
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Fuxiao Liu, Xiaoyang Wang, Wenlin Yao, Jianshu Chen, Kaiqiang Song, Sangwoo Cho, Yaser Yacoob, Dong Yu
| Challenge: | Existing large language models have limited ability to perform tasks effectively. |
| Approach: | They propose a large-scale multimodal chart instruction dataset with 600k instances supporting diverse tasks and chart types. |
| Outcome: | The proposed LMM achieves state-of-the-art performance on existing chart QA benchmarks. |
RichRAG: Crafting Rich Responses for Multi-faceted Queries in Retrieval-Augmented Generation (2025.coling-main)
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| Challenge: | Existing studies focus on question scenarios with clear user intents and concise answers, but it is prevalent that users issue broad, open-ended queries with diverse sub-intents. |
| Approach: | They propose a framework that includes a sub-aspect explorer and a multi-faceted retriever to build a candidate pool of diverse external documents related to these sub-intents. |
| Outcome: | The proposed framework provides comprehensive and satisfying responses to users on two publicly available datasets. |
Large Language Models with Controllable Working Memory (2023.findings-acl)
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Daliang Li, Ankit Singh Rawat, Manzil Zaheer, Xin Wang, Michal Lukasik, Andreas Veit, Felix Yu, Sanjiv Kumar
| Challenge: | Large language models (LLMs) have led to a series of breakthroughs in natural language processing due to the massive amounts of world knowledge they memorize during pretraining. |
| Approach: | They propose a method to inject counterfactual and irrelevant contexts into standard supervised datasets to strengthen both controllability and robustness. |
| Outcome: | The proposed method improves controllability and robustness across model architectures and sizes. |
CharacterGLM: Customizing Social Characters with Large Language Models (2024.emnlp-industry)
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Jinfeng Zhou, Zhuang Chen, Dazhen Wan, Bosi Wen, Yi Song, Jifan Yu, Yongkang Huang, Pei Ke, Guanqun Bi, Libiao Peng, JiaMing Yang, Xiyao Xiao, Sahand Sabour, Xiaohan Zhang, Wenjing Hou, Yijia Zhang, Yuxiao Dong, Hongning Wang, Jie Tang, Minlie Huang
| Challenge: | Character-based dialogue systems (CharacterDial) allow users to customize social characters for social interactions. |
| Approach: | They will collect a large-scale Chinese corpus of characters with diverse categories and behaviors and develop CharacterGLM models to address these challenges. |
| Outcome: | Experiments show that CharacterGLM outperforms most popular open- and closed-source LLMs and performs comparable to GPT-4. |
ERNIE-Code: Beyond English-Centric Cross-lingual Pretraining for Programming Languages (2023.findings-acl)
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| Challenge: | ERNIE-Code is a unified pre-trained language model for 116 NLs and 6 PLs. |
| Approach: | They propose a unified pre-trained language model for 116 NLs and 6 PLs . they employ span-corruption language modeling that learns patterns from monolingual NL or PL . |
| Outcome: | The proposed model outperforms previous multilingual models for NL or NL across end tasks. |
Self-Correcting RAG: Enhancing Faithfulness via MMKP Context Selection and NLI-Guided MCTS (2026.findings-acl)
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| Challenge: | Existing approaches to retrieval-augmented generation still face problems with low context utilization and frequent hallucinations. |
| Approach: | They propose a framework that reformulates retrieval and generation as constrained optimization and path planning. |
| Outcome: | The proposed framework significantly improves reasoning accuracy on complex queries while reducing hallucinations. |
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-Speech (2022.acl-long)
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| Challenge: | Experimental results show that the proposed model improves naturalness and prosody diversity with clear margins. |
| Approach: | They propose a cross-utterance conditional VAE to estimate posterior probability distribution of latent prosody features for each phoneme by conditioning on acoustic features, speaker information, and text features from past and future sentences. |
| Outcome: | The proposed model improves naturalness and prosody diversity with clear margins. |
RAG in the Wild: On the (In)effectiveness of LLMs with Mixture-of-Knowledge Retrieval Augmentation (2026.findings-acl)
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| Challenge: | Retrieval-augmented generation (RAG) enhances large language models by integrating external knowledge retrieved at inference time. |
| Approach: | They evaluate RAG systems using MassiveDS, a large-scale datastore with mixture of knowledge. |
| Outcome: | The proposed approach improves performance on knowledge-intensive NLP tasks. |
HoWToBench: Holistic Evaluation for LLM’s Capability in Human-level Writing using Tree of Writing (2026.acl-long)
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Andrew Zhuoer Feng, Cunxiang Wang, Yu Luo, Lin Fan, Irene Zhou, Zikang Wang, Xiaotao Gu, Jie Tang, Hongning Wang, Minlie Huang
| Challenge: | Evaluating the writing capabilities of large language models remains a significant challenge due to the multidimensional nature of writing skills and the limitations of existing metrics. |
| Approach: | They propose to model the aggregation weights of sub-features in a tree-structured workflow and propose a Chinese writing benchmark that mitigates biases. |
| Outcome: | The proposed tree-of-writing (ToW) measures the writing capabilities of large language models (LLMs) in Chinese and shows that it mitigates biases and achieves a *0.93* Pearson correlation with human judgments. |
LLMs + Persona-Plug = Personalized LLMs (2025.acl-long)
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Jiongnan Liu, Yutao Zhu, Shuting Wang, Xiaochi Wei, Erxue Min, Yu Lu, Shuaiqiang Wang, Dawei Yin, Zhicheng Dou
| Challenge: | Large language models (LLMs) have demonstrated extraordinary capabilities in natural language understanding, generation, and reasoning. |
| Approach: | They propose a plug-and-play LLM model that embeds a user-specific embedding for each individual by modeling her historical contexts through a lightweight plug-in user embedder module. |
| Outcome: | Experiments on various tasks in the language model personalization (LaMP) benchmark show that the proposed model significantly outperforms existing personalized LLM approaches. |
Retrieval-Augmented Process Reward Model for Generalizable Mathematical Reasoning (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have advanced mathematical reasoning, but they still struggle with out-of-distribution (OOD) issues. |
| Approach: | They propose a framework to evaluate the logical validity of reasoning steps . they retrieves semantically similar questions and steps for PRM as a warmup . |
| Outcome: | The proposed framework outperforms baseline models on multiple real-world datasets. |
Pruning Foundation Models for High Accuracy without Retraining (2024.findings-emnlp)
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| Challenge: | Despite the superior performance of foundation models, it is challenging to deploy large language models in practical applications due to their massive parameters and computations. |
| Approach: | They propose a pruning algorithm to prune LLMs in one-shot without retraining . they propose retrainable pruning algorithms to prune multiple weights in LLM . |
| Outcome: | The proposed pruning methods perform better than baseline pruning methods on sparse and unstructured sparsity models. |
TableEval: A Real-World Benchmark for Complex, Multilingual, and Multi-Structured Table Question Answering (2025.emnlp-main)
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| Challenge: | Existing TableQA benchmarks focus on simple flat tables and suffer from data leakage . current benchmarks are monolingual and fail to capture cross-lingual variability . |
| Approach: | They propose a table-based TableQA benchmark to evaluate LLMs on real-world tasks. |
| Outcome: | The proposed benchmarks show that they achieve high agreement with human judgment . the proposed framework improves on the alignment between model responses and reference answers . |
Uncertainty Unveiled: Can Exposure to More In-context Examples Mitigate Uncertainty for Large Language Models? (2025.findings-acl)
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| Challenge: | Recent advances in handling long sequences have unlocked new possibilities for long-context in-contact learning (ICL). |
| Approach: | They investigate how increased examples influence predictive uncertainty . they quantify uncertainty across different “shot” configurations and focus on EU . |
| Outcome: | The proposed model reduces uncertainty in simple and complex tasks by injecting task-specific knowledge. |
When Seeing Is not Enough: Revealing the Limits of Active Reasoning in MLLMs (2026.acl-long)
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| Challenge: | Existing evaluations of multimodal large language models focus on passive inference, where seeing is not enough. |
| Approach: | They propose a benchmark to evaluate active reasoning in multimodal large language models . they propose to acquire missing evidence and iteratively refine decisions under incomplete information . |
| Outcome: | The proposed model performs better on active reasoning than on passive inference settings. |
QAVA: Query-Agnostic Visual Attack to Large Vision-Language Models (2025.naacl-long)
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| Challenge: | Currently, large vision-language models are limited in their ability to provide correct answers for multimodal tasks . however, they can still provide correct responses for multiple images associated with a single image . a query-agnostic visual attack (QAVA) provides robust adversarial examples that generate incorrect responses to unspecified and unknown questions. |
| Approach: | They propose a query-agnostic visual attack to create adversarial examples that generate incorrect answers to unspecified and unknown questions. |
| Outcome: | The proposed model improves performance on images when the question is unknown compared to known target questions . |
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing (2022.acl-long)
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Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei
| Challenge: | Existing work shows that pre-trained models can improve in various natural language processing tasks. |
| Approach: | They propose a unified-modal encoder-decoder framework that pre-trains speech-text representations using large-scale unlabeled speech and text data. |
| Outcome: | The proposed framework is superior to existing models on speech-to-text processing tasks. |
Multi-Domain Dialogue Acts and Response Co-Generation (2020.acl-main)
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| Challenge: | Existing pipeline approaches for task-oriented dialogue systems tend to predict multiple dialogue acts first and use them to assist response generation. |
| Approach: | They propose a neural co-generation model that generates dialogue acts and responses concurrently and preserves semantic structures of multi-domain dialogue acts. |
| Outcome: | The proposed model improves over state-of-the-art models in automatic and human evaluations on a large-scale dataset. |
CoreCodeBench: Decoupling Code Intelligence via Fine-Grained Repository-Level Tasks (2026.acl-long)
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Lingyue Fu, Hao Guan, Bolun Zhang, Haowei Yuan, Yaoming Zhu, Lin Qiu, ZongYu Wang, Xuezhi Cao, Xunliang Cai, Weiwen Liu, Weinan Zhang, Yong Yu
| Challenge: | Existing large language models for software engineering rely on coarse-grained pass rates obscuring specific cognitive bottlenecks. |
| Approach: | They propose a repository-level benchmark that dissects coding capabilities through atomized tasks. |
| Outcome: | The proposed framework achieves a 78.55% validity yield, surpassing the 31.7% retention rate of SWE-bench-Verified. |
COSMIC: Generalized Refusal Direction Identification in LLM Activations (2025.findings-acl)
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| Challenge: | Existing methods depend on predefined refusal templates detectable in output tokens or manual review. |
| Approach: | They propose a framework that optimally identifies steering directions and target layers using cosine similarity, entirely independent of output text. |
| Outcome: | The proposed framework achieves comparable steering effectiveness without any prior knowledge or assumptions of a model’s refusal behavior such as the use of certain refusal tokens. |
Towards Efficient and Effective Diffusion Language Model Inference via Semantic-Aware Adaptive Denoising (2026.acl-long)
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| Challenge: | Existing acceleration works cannot accurately detect semantically stabilized tokens and then skip computation, leading to sub-optimal speedup in practice. |
| Approach: | They propose a semantic-aware adaptive denoising framework that encodes scalar confidence scores into an evolution-awful feature vector and clusters vectors proactively and adaptively identify semantically converged tokens. |
| Outcome: | The proposed framework outperforms the SOTA competitor in speed and quality . it can detect semantically stabilized tokens and skip computation, resulting in sub-optimal speedup . |
Faster and Better LLMs via Latency-Aware Test-Time Scaling (2025.findings-emnlp)
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| Challenge: | Existing research has overlooked the efficiency of TTS from a latency-sensitive perspective. |
| Approach: | They propose two approaches to achieve latency-optimal TTS by branch-wise parallelism and sequence-wise parallelism. |
| Outcome: | The proposed approach achieves latency-optimal TTS for large models . branch-wise parallelism and sequence-wise parallelism are key approaches . |
SOM-NCSCM : An Efficient Neural Chinese Sentence Compression Model Enhanced with Self-Organizing Map (2021.emnlp-main)
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| Challenge: | Sentence Compression (SC) is an important natural language processing task . it aims to shorten sentences while preserving the original meanings of the words . improvements on Chinese SC models are still lacking due to several difficulties . |
| Approach: | They propose a neural Chinese SC model enhanced with a Self-Organizing Map from Chinese colloquial sentences from a real-life question answering system. |
| Outcome: | The proposed model achieves a promising F1 score of 89.655 and BLEU4 score of 70.116 . it improves the performance of the whole neural Chinese SC model in a valid manner . |
Toward Unifying Text Segmentation and Long Document Summarization (2022.emnlp-main)
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| Challenge: | Abstractive strategies produce more condensed summaries, but they suffer from hallucinations and factual errors, which pose a more difficult generation challenge. |
| Approach: | They propose a method that learns robust sentence representations by performing summarization and segmentation simultaneously, which is further enhanced by an optimization-based regularizer to promote selection of diverse summary sentences. |
| Outcome: | The proposed model achieves state-of-the-art performance on publicly available benchmarks and better cross-genre transferability when equipped with text segmentation. |
Stand on The Shoulders of Giants: Building JailExpert from Previous Attack Experience (2025.emnlp-main)
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Xi Wang, Songlei Jian, Shasha Li, Xiaopeng Li, Bin Ji, Ma Jun, Xiaodong Liu, Jing Wang, Jianfeng Zhang, Jie Yu, Feilong Bao, null Wangbaosheng
| Challenge: | Existing methods to generate human-aligned content with a “jailbreak prompt” are inefficient and repetitive, causing inefficiency and a lack of experience. |
| Approach: | They propose a framework that integrates past attack experiences to aid current jailbreak attempts. |
| Outcome: | The proposed framework improves both attack effectiveness and efficiency compared to the current black-box jailbreak method. |
POINTS-Reader: Distillation-Free Adaptation of Vision-Language Models for Document Conversion (2025.emnlp-main)
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Yuan Liu, Zhongyin Zhao, Le Tian, Haicheng Wang, Xubing Ye, Yangxiu You, Zilin Yu, Chuhan Wu, Zhou Xiao, Yang Yu, Jie Zhou
| Challenge: | Existing approaches to training document conversion models with manual annotation are costly and time-consuming, and training student models by distilling outputs from teacher models can significantly limit their performance in real-world applications. |
| Approach: | They propose a fully automated framework for constructing high-quality document extraction datasets and models capable of handling diverse document formats and layouts. |
| Outcome: | The proposed model outperforms existing models and improves on annotated documents. |
Towards A Better Initial Policy Model For Scalable Long-CoT Reinforcement Learning (2025.findings-acl)
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| Challenge: | Long-CoT reasoning and reinforcement learning are demonstrating remarkable performance and scalability, however, there is a lack of systematic guidelines for obtaining a better initial policy model. |
| Approach: | They propose a systematic guideline and a novel Re-RFT method to obtain more efficient reasoning patterns from different initial models. |
| Outcome: | The proposed method surpasses DeepSeek-R1-Distill-Qwen-14B model by 4.6%, demonstrating its effectiveness and superiority. |
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. |
Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models (2026.acl-long)
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Xin Cheng, Wangding Zeng, Damai Dai, Qinyu Chen, Bingxuan Wang, Zhenda Xie, Kezhao Huang, Xingkai Yu, Zhewen Hao, Han Zhang, Yu-Kun Li, Huishuai Zhang, Dongyan Zhao, Wenfeng Liang
| Challenge: | Mixture-of-Experts (MoE) scales capacity via conditional computation, but lacks knowledge lookup primitive. |
| Approach: | They propose a conditional memory instantiated via Deep Sparse Embedding (DSE) they propose 'u-shaped scaling law' that identifies optimal balance between MoE experts and DSE memory . |
| Outcome: | The proposed model outperforms an iso-parameter and isoFLOPs MoE baseline across knowledge and reasoning benchmarks and is infrastructure-efficient. |
TextHacker: Learning based Hybrid Local Search Algorithm for Text Hard-label Adversarial Attack (2022.findings-emnlp)
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| Challenge: | Existing textual adversarial attacks use gradient or prediction confidence to generate adversarials, making it hard to be deployed in real-world applications. |
| Approach: | They propose a textual adversarial attack that randomly perturbs lots of words to craft an adversarial example. |
| Outcome: | The proposed attack outperforms existing hard-label attacks in terms of attack performance and adversary quality. |
Enhancing Reasoning Capabilities by Instruction Learning and Chain-of-Thoughts for Implicit Discourse Relation Recognition (2023.findings-emnlp)
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| Challenge: | Existing models for implicit discourse relation recognition are based on generative models, but some studies suggest they do not perform as well as generic encoder-only models for NLU tasks. |
| Approach: | They propose a classification method that is solely based on generative models and utilize Chain-of-Thoughts to partition the inference process into a sequence of three successive stages. |
| Outcome: | The proposed model outperforms existing models on a natural language understanding task. |
Responding E-commerce Product Questions via Exploiting QA Collections and Reviews (C18-1)
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| Challenge: | Existing QA and review collections can be used to provide instant responses to product questions . a proposed framework can be applied to a real-world commercial E-commerce site . |
| Approach: | They propose a framework for automatically responding product questions in E-commerce sites . existing QA pairs are exploited as distant supervision for learning to rank responses . |
| Outcome: | The proposed framework can return a ranked list of snippets serving as the automated response for a given question. |
COAST: Enhancing the Code Debugging Ability of LLMs through Communicative Agent Based Data Synthesis (2025.findings-naacl)
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Weiqing Yang, Hanbin Wang, Zhenghao Liu, Xinze Li, Yukun Yan, Shuo Wang, Yu Gu, Minghe Yu, Zhiyuan Liu, Ge Yu
| Challenge: | Existing code debugging benchmarks focus on the Code Repair stage of the code generation process. |
| Approach: | They propose a framework to evaluate the debugging abilities of large language models by emulating the human debug process. |
| Outcome: | The proposed framework outperforms human-curated and GPT-4-generated training data, enabling 7B-scale LLMs to achieve comparable debugging performance to GPT-3.5. |
Towards Mitigating Modality Bias in Vision-Language Models for Temporal Action Localization (2026.acl-long)
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| Challenge: | Existing vision-language models overemphasize linguistic priors, leading to modality bias. |
| Approach: | They propose a vision-language aggregation framework that mitigates modality bias in TAL by preserving vision as the dominant signal while adaptively exploiting language only when beneficial. |
| Outcome: | Experiments on THUMOS14 show that the proposed model outperforms state-of-the-art models by up to 3.2% mAP. |
GenProve: Learning to Generate Text with Fine-Grained Provenance (2026.acl-long)
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| Challenge: | Existing methods for large language models (LLMs) are coarse-grained and fail to distinguish between direct quotes and complex reasoning. |
| Approach: | They propose a framework that combines supervised fine-tuning and group relative policy optimization to generate fluent answers while simultaneously producing sentence-level provenance triples. |
| Outcome: | The proposed framework outperforms 14 strong large language models in joint evaluation. |
Logical Natural Language Generation from Open-Domain Tables (2020.acl-main)
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| Challenge: | Existing studies on neural natural language generation focus on surface-level realizations with limited emphasis on logical inference. |
| Approach: | They propose a task where a model is tasked with generating natural language statements that can be logically entailed by facts in an open-domain semi-structured table. |
| Outcome: | The proposed task is based on the existing TabFact dataset with a wide range of logical/symbolic inferences. |
When Hearst Is not Enough: Improving Hypernymy Detection from Corpus with Distributional Models (2020.emnlp-main)
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| Challenge: | a taxonomy is a semantic hierarchy of words or concepts organized w.r.t. their hypernymy relationships. |
| Approach: | They propose a framework for hypernymy detection using large textual corpora . they quantify the non-negligible existence of specific sparsity cases . |
| Outcome: | The proposed framework quantifies the non-negligible existence of specific sparsity cases on several benchmark datasets. |
UAlign: Leveraging Uncertainty Estimations for Factuality Alignment on Large Language Models (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) often struggle to accurately express factual knowledge, especially in cases where the knowledge boundaries are ambiguous. |
| Approach: | They propose a framework that leverages Uncertainty estimations to represent knowledge boundaries and incorporates these representations into prompts for LLMs to Align with factual knowledge. |
| Outcome: | The proposed framework significantly improves the LLMs’ capacities to confidently answer known questions and refuse unknown questions on both in-domain and out-of-domain tasks. |
Event Detection from Social Media for Epidemic Prediction (2024.naacl-long)
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Tanmay Parekh, Anh Mac, Jiarui Yu, Yuxuan Dong, Syed Shahriar, Bonnie Liu, Eric Yang, Kuan-Hao Huang, Wei Wang, Nanyun Peng, Kai-Wei Chang
| Challenge: | Social media is an easy-to-access platform providing timely updates about societal trends and events. |
| Approach: | They propose a framework to extract epidemic-related events from social media posts to provide early warnings. |
| Outcome: | The proposed framework can detect epidemic events for three unseen epidemics of Monkeypox, Zika, and Dengue while existing models fail miserably. |
GeoEdit: Geometric Knowledge Editing for Large Language Models (2025.emnlp-main)
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Yujie Feng, Li-Ming Zhan, Zexin Lu, Yongxin Xu, Xu Chu, Yasha Wang, Jiannong Cao, Philip S. Yu, Xiao-Ming Wu
| Challenge: | Existing training-based model editing methods struggle to incorporate new knowledge while preserving unrelated general knowledge. |
| Approach: | They propose a framework that uses geometric relationships to differentiate between neurons associated with new knowledge updates and those related to general knowledge perturbations. |
| Outcome: | The proposed framework avoids updating neurons with directions approximately orthogonal to existing knowledge, thus preserving the model’s generalization ability. |
MedCite: Can Language Models Generate Verifiable Text for Medicine? (2025.findings-acl)
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| Challenge: | Existing LLM-based medical question answering systems lack citation generation and evaluation capabilities, raising concerns about their adoption in practice. |
| Approach: | They propose a framework that facilitates the design and evaluation of LLM citations for medical tasks and a retrieval-citation method that generates high-quality citation. |
| Outcome: | The proposed method achieves superior citation precision and recall improvements compared to strong baseline methods and correlates well with annotation results from professional experts. |
RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL (2022.emnlp-main)
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Jiexing Qi, Jingyao Tang, Ziwei He, Xiangpeng Wan, Yu Cheng, Chenghu Zhou, Xinbing Wang, Quanshi Zhang, Zhouhan Lin
| Challenge: | Experimental results show RASAT can leverage a variety of relational structures while inheriting the pretrained parameters from the T5 model. |
| Approach: | They propose a Transformer seq2seq architecture augmented with relation-aware self-attention that leverages relational structures while inheriting pretrained parameters from the T5 model. |
| Outcome: | The proposed model can leverage relational structures while inheriting pretrained parameters from the T5 model effectively. |
Chain-of-Reasoning: Towards Unified Mathematical Reasoning in Large Language Models via a Multi-Paradigm Perspective (2025.acl-long)
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Yiyao Yu, Yuxiang Zhang, Dongdong Zhang, Xiao Liang, Hengyuan Zhang, Xingxing Zhang, Mahmoud Khademi, Hany Hassan Awadalla, Junjie Wang, Yujiu Yang, Furu Wei
| Challenge: | Existing work shows that LLMs rely on single-paradigm reasoning that limits their effectiveness across diverse tasks. |
| Approach: | They propose a new framework that integrates multiple reasoning paradigms to enable synergistic collaboration. |
| Outcome: | The proposed model outperforms current SOTA models in theorem proving tasks and the MATH benchmark in arithmetic tasks. |
CopyNE: Better Contextual ASR by Copying Named Entities (2024.acl-long)
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| Challenge: | Existing approaches to transcribe contextual named entities (NEs) treat entities as tokens and generate them token-by-token, which may result in incomplete transcriptions of entities. |
| Approach: | They propose a mechanism that can copy entities from the NE dictionary and reduce errors during entity transcription. |
| Outcome: | The proposed mechanism can copy entities from the NE dictionary, reducing errors during entity transcription, ensuring the completeness of the entity. |
Dense X Retrieval: What Retrieval Granularity Should We Use? (2024.emnlp-main)
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| Challenge: | a learned dense retrieval model is often overlooked when using a corpus for inference, resulting in a design choice of retrieval unit . granularity of retrievals is important for both retrieval and downstream tasks . |
| Approach: | They propose a retrieval unit for dense retrieval that uses propositions to index corpus . propositions are defined as atomic expressions within text, each encapsulating a distinct factoid . |
| Outcome: | The proposed retrieval unit outperforms passage-level units on retrieval and downstream tasks. |
Improving Prompt Generalization for Cross-prompt Essay Trait Scoring from the Scoring-invariance Perspective (2025.findings-emnlp)
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| Challenge: | Existing research on cross-prompt trait essay scoring focuses on improving model generalization by obtaining prompt-invariant representations. |
| Approach: | They propose a scoring-invariant learning objective that encourages the model to focus on intrinsic information within the essay that reflects its quality during training, thereby learning generic scoring features. |
| Outcome: | The proposed scoring-invariant learning objective encourages the model to focus on intrinsic information within the essay that reflects its quality during training, thereby learning generic scoring features. |
Continual Learning in Task-Oriented Dialogue Systems (2021.emnlp-main)
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Andrea Madotto, Zhaojiang Lin, Zhenpeng Zhou, Seungwhan Moon, Paul Crook, Bing Liu, Zhou Yu, Eunjoon Cho, Pascale Fung, Zhiguang Wang
| Challenge: | Existing continuous learning systems are not designed to add new domains and functionalities through time without incurring the high cost of retraining the whole system. |
| Approach: | They propose a first-ever continual learning benchmark for task-oriented dialogue systems . they propose 'architecture' method based on residual adapters to implement continual training . |
| Outcome: | The proposed architectural method performs better than multitask learning while being 20X faster in learning new domains. |
Explainable Question Answering based on Semantic Graph by Global Differentiable Learning and Dynamic Adaptive Reasoning (2022.emnlp-main)
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| Challenge: | Existing models for multi-hop Question Answering have improved the implicit reasoning ability, but the black box nature of pure neural networks has hindered the construction of explainable intelligent systems. |
| Approach: | They propose a global differentiation strategy to explore optimal reasoning paths from latent probability space and a Dynamic Adaptive Reasoner to enhance generalization of unseen questions. |
| Outcome: | The proposed method achieves 17% improvements in F1-score against BreakRC and shows better interpretability. |
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. |
MAXS: Meta-Adaptive Exploration with LLM Agents (2026.findings-acl)
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Jian Zhang, Zhiyuan Wang, Zhangqi Wang, Yu He, Haoran Luo, li Yuan, Lingling Zhang, Rui Mao, Qika Lin, Jun Liu
| Challenge: | Existing methods for inference are often myopic and have divergent reasoning paths . a meta-adaptive reasoning framework is proposed to improve the efficiency of LLM agents . |
| Approach: | They propose a meta-adaptive reasoning framework that integrates tool execution and reasoning planning. |
| Outcome: | The proposed framework outperforms existing methods in performance and inference efficiency. |
iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering (2025.acl-long)
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| Challenge: | Large language models suffer from factual inaccuracies in knowledge-intensive domains. |
| Approach: | They propose a question-guided KBQA framework that iteratively decomposes complex queries into simpler sub-questions and integrates a Graph Neural Network (GNN) to look ahead and incorporate 2-hop neighbor information at each reasoning step. |
| Outcome: | The proposed framework improves on four benchmark datasets and four LLMs. |
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. |
BeamLoRA: Beam-Constraint Low-Rank Adaptation (2025.acl-long)
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Naibin Gu, Zhenyu Zhang, Xiyu Liu, Peng Fu, Zheng Lin, Shuohuan Wang, Yu Sun, Hua Wu, Weiping Wang, Haifeng Wang
| Challenge: | Low-Rank Adaptation (LoRA) is one of the most efficient parameter-efficient fine-tuning methods. |
| Approach: | They propose to conceptualize each LoRA module as a beam where each rank corresponds to a potential sub-solution. |
| Outcome: | The proposed method improves performance on three base models and 12 datasets. |
Demystifying the Power of Large Language Models in Graph Generation (2025.findings-naacl)
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Yu Wang, Ryan A. Rossi, Namyong Park, Nesreen K. Ahmed, Danai Koutra, Franck Dernoncourt, Tyler Derr
| Challenge: | Large Language Models (LLMs) have been used for graph discriminative tasks, but their potential for graph structure generation remains unexplored. |
| Approach: | They propose to use LLMs to generate graphs that optimize network properties by injecting domain expertise from network science into the code. |
| Outcome: | The proposed model generates graphs satisfying each property in different domains and compares it with established graph generative models across multiple domains. |
Robust Singing Voice Transcription Serves Synthesis (2024.acl-long)
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| Challenge: | Current AST methods struggle with accuracy and robustness when used for practical annotation. |
| Approach: | They propose a model that converts singing recordings into note sequences for automatic annotation of singing datasets. |
| Outcome: | The proposed model outperforms baseline models on enlarged, automatically annotated datasets. |
Course Concept Expansion in MOOCs with External Knowledge and Interactive Game (P19-1)
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| Challenge: | Existing methods to expand course concepts in MOOCs suffer from semantic drifts and lack of knowledge guidance. |
| Approach: | They propose to use a boundary search method to search for new concepts via external knowledge base and then use heterogeneous features to verify the results. |
| Outcome: | The proposed method improves on the datasets from Coursera and XuetangX. |
AlignSum: Data Pyramid Hierarchical Fine-tuning for Aligning with Human Summarization Preference (2024.findings-emnlp)
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| Challenge: | Text summarization tasks employ Pre-trained Language Models (PLMs) to fit diverse datasets. |
| Approach: | They propose a human summarization preference alignment framework to align PLMs with human preferences. |
| Outcome: | The proposed framework narrows the gap between automatic and human evaluations by integrating three components. |
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. |
Intention Knowledge Graph Construction for User Intention Relation Modeling (2026.eacl-long)
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Jiaxin Bai, Zhaobo Wang, Junfei Cheng, Dan Yu, Zerui Huang, Weiqi Wang, Xin Liu, Chen Luo, Yanming Zhu, Bo Li, Yangqiu Song
| Challenge: | Existing knowledge graphs focus on connecting intentions but lacks the ability to model the relationships between different intentions. |
| Approach: | They propose a framework to automatically generate an intention knowledge graph, capturing connections between user intentions. |
| Outcome: | The proposed model outperforms state-of-the-art methods and shows its utility. |
ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding (2021.naacl-main)
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| Challenge: | Existing methods to model coarse-grained linguistic information do not integrate coarse-gram information into pre-training. |
| Approach: | They propose an explicitly n-gram masking method to enhance integration of coarse-grained linguistic information into pre-training. |
| Outcome: | The proposed method outperforms existing models on English and Chinese text corpora and fine-tunes on 19 downstream tasks. |
Code-Switching for Enhancing NMT with Pre-Specified Translation (N19-1)
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| Challenge: | Existing methods to constrain NMT use placeholder tags for lexicon words and hard constraints during decoding. |
| Approach: | They propose to use placeholder tags to replace lexicon words with target translations . they use a data augmentation method to make code-switched training data . |
| Outcome: | The proposed method improves translation quality without hurting unconstrained words. |
A Survey on Natural Language Counterfactual Generation (2024.findings-emnlp)
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| Challenge: | Recent advances in NLP are driven by a variety of Large Language Models (LLMs), such as GPT-3 (175B) and PaLM (540B). |
| Approach: | They propose a taxonomy that categorizes the methods into four groups and summarizes the metrics for evaluating the generation quality. |
| Outcome: | The proposed taxonomy categorizes the generation methods into four groups and summarizes the metrics for evaluating the quality. |
QualiSpeech: A Speech Quality Assessment Dataset with Natural Language Reasoning and Descriptions (2025.acl-long)
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Siyin Wang, Wenyi Yu, Xianzhao Chen, Xiaohai Tian, Jun Zhang, Lu Lu, Yu Tsao, Junichi Yamagishi, Yuxuan Wang, Chao Zhang
| Challenge: | Existing datasets lack comprehensive annotations for speech quality assessment . existing methods lack detailed annotations, resulting in inaccurate evaluations. |
| Approach: | They propose a low-level speech quality assessment dataset incorporating natural language descriptions and a Benchmark to evaluate low- level speech understanding capabilities of auditory large language models. |
| Outcome: | The proposed model can be used to evaluate the low-level speech understanding capabilities of auditory large language models. |
Marco-o1 v2: Towards Widening The Distillation Bottleneck for Reasoning Models (2025.acl-long)
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Huifeng Yin, Yu Zhao, Minghao Wu, Xuanfan Ni, Bo Zeng, Huaiyu.wh Huaiyu.wh, Tianqi Shi, Liangying Shao, Chenyang Lyu, Longyue Wang, Weihua Luo, Kaifu Zhang
| Challenge: | Recent efforts to distill large reasoning models into smaller lightweight models have shown competitive performances. |
| Approach: | They propose to distill long Chain-of-Thought data to improve SFT and RL methods by constructing data from scratch using Monte Carlo Tree Search. |
| Outcome: | The proposed method significantly improves reasoning performance on various benchmarks such as math (GSM8K, MATH, AIME). |
KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering (2022.acl-long)
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Donghan Yu, Chenguang Zhu, Yuwei Fang, Wenhao Yu, Shuohang Wang, Yichong Xu, Xiang Ren, Yiming Yang, Michael Zeng
| Challenge: | Open-Domain Question Answering (ODQA) models typically include a retrieving module and a reading module. |
| Approach: | They propose a new open-domain question-answering framework that uses a knowledge-enhanced version of FiD to improve the approach. |
| Outcome: | The proposed model improves on ODQA benchmark datasets with less than 40% computation cost. |
Accelerate Parallelizable Reasoning via Parallel Decoding within One Sequence (2025.emnlp-main)
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| Challenge: | Existing methods for parallelizable reasoning tasks are inefficient, says a new study . generating lengthy reasoning sequences is computationally expensive and time-consuming, says the study authors . |
| Approach: | They propose a method that decodes multiple tokens per forward pass using a tree-like attention mask . their method achieves nearly 100% speedup in decoding while basically maintaining the answer quality . |
| Outcome: | Experimental results show that the method achieves nearly 100% speedup in decoding while maintaining the answer quality. |
Federated Domain Adaptation for Named Entity Recognition via Distilling with Heterogeneous Tag Sets (2023.findings-acl)
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| Challenge: | a federated domain adaptation approach is used to learn with NER datasets from multiple platforms while not violating data privacy. |
| Approach: | They propose to use a distillation approach to facilitate knowledge transfer across platforms. |
| Outcome: | The proposed model performs better in the clinic domain. |
My Words Imply Your Opinion: Reader Agent-Based Propagation Enhancement for Personalized Implicit Emotion Analysis (2025.acl-long)
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| Challenge: | Current studies personalize emotion analysis by focusing on the author but neglect the impact of the intended reader on implicit emotional feedback. |
| Approach: | They propose a model which incorporates reader feedback into implicit emotion analysis (IEA) they use large language models to create reader agents to simulate reader feedback . |
| Outcome: | The proposed model outperforms state-of-the-art models in a text-centric environment. |
Let the Comments Speak: A Multi-Agent Framework based on Large Language Model for Comment-Guided Code Refactoring (2026.findings-acl)
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| Challenge: | Current Large Language Models focus on syntax and ignore the vital semantic signals in code comments. |
| Approach: | They propose a Multi-Agent framework for COmment-guided code Refactoring that populates original code with precise comments to provide necessary semantic guidance for subsequent refactoring. |
| Outcome: | The proposed framework significantly improves code quality and achieves higher developer acceptance compared to baselines. |
Router-Tuning: A Simple and Effective Approach for Dynamic Depth (2025.emnlp-main)
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| Challenge: | Existing methods to improve computational efficiency are under-explored and face several critical challenges. |
| Approach: | They propose a method that selectively activates only a subset of the model's layers, skipping those deemed less important. |
| Outcome: | The proposed method significantly improves performance on Attention layers and MoE layers while reducing redundant computation and memory usage. |
HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generation (2025.findings-acl)
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| Challenge: | Traditional retrieval systems focus on lexical or semantic similarity rather than logical relevance. |
| Approach: | They propose a new RAG framework that augments retrieval with logical reasoning . hopRAG uses a retrieve-reason-prune mechanism to explore multi-hop neighbors . |
| Outcome: | The proposed framework outperforms conventional retrieval systems and state-of-the-art benchmarks on multi-hop QA tasks. |
The Best of Both Worlds: Combining Parallel and Sequential Inference Scaling via Aggregation Fine-Tuning (2026.findings-acl)
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| Challenge: | Empirical results show that AFT-trained models achieve substantial gains with test-time scaling. |
| Approach: | They introduce a supervised fine-tuning paradigm where models synthesize multiple draft responses into a single, refined answer. |
| Outcome: | Empirical results show that AFT-trained models outperform baseline models while eliminating external guidance. |
A New Concept of Knowledge based Question Answering (KBQA) System for Multi-hop Reasoning (2022.naacl-main)
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| Challenge: | Existing knowledge based question answering systems are trained based on labeled reasoning paths, which hinder their performance. |
| Approach: | They propose a KBQA system which leverages multiple reasoning paths’ information and only requires labeled answer as supervision. |
| Outcome: | The proposed system can leverage multiple reasoning paths’ information and only requires labeled answer as supervision. |
Task Compass: Scaling Multi-task Pre-training with Task Prefix (2022.findings-emnlp)
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Zhuosheng Zhang, Shuohang Wang, Yichong Xu, Yuwei Fang, Wenhao Yu, Yang Liu, Hai Zhao, Chenguang Zhu, Michael Zeng
| Challenge: | Existing studies show that multi-task learning with large-scale supervised tasks suffers from negative effects across tasks. |
| Approach: | They propose a task prefix guided multi-task pre-training framework to explore the relationships among tasks. |
| Outcome: | The proposed model can be used as a foundation backbone for a wide range of tasks and as augmentation tool for data augmentation with complementary tasks. |
AdvancedIF: Rubric-Based Benchmarking and Reinforcement Learning for Advancing LLM Instruction Following (2026.acl-long)
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Yun He, Wenzhe Li, Hejia Zhang, Songlin Li, Karishma Mandyam, Sopan Khosla, Yuanhao Xiong, Nanshu Wang, Xiaoliang Peng, Beibin Li, Shengjie Bi, Shishir G Patil, Qi Qi, Shengyu Feng, Julian Katz-Samuels, Richard Yuanzhe Pang, Sujan Kumar Gonugondla, Hunter Lang, Yue Yu, Yundi Qian, Maryam Fazel-Zarandi, Licheng Yu, Amine Benhalloum, Hany Hassan Awadalla, Manaal Faruqui
| Challenge: | Recent advances in large language models (LLMs) have shown impressive performance on a range of tasks, yet advanced instruction following (IF) remains a significant challenge. |
| Approach: | They propose a benchmark that features over 1,600 prompts and expert-curated rubrics that assess LLMs’ ability to follow complex, multi-turn, and system-level instructions. |
| Outcome: | The proposed framework improves instruction-following abilities of large language models, achieving a 6.7% gain on AdvancedIF and strong results on public benchmarks. |
BeamAggR: Beam Aggregation Reasoning over Multi-source Knowledge for Multi-hop Question Answering (2024.acl-long)
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Zheng Chu, Jingchang Chen, Qianglong Chen, Haotian Wang, Kun Zhu, Xiyuan Du, Weijiang Yu, Ming Liu, Bing Qin
| Challenge: | Large language models (LLMs) have demonstrated strong reasoning capabilities, but they still suffer from factual errors when tackling knowledge-intensive tasks. |
| Approach: | They propose a reasoning framework for knowledge-intensive multi-hop QA that prioritizes promising answers at each hop of question. |
| Outcome: | The proposed framework outperforms SOTA methods on four open-domain multi-hop reasoning datasets by 8.5%. |
Self Promotion in US Congressional Tweets (2021.naacl-main)
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| Challenge: | Prior studies have found that women self-promote less than men due to gender stereotypes. |
| Approach: | They built a BERT-based NLP model to predict whether a Congressional tweet shows self-promotion and then used it to examine whether he gender gap exists among Congressional Tweets. |
| Outcome: | The model predicts whether a Congressional tweet shows self-promotion and then tests it against 2 million tweets from 2017 to 2021. |
RoAST: Robustifying Language Models via Adversarial Perturbation with Selective Training (2023.findings-emnlp)
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Jaehyung Kim, Yuning Mao, Rui Hou, Hanchao Yu, Davis Liang, Pascale Fung, Qifan Wang, Fuli Feng, Lifu Huang, Madian Khabsa
| Challenge: | Several perspectives of robustness for pre-trained language models have been studied independently, but lacking a unified consideration in multiple perspectives. |
| Approach: | They propose a technique to enhance the multi-perspective robustness of LMs by introducing adversarial perturbation while the model parameters are selectively updated upon their relative importance. |
| Outcome: | The proposed technique improves the robustness of LMs by incorporating four perspectives on model robustness. |
SessionIntentBench: A Multi-task Inter-session Intention-shift Modeling Benchmark for E-commerce Customer Behavior Understanding (2026.findings-acl)
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Yuqi Yang, Weiqi Wang, Baixuan Xu, Wei Fan, Qing Zong, Chunkit Chan, Zheye Deng, Xin Liu, Yifan Gao, Changlong Yu, Chen Luo, Yang Li, Zheng Li, Qingyu Yin, Bing Yin, Yangqiu Song
| Challenge: | Existing models fail to capture and model customer intention effectively because of insufficient information exploitation and only apparent information like descriptions and titles are used. |
| Approach: | They propose to exploit existing session data to capture and model intention in E-commerce product purchase sessions using a multimodal benchmark. |
| Outcome: | The proposed framework can bridge the gap between intention understanding in simplified research cases like co-buy intention and more complex yet practical scenarios like session history. |
FolkScope: Intention Knowledge Graph Construction for E-commerce Commonsense Discovery (2023.findings-acl)
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Changlong Yu, Weiqi Wang, Xin Liu, Jiaxin Bai, Yangqiu Song, Zheng Li, Yifan Gao, Tianyu Cao, Bing Yin
| Challenge: | Existing intention-based studies on recommendation tasks are limited and use models to implicitly model the intention memberships. |
| Approach: | They propose a framework that leverages the generation power of large language models and human-in-the-loop annotation to semi-automatically construct the intention knowledge graph. |
| Outcome: | The proposed framework can model e-commerce knowledge and have many potential applications. |
Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clustering (2022.acl-long)
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| Challenge: | Existing work exploits easily accessible co-occurrence information of events to learn event representations. |
| Approach: | They propose a weakly supervised contrastive learning method and a prototype-based clustering method for event representation learning. |
| Outcome: | The proposed framework outperforms baselines on Hard Similarity and Transitive Sentence Similarity tasks. |
LLM×MapReduce: Simplified Long-Sequence Processing using Large Language Models (2025.acl-long)
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Zihan Zhou, Chong Li, Xinyi Chen, Shuo Wang, Yu Chao, Zhili Li, Haoyu Wang, Qi Shi, Zhixing Tan, Xu Han, Xiaodong Shi, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing studies have focused on extending the context length of large language models (LLMs) due to their quadratic computational complexity and a lack of high-quality long training examples, most LLMs are trained with a limited window size. |
| Approach: | They propose a training-free framework that enables large language models to effectively process long texts using a divide-and-conquer strategy for comprehensive document understanding. |
| Outcome: | The proposed framework outperforms open-source and commercial long-context LLMs and is compatible with several models. |
Easy Guided Decoding in Providing Suggestions for Interactive Machine Translation (2023.acl-long)
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| Challenge: | In order to improve translation efficiency, human translators perform post-editing on machine translations to correct errors. |
| Approach: | They propose to use the parameterized objective function of neural machine translation to deal with the TS problem without additional training. |
| Outcome: | The proposed method improves translation quality by 10.6 BLEU and reduces time overhead by 63.4% on benchmark datasets. |
Ideology Takes Multiple Looks: A High-Quality Dataset for Multifaceted Ideology Detection (2023.emnlp-main)
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| Challenge: | Existing datasets for the ID task only label a text as ideologically left- or right-leaning as a whole, regardless whether the text containing one or more different issues. |
| Approach: | They construct an ideological schema for a multifaceted ideology detection task using MITweet and an English Twitter dataset. |
| Outcome: | The proposed task uses a MITweet dataset with 12,594 English Twitter posts, each annotated with a Relevance and an Ideology label for all twelve facets. |
Curiosity-Driven Reinforcement Learning from Human Feedback (2025.acl-long)
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| Challenge: | Reinforcement learning from human feedback (RLHF) has proven effective in aligning large language models with human preferences, but often at the cost of reduced output diversity. |
| Approach: | They propose a framework that incorporates intrinsic rewards for novel states alongside traditional sparse extrinsic rewards to optimize both output diversity and alignment quality. |
| Outcome: | The proposed framework achieves significant gains in diversity on multiple diversity-oriented metrics while maintaining alignment with human preferences comparable to standard RLHF. |
Bridging Kernel Drivers and Virtual Device Models with LLM-Powered Automation (2026.acl-demo)
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| Challenge: | Linux kernel device drivers are tightly coupled with hardware, making them difficult to execute and test without physical devices. |
| Approach: | They present a tool that generates QEMU-based virtual devices directly from Linux driver source code. |
| Outcome: | The proposed tool generates QEMU-based virtual devices directly from Linux driver source code. |
Separate the Wheat from the Chaff: Winnowing Down Divergent Views in Retrieval Augmented Generation (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) lack robustness in knowledge-intensive tasks due to noisy or irrelevant retrieved data. |
| Approach: | They propose a multi-agent debate-based RAG framework that integrates external knowledge sources into large language models to improve their accuracy. |
| Outcome: | The proposed framework is unsupervised and leverages pretrained LLMs without fine-tuning, making it easily adaptable to various tasks. |
Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network (P19-1)
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| Challenge: | Existing approaches to cross-lingual knowledge graph (KG) alignment rely on entity embeddings derived from monolingual KG structural information. |
| Approach: | They propose a topic entity graph to represent entities with contextual information in KGs. |
| Outcome: | The proposed model outperforms state-of-the-art methods by a large margin. |
Dynamic Guided and Domain Applicable Safeguards for Enhanced Security in Large Language Models (2025.findings-naacl)
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| Challenge: | Existing defense methods struggle with two key issues: inadequate defense capabilities and over-defensiveness. |
| Approach: | They propose a multi-agents-based framework that leverages accurate external information to provide an unbiased summary of user intentions and safety response guidance. |
| Outcome: | Experiments on popular jailbreak attacks and benign datasets show that the proposed framework can enhance LLM's robustness against jailbreaks without compromising its general functionality. |
JPU: Bridging Jailbreak Defense and Unlearning via On-Policy Path Rectification (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) are vulnerable to diverse jailbreak attacks despite extensive safety alignment . |
| Approach: | They propose a method to rectify dynamic jailbreak paths towards safety anchors by dynamically mining on-policy adversarial samples to expose vulnerabilities and identify jailbreak path. |
| Outcome: | The proposed model significantly improves jailbreak resistance against dynamic attacks while maintaining its utility. |
Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks (2020.acl-main)
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| Challenge: | Existing graph-based methods for text classification cannot capture contextual word relationships within each document nor can they produce inductive learning of new words. |
| Approach: | They propose to use Graph Neural Networks to learn the local word representations and then aggregate the word nodes as the document embeddings. |
| Outcome: | The proposed method outperforms state-of-the-art methods on four benchmark datasets. |
Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models (2024.emnlp-main)
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| Challenge: | Standard RALMs often neglect their intrinsic knowledge due to the interference from retrieved information. |
| Approach: | They propose a new approach to improve robustness of RALMs by generating sequential reading notes for each retrieved document. |
| Outcome: | The proposed approach outperforms standard RALMs on four open-domain QA benchmarks. |
Multi-Fact Correction in Abstractive Text Summarization (2020.emnlp-main)
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| Challenge: | Existing abstractive summarization systems generate incorrect facts with respect to the source text. |
| Approach: | They propose a suite of two factual correction models that leverages question-answering knowledge to make corrections in system-generated summaries via span selection. |
| Outcome: | The proposed model improves factuality of news summarization without sacrificing summary quality. |
Distractor Generation based on Text2Text Language Models with Pseudo Kullback-Leibler Divergence Regulation (2023.findings-acl)
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Hui-Juan Wang, Kai-Yu Hsieh, Han-Cheng Yu, Jui-Ching Tsou, Yu An Shih, Chen-Hua Huang, Yao-Chung Fan
| Challenge: | Existing methods for cloze-style multiple choice questions (MCQs) distractor generation are based on knowledge bases and pre-trained language models. |
| Approach: | They propose to formulate cloze distractor generation task as Text2Text task and propose a pseudo Kullback-Leibler divergence for regulating the generation to consider item discrimination index in education evaluation. |
| Outcome: | The proposed model improves state-of-the-art performance from 10.81 to 22.00 (p@1 score) |
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. |
SALAD-Bench: A Hierarchical and Comprehensive Safety Benchmark for Large Language Models (2024.findings-acl)
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| Challenge: | SALAD-Bench is a safety benchmark specifically designed for LLMs . it provides a robust source for evaluating both attack and defense algorithms . |
| Approach: | They propose a hierarchical safety benchmark specifically designed for LLMs . it uses a taxonomy of questions spanning three levels and a robust taxonomies based on a QA pair . |
| Outcome: | The proposed safety benchmark shows that LLMs are resilient against emerging threats and the effectiveness of contemporary defense methods. |
TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking (2020.coling-main)
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| Challenge: | Existing methods to extract entities and relations from unstructured text are susceptible to cascading errors due to the separation of entity detection and relation classification. |
| Approach: | They propose a one-stage joint extraction model that detects overlapping relations while being immune from exposure bias. |
| Outcome: | The proposed model can identify overlapping relations while being immune from exposure bias. |
SepSeq: A Training-Free Framework for Long Numerical Sequence Processing in LLMs (2026.findings-acl)
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Jie Sun, Yu Liu, Lu Han, Qiwen Deng, Xiang Shu, Yang Xiao, Lintao Ma, Xingyu Lu, Jun Zhou, Pengfei Liu, Jiancan Wu, Xiang Wang
| Challenge: | Existing large-scale large-context models suffer from performance degradation when processing long numerical sequences. |
| Approach: | They propose a framework to mitigate attention dispersion by strategically inserting separator tokens into the model to recalibrat attention to local segments while preserving global context. |
| Outcome: | The proposed framework improves accuracy and reduces inference token consumption by 16.4% on 9 widely-adopted LLMs. |
Speed Up Your Code: Progressive Code Acceleration Through Bidirectional Tree Editing (2025.acl-long)
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Longhui Zhang, Jiahao Wang, Meishan Zhang, GaoXiong Cao, Ensheng Shi, Mayuchi Mayuchi, Jun Yu, Honghai Liu, Jing Li, Min Zhang
| Challenge: | Existing training methods, such as direct instruction fine-tuning, overlook hierarchical relationships among acceleration patterns. |
| Approach: | They propose a new training paradigm that uses bidirectional tree editing and progressive code acceleration learning to improve LLMs’ CA capabilities. |
| Outcome: | The proposed training paradigm outperforms prompt-enhanced GPT-4 and current training-based methods on average across five programming languages. |
Enhancing Large Language Model for Knowledge Graph Completion via Structure-Aware Alignment-Tuning (2025.emnlp-main)
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| Challenge: | Existing knowledge graph completion methods ignore inconsistent representation spaces between natural language and graph structures, leading to duplicate works and time-consuming processes. |
| Approach: | They propose a framework that enhances LLMs for KGC via structure-aware alignment-tuning to align graph embeddings with the natural language space through multi-task contrastive learning. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on two KGC tasks across four benchmark datasets. |
StablePT : Towards Stable Prompting for Few-shot Learning via Input Separation (2024.findings-emnlp)
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| Challenge: | Existing studies on prompt tuning have shown that language models can be effective few-shot learners with prompting. |
| Approach: | They propose to treat the hard prompt and soft prompt as separate inputs to mitigate noise brought by prompt initialization. |
| Outcome: | Experimental results show that the proposed method outperforms state-of-the-art methods by 6.97% in accuracy and reduces the standard deviation by 1.92 on average. |
Upcycling Instruction Tuning from Dense to Mixture-of-Experts via Parameter Merging (2025.acl-long)
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| Challenge: | Existing methods for tuning large language models from dense to MoE face significant data requirements and require large-scale post-training. |
| Approach: | They propose an upcycling instruction tuning approach for tuning a dense pre-trained model into a MoE instruction model using genetic algorithm and parameter merging. |
| Outcome: | The proposed approach improves the performance of large language models with a small amount of seed data and improves their scaling. |
Improving Personalized Sentiment Representation with Knowledge-enhanced and Parameter-efficient Layer Normalization (2024.lrec-main)
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| Challenge: | Existing studies on personalized sentiment classification consider document reviews as overall text unit and incorporate backgrounds (i.e., user and product information) Existing methods for personalized sentiment modeling have quadratic costs that increase with text length and heterogeneous mixes of background information and textual information. |
| Approach: | They propose a knowledge-enhanced and parameter-efficient layer normalization model that leverages pretrained checkpoints and background information into transformer structures. |
| Outcome: | The proposed model can be used to improve pretrained language models in document reviews and incorporate background information with parameter-efficient fine-tuning and knowledge injecting. |
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. |
Activation-Guided Local Editing for Jailbreaking Attacks (2026.acl-long)
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| Challenge: | Existing methods for jailbreaking Large Language Models (LLMs) are limited and produce incoherent or unreadable inputs. |
| Approach: | They propose a two-stage framework that performs a one-shot, scenario-based generation of context and rephrases the original malicious query to obscure its harmful intent. |
| Outcome: | The proposed framework achieves state-of-the-art Attack Success Rate, with gains of up to 37.74% over the strongest baseline, and excellent transferability to black-box and large-scale models. |
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. |
OpenICL: An Open-Source Framework for In-context Learning (2023.acl-demo)
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| Challenge: | In-context Learning (ICL) is a new paradigm for large language model evaluation. |
| Approach: | They propose an open-source toolkit for ICL and LLM evaluation. |
| Outcome: | The proposed framework is highly flexible and flexible and can be easily combined with other tools to suit users' needs. |
KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models (2024.acl-long)
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Zhuohao Yu, Chang Gao, Wenjin Yao, Yidong Wang, Wei Ye, Jindong Wang, Xing Xie, Yue Zhang, Shikun Zhang
| Challenge: | Existing methods to detect contaminated texts focus on quantifying contamination status instead of accurately gauging model performance. |
| Approach: | They propose a Knowledge-grounded Interactive Evaluation framework which incorporates an LLM-powered “interactor” role for the first time to accomplish a dynamic contamination-resilient evaluation. |
| Outcome: | The proposed framework is based on a question in a standard LLM benchmark and can be used to evaluate models in real-world conversations. |
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. |
Knowledge-Aware Query Expansion with Large Language Models for Textual and Relational Retrieval (2025.naacl-long)
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| Challenge: | Existing methods to generate query expansions focus on enhancing textual similarities between search queries and document corpus, overlooking document relations. |
| Approach: | They propose a knowledge-aware query expansion framework augmenting LLMs with structured document relations from knowledge graph (KG) they leverage document texts as rich KG node representations and use document-based relation filtering for their method. |
| Outcome: | The proposed framework augments LLMs with structured document relations from knowledge graph (KG) Extensive experiments on three datasets of diverse domains show the advantages compared against state-of-the-art methods on textual and relational semi-structured retrieval. |
TiMem: Temporal-Hierarchical Memory Consolidation for Long-Horizon Conversational Agents (2026.findings-acl)
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Kai Li, Xuanqing Yu, Ziyi Ni, Yi Zeng, Yao Xu, Zheqing Zhang, Xin Li, Jitao Sang, Xiaogang Duan, Xuelei Wang, Chengbao Liu, Jie Tan
| Challenge: | Existing memory frameworks provide limited support for temporally structured information across hierarchical levels, leading to fragmented memories and unstable long-horizon personalization. |
| Approach: | They propose a temporal–hierarchical memory framework that organizes conversations through a Temporal Memory Tree. |
| Outcome: | The proposed framework outperforms baselines while reducing the recalled memory length by 52.20%. |
ReviewGrounder: Improving Review Substantiveness with Rubric-Guided, Tool-Integrated Agents (2026.acl-long)
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Zhuofeng Li, Yi Lu, Dongfu Jiang, Haoxiang Zhang, Yuyang Bai, Chuan Li, Yu Wang, Shuiwang Ji, Jianwen Xie, Yu Zhang
| Challenge: | Rapid rise in AI conference submissions has driven increasing exploration of large language models (LLMs) for peer review support. |
| Approach: | They propose a peer review benchmarking tool based on paper-specific rubrics and a rubric-guided framework that decomposes reviewing into drafting and grounding stages. |
| Outcome: | The proposed framework outperforms baselines with stronger/larger backbones in both alignment with human judgments and rubric-based review quality across 8 dimensions. |
Enhancing Joint Multiple Intent Detection and Slot Filling with Global Intent-Slot Co-occurrence (2022.emnlp-main)
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| Challenge: | Existing joint models only use training procedure to determine the implicit correlation between intents and slots. |
| Approach: | They propose to make full use of the statistical co-occurrence frequency between intents and slots as prior knowledge to enhance joint multiple intent detection and slot filling. |
| Outcome: | The proposed model outperforms state-of-the-art models on two public multi-intent datasets. |
Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training (2021.emnlp-main)
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| Challenge: | Named entity recognition models require abundant high-quality annotations to train . distant supervision may induce incomplete and noisy labels, making supervised learning ineffective. |
| Approach: | They propose a noise-robust learning scheme for training named entity recognition models using only distantly-labeled data and a self-training method that uses contextualized augmentations created by pre-trained language models. |
| Outcome: | The proposed method outperforms existing supervised NER models on three datasets by significant margins. |
Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats (2026.acl-industry)
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Pengxiang Zhao, Hui-Ling Zhen, Xing Li, Han Bao, Weizhe Lin, Zhiyuan Yang, Yu Zi Wei, Xin Wang, Mingxuan Yuan, Xianzhi Yu, Zhenhua Dong
| Challenge: | Low-bit floating-point formats like MXFP and NVFP4 offer new opportunities for precision and efficiency. |
| Approach: | They evaluate HiFloat (HiF8 and HiF4), a family of floating-point formats tailored for Ascend NPUs. |
| Outcome: | The proposed formats excel with high-variance data and are compatible with state-of-the-art quantization frameworks. |
FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language Models (2024.emnlp-demo)
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Zhuohao Yu, Chang Gao, Wenjin Yao, Yidong Wang, Zhengran Zeng, Wei Ye, Jindong Wang, Yue Zhang, Shikun Zhang
| Challenge: | Large language models (LLMs) have revolutionized natural language processing with impressive performance across various tasks. |
| Approach: | They propose a framework for automated evaluations of large language models . they open-source their code at https://github.com/WisdomShell/FreeEval . |
| Outcome: | The framework is open-source and can be used to develop and validate new evaluation methods. |
Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub (2025.acl-long)
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Bohan Lyu, Xin Cong, Heyang Yu, Pan Yang, Cheng Qian, Zihe Wang, Yujia Qin, Yining Ye, Yaxi Lu, Chen Qian, Zhong Zhang, Yukun Yan, Yankai Lin, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing approaches lack flexibility to address diverse and ever-evolving user queries in open domains. |
| Approach: | They propose to evaluate LLMs on open-domain knowledge that requires tools to solve diverse and ever-evolving user queries. |
| Outcome: | The proposed system outperforms baselines in the open domain task-solving benchmark. |
Polarity Calibration for Opinion Summarization (2024.naacl-long)
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| Challenge: | Existing opinions summarization models emphasize the majority opinions while ignoring the minority opinions. |
| Approach: | They propose a method to align output summary and input text to achieve polarity calibration. |
| Outcome: | The proposed model can mitigate the polarity mismatch between output summary and input text, and maintain the content semantic and language quality. |
VarBench: Robust Language Model Benchmarking Through Dynamic Variable Perturbation (2024.findings-emnlp)
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| Challenge: | Recent benchmarks release only training and validation sets, keeping the test set labels closed-source. |
| Approach: | They propose to extract variables from each test case and define a value range for each variable. |
| Outcome: | The proposed method improves the accuracy of the evaluations on four datasets covering mathematical generation and multiple-choice tasks. |
Scaling Law for Multimodal Large Language Model Supervised Fine-Tuning (2026.acl-long)
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YiFan Zhang, Tao Yu, Feng Li, Chaoyou Fu, Yibo Hu, Kun Wang, Qingsong Wen, Zhang Zhang, Liang Wang, Rong Jin
| Challenge: | supervised fine-tuning (SFT) is crucial for multimodal large language models, yet a comprehensive scaling law is lacking . et al.: scaling laws focus on model size, pre-training tokens, and MLLM SFT data volumes . |
| Approach: | They propose two scaling laws to guide optimal model-data configuration . they propose one applicable when training data volumes are well defined by researchers . |
| Outcome: | The proposed scaling laws provide valuable recommendations for optimal resource allocation . they show that the proposed laws are more accurate than existing models . |
Towards Unified Representations of Knowledge Graph and Expert Rules for Machine Learning and Reasoning (2022.aacl-main)
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| Challenge: | Empirical study shows superiority of proposed method over time-tested knowledge-driven and data-driven methods. |
| Approach: | They propose a cognitive knowledge graph that unifies expert rules and relational facts as the substrate of machine learning and reasoning models. |
| Outcome: | Empirical results show the proposed method superior to time-tested methods . the proposed model can perform both learning and reasoning with labeled data . |
ElitePLM: An Empirical Study on General Language Ability Evaluation of Pretrained Language Models (2022.naacl-main)
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Junyi Li, Tianyi Tang, Zheng Gong, Lixin Yang, Zhuohao Yu, Zhipeng Chen, Jingyuan Wang, Xin Zhao, Ji-Rong Wen
| Challenge: | Recent years have featured a trend towards Transformer based pretrained language models (PLMs) in natural language processing systems. |
| Approach: | They propose to use four evaluation dimensions to evaluate ten widely-used PLMs . they find that pretrained language models are good at different ability tests . |
| Outcome: | The results show that pretrained language models are good at different ability tests and have excellent transferability between tasks. |
CodeM: Less Data Yields More Versatility via Ability Matrix (2024.findings-acl)
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Daoguang Zan, Ailun Yu, Wei Liu, Bo Shen, Shaoxin Lin, Yongshun Gong, Yafen Yao, Yan Liu, Bei Guan, Weihua Luo, Yongji Wang, Qianxiang Wang, Lizhen Cui
| Challenge: | Recent efforts to train code large language models have been booming recently . however, this will incur significant costs in constructing data and training model considering the countless downstream scenarios. |
| Approach: | They propose a data construction strategy which decouples code LLMs’ abilities into two dimensions and constructs a lightweight training corpus that only covers a subset of target scenarios. |
| Outcome: | The proposed model can train a multilingual multitasking model using less data and training data. |
Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction (2020.coling-main)
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| Challenge: | Distant Supervision (DS) generates large-scale annotated data but has wrong labels that result in incorrect evaluation scores during testing. |
| Approach: | They build a dataset using DS-generated data as training data and hire annotators to label test data. |
| Outcome: | The proposed dataset NYTH has a much larger test set and performs more accurate and consistent evaluation. |
Free-MAD: Consensus-Free Multi-Agent Debate (2026.findings-acl)
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| Challenge: | Existing multi-agent debate methods rely on multiple rounds of interaction among agents to reach consensus, and the final output is decided by majority voting in the last round. |
| Approach: | They propose a multi-agent debate framework that eliminates the need for consensus among agents and reconstructs the debate phase by introducing anti-conformity. |
| Outcome: | Experiments on eight benchmark datasets show that Free-MAD significantly improves reasoning performance while requiring only a single-round debate and thus reducing token costs. |
Evolutionary Negative Module Pruning for Better LoRA Merging (2026.acl-long)
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| Challenge: | Existing methods for integrating multiple low-rank Adaptation experts into a single backbone are limited by negative modules. |
| Approach: | They propose a plug-and-play LoRA pruning method to locate and exclude negative modules prior to merging. |
| Outcome: | The proposed method boosts the performance of existing merging algorithms across languages and vision domains. |
BERT-BC: A Unified Alignment and Interaction Model over Hierarchical BERT for Response Selection (2024.lrec-main)
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| Challenge: | Recent performance boosting for dialogue response selection task achieved by Cross-Encoder based models is limited and the learned models have poor generalization capability in realistic scenarios. |
| Approach: | They propose a model that combines the representation-based Bi-Encoder and interaction-based Cross-Encoding to achieve better semantic representation. |
| Outcome: | The proposed model can achieve state-of-the-art performance on three benchmark datasets for multi-turn response selection. |
Improving the Robustness of Deep Reading Comprehension Models by Leveraging Syntax Prior (D19-58)
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| Challenge: | Recent studies indicate that the current machine reading comprehension systems suffer from weak robustness against adversarial samples. |
| Approach: | They propose to take sentence syntax as the leverage in the answer predicting process and exploit the syntactic elements of a question to improve the generalization and robustness of MRC models. |
| Outcome: | The proposed method improves generalization and robustness against adversarial samples, with performance well-maintained. |
Diverse Few-Shot Text Classification with Multiple Metrics (N18-1)
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Mo Yu, Xiaoxiao Guo, Jinfeng Yi, Shiyu Chang, Saloni Potdar, Yu Cheng, Gerald Tesauro, Haoyu Wang, Bowen Zhou
| Challenge: | Existing methods for few-shot learning are insufficient to capture task variations in natural language domains. |
| Approach: | They propose an adaptive metric learning approach that automatically determines the best weighted combination from a set of metrics obtained from meta-training tasks for a newly seen few-shot task. |
| Outcome: | The proposed method performs favorably against state-of-the-art few shot learning algorithms on real-world sentiment analysis and dialog intent classification datasets. |
Now You Hear Me: Audio Narrative Attacks Against Large Audio–Language Models (2026.eacl-long)
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| Challenge: | Existing jailbreaks against large audio-language models fall into two categories . early work converted text-based prompts into synthetic speech, while subsequent work introduced minor acoustic variations such as accent shifts, phonetic spellings, or stress patterns. |
| Approach: | They propose a text-to-audio jailbreak that embeds disallowed directives within a narrative-style audio stream. |
| Outcome: | The proposed attack exploits structural and acoustic properties of a text-to-audio model . it achieves 98.26% success rate, significantly exceeding baselines for text-based models . |
Confusion is the Final Barrier: Rethinking Jailbreak Evaluation and Investigating the Real Misuse Threat of LLMs (2025.findings-emnlp)
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Yu Yan, Sheng Sun, Zhe Wang, Yijun Lin, Zenghao Duan, Zhifei Zheng, Min Liu, Zhiyi Yin, Jianping Zhang
| Challenge: | Large Language Models have been developed to deal with real-world crimes, but it remains unclear whether they internalize authentic knowledge or are forced to simulate toxic language patterns. |
| Approach: | They construct knowledge-intensive Q&A to investigate misuse threats of Large Language Models in terms of dangerous knowledge possession, harmful task planning utility, and harmfulness judgment robustness. |
| Outcome: | The findings raise concerns that jailbreak success is often attributable to a hallucination loop between jailbroken LLM and judger LLM . |
When TableQA Meets Noise: A Dual Denoising Framework for Complex Questions and Large-scale Tables (2026.acl-long)
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Shenghao Ye, Yu Guo, Dong Jin, Yuxiang Wang, Yikai Shen, Yunpeng Hou, Shuangwu Chen, null Jianyang, Xiaofeng Jiang
| Challenge: | Extensive research shows that noisy data significantly degrades the performance of table reasoning in real-world applications. |
| Approach: | They propose a dual denoising framework for complex questions and large-scale tables that uses Tree-guided table pruning to remove irrelevant data step by step. |
| Outcome: | The proposed framework achieves outstanding performance on TableQA tasks with complex questions and large-scale tables. |
FuseSearch: Learning Adaptive Parallel Execution for Efficient Code Localization (2026.findings-acl)
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Ke Xu, Siyang Xiao, Ming Liang, Yichen Yu, Zhixiang Wang, Jingxuan Xu, Dajun Chen, Wei Jiang, Yong Li
| Challenge: | Existing parallel code localization agents suffer from a 34.9% redundant tool invocation rate . specialized localization agent that operate as dedicated search components is needed to achieve high localization accuracy. |
| Approach: | They propose a parallel code localization system that reframes parallel code execution as a quality–efficiency co-optimization problem. |
| Outcome: | The proposed method matches SOTA performance while being 93.6% faster. |