Papers by Kun Wu

Dependency Parsing as MRC-based Span-Span Prediction (2022.acl-long)

Challenge:	Domain Large Language Models (LLMs) are developed for domain-specific tasks based on general LLMs, but it still requires professional knowledge to facilitate the expertise for some domain- specific tasks.
Approach:	They propose a pipeline to solve domain-specific calculation problems with KIPG . they use it to extract key variables and calculate outcomes dependent on domain knowledge .
Outcome:	The proposed pipeline solves domain-specific calculation problems more effectively . it generates knowledge-intensive programs according to the domain- specific documents .

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Leilei Gan, Yuxian Meng, Kun Kuang, Xiaofei Sun, Chun Fan, Fei Wu, Jiwei Li

Syntactic Text Processing

Challenge:	Existing methods for dependency parsing address the issue that edges should be constructed at the text span/subtree level rather than word level.
Approach:	They propose a method that constructs dependency trees by directly modeling span-span relations by modeling subtree-subtree relationships.
Outcome:	The proposed method constructs dependency trees by modeling span-span relations . it can retrieve missing spans in the span proposal stage, which leads to higher recall .

FLEEK: Factual Error Detection and Correction with Evidence Retrieved from External Knowledge (2023.emnlp-demo)

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Farima Fatahi Bayat, Kun Qian, Benjamin Han, Yisi Sang, Anton Belyy, Samira Khorshidi, Fei Wu, Ihab Ilyas, Yunyao Li

Semantic Role Labeling Guided Multi-turn Dialogue ReWriter (2020.emnlp-main)

Challenge:	Existing large language models (LLMs) have a tendency to hallucinate and provide creative and fluent responses that are not factually accurate.
Approach:	They propose a tool that automatically extracts factual claims from text, gathers evidence from external knowledge sources, evaluates the factuality of each claim, and suggests revisions for identified errors.
Outcome:	The proposed tool detects errors in text and evaluates their factuality and suggests revisions based on the collected evidence.

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Kun Xu, Haochen Tan, Linfeng Song, Han Wu, Haisong Zhang, Linqi Song, Dong Yu

Paraphrasing

Challenge:	Existing attentive models attend to all words without prior focus, which results in inaccurate concentration on some dispensable words.
Approach:	They propose to use semantic role labeling to provide additional guidance for multi-turn dialogue rewriting models.
Outcome:	The proposed model outperforms existing models on multi-turn dialogue rewriting tasks.

Rewrite to Jailbreak: Discover Learnable and Transferable Implicit Harmfulness Instruction (2025.findings-acl)

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Yuting Huang, Chengyuan Liu, Yifeng Feng, Yiquan Wu, Chao Wu, Fei Wu, Kun Kuang

Tailoring Diagnostic Modeling to Individual Learners: Personalized Distractor Generation via MCTS-Guided Reasoning Reconstruction (2026.acl-long)

Challenge:	Existing jailbreak methods create a forced instruction-following scenario, or search adversarial prompts with prefix or suffix tokens to achieve a specific representation manually or automatically.
Approach:	They propose a method that rewrites the original instruction to achieve a jailbreak . they propose rewriting the original instructions to improve the attack strategy .
Outcome:	The proposed method is more efficient and easier to identify since no additional features are introduced.

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Tao Wu, Jingyuan Chen, Wang Lin, Jian Zhan, Mengze Li, Fangzhou Jin, Min Zhang, Kun Kuang, Fei Wu

Adaptive Query Rewriting: Aligning Rewriters through Marginal Probability of Conversational Answers (2024.emnlp-main)

Challenge:	Current distractor generation methods produce shared distractors for all students, ignoring individual variations in reasoning, which limits their diagnostic effectiveness.
Approach:	They propose a method which tailors distractors to each student’s specific cognitive flaws, inferred from their past question-answering (QA) history.
Outcome:	The proposed framework outperforms existing methods in generating plausible distractors and adapts to group-level settings.

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Tianhua Zhang, Kun Li, Hongyin Luo, Xixin Wu, James Glass, Helen Meng

Evolving Knowledge Distillation with Large Language Models and Active Learning (2024.lrec-main)

Challenge:	Existing methods to incorporate retriever’s preference during the training of query rewriting models rely on extensive annotations such as in-domain rewrites and/or relevant passage labels, limiting their generalization and adaptation capabilities.
Approach:	They propose a framework for training query rewriting models with limited rewrite annotations from seed datasets and completely no passage label.
Outcome:	The proposed approach decontexualizes conversational queries into self-contained questions suitable for off-the-shelf retrievers.

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Chengyuan Liu, Fubang Zhao, Kun Kuang, Yangyang Kang, Zhuoren Jiang, Changlong Sun, Fei Wu

Hierarchical Catalogue Generation for Literature Review: A Benchmark (2023.findings-emnlp)

Challenge:	Existing studies have focused on the direct use of large language models for text generation and labeling, without fully exploring their potential to comprehend the target task and acquire valuable knowledge.
Approach:	They propose to distill the knowledge of large language models into smaller models by generating annotated data.
Outcome:	The proposed method improves the performance of small domain models while enhancing the ability of large language models.

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Kun Zhu, Xiaocheng Feng, Xiachong Feng, Yingsheng Wu, Bing Qin

Challenge:	Scientific literature review generation aims to extract and organize important information from an abundant collection of reference papers and produces corresponding reviews while lacking a clear and logical hierarchy.
Approach:	They propose a task to generate a hierarchical catalogue of a review paper given various references by using a database of 7.6k literature review catalogues and 389k reference papers.
Outcome:	The proposed method produces a hierarchical catalogue of a review paper given various references.

De-Biased Court’s View Generation with Causality (2020.emnlp-main)

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Yiquan Wu, Kun Kuang, Yating Zhang, Xiaozhong Liu, Changlong Sun, Jun Xiao, Yueting Zhuang, Luo Si, Fei Wu

Enhancing Court View Generation with Knowledge Injection and Guidance (2024.lrec-main)

Challenge:	Existing approaches to court’s view generation can be used to address this problem, but neglecting the confounding bias in data can limit the model performance and pollute learning outcomes.
Approach:	They propose a novel Attentional and Counterfactual based Natural Language Generation method consisting of an attentional encoder and a pair of innovative counterfactual decoders to generate judgment-discriminative court's views.
Outcome:	The proposed method is able to generate judgment-discriminative court's views (both supportive and non-supportive views) under both quantitative and qualitative evaluation metrics.

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Ang Li, Yiquan Wu, Yifei Liu, Kun Kuang, Fei Wu, Ming Cai

SQL-to-Text Generation with Graph-to-Sequence Model (D18-1)

Challenge:	Existing methods of natural language generation (NLG) rely on the extensive parameters of pretrained language models (PLMs) but their effectiveness may be compromised by insufficient domain-specific knowledge.
Approach:	They propose a knowledge-injected prompt encoder to incorporate domain knowledge during the training stage, thereby reducing computational overhead.
Outcome:	The proposed approach outperforms established baselines on real-world data in responsivity of claims and in the ability to transfer domain knowledge.

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Kun Xu, Lingfei Wu, Zhiguo Wang, Yansong Feng, Vadim Sheinin

From Graph to Word Bag: Introducing Domain Knowledge to Confusing Charge Prediction (2024.lrec-main)

Challenge:	Existing approaches to generate SQL-to-text using seq2seq models do not capture graph-structured information in SQL query.
Approach:	They propose a graph-to-sequence model to encode global structure information into node embeddings.
Outcome:	The proposed model outperforms the Seq2Seq and Tree2Sq baselines on the WikiSQL and Stackoverflow datasets.

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Ang Li, Qiangchao Chen, Yiquan Wu, Xiang Zhou, Kun Kuang, Fei Wu, Ming Cai

Structured Data in NLP

Challenge:	Existing charge prediction methods have shown impressive performance, but they face significant challenges when dealing with confusing charges, such as Snatch and Robbery.
Approach:	They propose a novel approach which introduces domain knowledge regarding constituent elements to guide the model in making judgments on confusing charges, much like a judge’s reasoning process.
Outcome:	The proposed approach maintains exceptional performance in imbalanced label distributions.

Gold Panning in Vocabulary: An Adaptive Method for Vocabulary Expansion of Domain-Specific LLMs (2024.emnlp-main)

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Chengyuan Liu, Shihang Wang, Lizhi Qing, Kun Kuang, Yangyang Kang, Changlong Sun, Fei Wu

Emotion Transfer with Enhanced Prototype for Unseen Emotion Recognition in Conversation (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) struggle when it comes to specialized domains due to limited domain-specific knowledge.
Approach:	They propose an adaptive method that automatically identifies valuable words from a given domain vocabulary.
Outcome:	The proposed method has been validated on three Chinese datasets and performed on general tasks.

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Kun Peng, Cong Cao, Hao Peng, Guanlin Wu, Zhifeng Hao, Lei Jiang, Yanbing Liu, Philip S. Yu

Sentiment Analysis

Challenge:	Existing research on emotion recognition in conversation does not reach a consensus on classification theories . despite this, there is no clear consensus on how to recognize previously unseen emotions in real-world applications.
Approach:	They propose a prototype-based emotion transfer framework that can be used in real-world applications.
Outcome:	The proposed framework shows promise but still faces key challenges in the field of emotion recognition in conversation.

KRLS: Improving End-to-End Response Generation in Task Oriented Dialog with Reinforced Keywords Learning (2023.emnlp-main)

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Xiao Yu, Qingyang Wu, Kun Qian, Zhou Yu

MolTC: Towards Molecular Relational Modeling In Language Models (2024.findings-acl)

Challenge:	Task-oriented dialogs (TOD) require a model to generate a response that optimizes for task-related metrics.
Approach:	They propose a faster generation procedure that samples from independent next-word distributions and introduce a fine-grained reward function to help the model focus on learning key information in a dialog.
Outcome:	The proposed algorithm achieves state-of-the-art performance on an offline task with 15% training time reduction compared to a standard RL algorithm using auto-regressive generation.

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Junfeng Fang, Shuai Zhang, Chang Wu, Zhengyi Yang, Zhiyuan Liu, Sihang Li, Kun Wang, Wenjie Du, Xiang Wang

CSAGN: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling (2021.emnlp-main)

Challenge:	Molecular Relational Learning (MRL) is a promising way to understand interactions between molecular pairs.
Approach:	They propose a novel LLM-based multi-modal framework for molecular interaction modeling following Chain-of-Thought (CoT) theory which integrates graphical information of two molecules in pair.
Outcome:	The proposed framework integrates graphical information of two molecules in pair.

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Han Wu, Kun Xu, Linqi Song

Towards Interactivity and Interpretability: A Rationale-based Legal Judgment Prediction Framework (2022.emnlp-main)

Challenge:	Existing CSRL parsers struggle to handle conversational structural information.
Approach:	They propose a conversational semantic role labeling task which explicitly encodes speaker dependent information and proposes a multi-task learning method to further improve the model.
Outcome:	The proposed model outperforms baselines on benchmark datasets on conversation-based tasks.

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Yiquan Wu, Yifei Liu, Weiming Lu, Yating Zhang, Jun Feng, Changlong Sun, Fei Wu, Kun Kuang

LoraRetriever: Input-Aware LoRA Retrieval and Composition for Mixed Tasks in the Wild (2024.findings-acl)

Challenge:	Existing models lack interpretability due to the neglect of rationale in the prediction process.
Approach:	They propose a rationale-based legal judgment prediction framework that follows the judge's real trial logic and provides good interactivity and interpretability.
Outcome:	The proposed framework provides good interactivity and interpretability which enables practical use.

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Ziyu Zhao, Leilei Gan, Guoyin Wang, Wangchunshu Zhou, Hongxia Yang, Kun Kuang, Fei Wu

Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models (2021.emnlp-main)

Challenge:	Low-Rank Adaptation (LoRA) is an effective yet efficient solution for fine-tuning large language models.
Approach:	They propose a low-rank Adaptation framework that retrieves and composes multiple LoRAs according to input prompts.
Outcome:	Experimental results show that LoraRetriever outperforms baselines in terms of performance and versatility.

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Kun Zhou, Wayne Xin Zhao, Sirui Wang, Fuzheng Zhang, Wei Wu, Ji-Rong Wen

Neural News Recommendation with Long- and Short-term User Representations (P19-1)

Challenge:	Recent studies have shown that powerful pre-trained language models can be fooled by small perturbations or intentional attacks.
Approach:	They propose a framework for fine-tuning PLMs using a masked language model and Gaussian noise to augment semantically relevant examples with sufficient diversity.
Outcome:	The proposed framework improves the robustness of pre-trained language models and alleviates performance degradation under adversarial attacks.

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Mingxiao An, Fangzhao Wu, Chuhan Wu, Kun Zhang, Zheng Liu, Xing Xie

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

Challenge:	Existing news recommendation methods learn a single representation for each user, which may be insufficient.
Approach:	They propose a neural news recommendation approach which can learn both long- and short-term user representations by using a news encoder and a user encoder.
Outcome:	The proposed approach can learn both long- and short-term user representations on a real-world dataset.

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

Investigating the Robustness of Natural Language Generation from Logical Forms via Counterfactual Samples (2022.emnlp-main)

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

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Chengyuan Liu, Leilei Gan, Kun Kuang, Fei Wu

Robustness in NLP

Challenge:	State-of-the-art methods based on pre-trained models have achieved remarkable performance on the standard test dataset.
Approach:	They propose to incorporate hierarchical structure of logical forms into the model and exploit automatically generated counterfactual data for training.
Outcome:	The proposed method is effective to alleviate spurious correlations between the headers of the tables and operators of the logical form.

JARVIS or Ultron? A Survey on the Safety and Security Threats of Computer-Using Agents (2026.acl-long)

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Ada Chen, Yongjiang Wu, Junyuan Zhang, Jingyu Xiao, Shu Yang, Jen-tse Huang, Kun Wang, Wenxuan Wang, Shuai Wang

Challenge:	Recent advances in computer-using agents have created new safety and security risks . despite the impressive capabilities of CUAs, there are still significant security risks.
Approach:	They propose a systematization of knowledge on the safety and security threats of Computer-Using Agents.
Outcome:	The proposed framework provides a framework for assessing the safety and security risks of computer-using agents.

UniLR: Unleashing the Power of LLMs on Multiple Legal Tasks with a Unified Legal Retriever (2025.acl-long)

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Ang Li, Yiquan Wu, Yifei Liu, Ming Cai, Lizhi Qing, Shihang Wang, Yangyang Kang, Chengyuan Liu, Fei Wu, Kun Kuang

Reinforcement Learning on Pre-Training Data (2026.acl-long)

Challenge:	Existing retrieval methods are designed for general domains, struggling with legal knowledge, or tailored for specific legal tasks, unable to handle diverse legal knowledge types.
Approach:	They propose a novel retrieval method that integrates specialized knowledge into LLMs.
Outcome:	The proposed method can perform multiple legal retrieval tasks for LLMs.

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Siheng Li, Kejiao Li, Zenan Xu, Guanhua Huang, Kun Li, Haoyuan Wu, null Wujiajia, Zihao Zheng, Chenchen Zhang, Kun Shi, Xue Gong, Qi Yi, Ruibin Xiong, Tingqiang Xu, Yuhao Jiang, Jianfeng Yan, Yuyuan Zeng, Guanghui Xu, Jinbao Xue, Zhijiang xu, Zheng Fang, Shuai LI, Qibin Liu, Xiaoxue Li, Zhuoyu Li, Yangyu Tao, Fei Gao, Cheng Jiang, Bochao Wang, Kai Liu, Jianchen Zhu, Wai Lam, Bo Zhou, Di Wang

Unleashing the Power of LLMs in Court View Generation by Stimulating Internal Knowledge and Incorporating External Knowledge (2024.findings-naacl)

Challenge:	Recent progress in large language models is driven by scaling of training compute through pre-training with nexttoken prediction (NTP) or post-training (RL) Pre-training using NTP enables models to acquire extensive knowledge and skills from general data, but it suffers from data inefficiency and catastrophic forgetting in continual learning settings.
Approach:	They propose to scale training compute through pre-training with next-token prediction (NTP) or post-training by scaling reinforcement learning (RL) to improve learning from general data.
Outcome:	Experiments on multiple benchmarks and models show that the proposed approach improves continual pre-training and provides a strong foundation for post-training on Qwen3-8B-Base.

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Yifei Liu, Yiquan Wu, Ang Li, Yating Zhang, Changlong Sun, Weiming Lu, Fei Wu, Kun Kuang

DECOR: Improving Coherence in L2 English Writing with a Novel Benchmark for Incoherence Detection, Reasoning, and Rewriting (2024.emnlp-main)

Challenge:	Large Language Models (LLMs) have made remarkable strides in language generation, but they encounter difficulties in the knowledge-intensive legal domain.
Approach:	They propose to decompose court views into different parts, stimulate internal knowledge, and incorporate external information to unleash the power of LLMs in the task.
Outcome:	The proposed method generates more accurate and reliable court views on two real-world datasets LAIC2021 and CJO2022.

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Xuanming Zhang, Anthony Diaz, Zixun Chen, Qingyang Wu, Kun Qian, Erik Voss, Zhou Yu

Text Generation

Challenge:	Existing automated writing evaluation systems only detect incoherence in writing . a recent study has found that incorporating specific reasons for incohence improves the quality of rewrites .
Approach:	They propose a benchmark that includes expert annotations for detecting incoherence in L2 English writing, identifying the underlying reasons, and rewriting the incoerent sentences.
Outcome:	The proposed benchmark improves coherence in L2 English writing by fine-tuning models . the authors find that incorporating specific reasons improves quality of rewrites .

RexUIE: A Recursive Method with Explicit Schema Instructor for Universal Information Extraction (2023.findings-emnlp)

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Chengyuan Liu, Fubang Zhao, Yangyang Kang, Jingyuan Zhang, Xiang Zhou, Changlong Sun, Kun Kuang, Fei Wu

Information Extraction & Text Mining

Challenge:	Named Entity Recognition (NER) and Relation Extraction (RE) models have limited success when extracting general schemas such as quadruples and quintuples.
Approach:	They propose a formal formulation that covers almost all extraction schemas and a Recursive Method with Explicit Schema Instructor for UIE.
Outcome:	The proposed method shows strong performance under full-shot and few-shot settings and achieves state-of-the-art results on the tasks of extracting complex schemas.

Generate, Discriminate, Evolve: Enhancing Context Faithfulness via Fine-Grained Sentence-Level Self-Evolution (2025.findings-acl)

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Kun Li, Tianhua Zhang, Yunxiang Li, Hongyin Luo, Abdalla Mohamed Salama Sayed Moustafa, Xixin Wu, James R. Glass, Helen M. Meng

Challenge:	Existing methods to improve context faithfulness in large language models are either inadequate or overlook the potential for self-improvement.
Approach:	They propose a framework that enhances context faithfulness through fine-grained sentence-level optimization.
Outcome:	Experiments on ASQA and ConFiQA datasets show that GenDiE surpasses baselines in faithfulness and correctness and exhibits robust performance for domain adaptation.

More Than Catastrophic Forgetting: Integrating General Capabilities For Domain-Specific LLMs (2024.emnlp-main)

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Chengyuan Liu, Yangyang Kang, Shihang Wang, Lizhi Qing, Fubang Zhao, Chao Wu, Changlong Sun, Kun Kuang, Fei Wu

RAG-Zeval: Enhancing RAG Responses Evaluator through End-to-End Reasoning and Ranking-Based Reinforcement Learning (2025.emnlp-main)

Challenge:	a recent study shows that performance on general tasks decreases after Large Language Models are fine-tuned on domain-specific tasks.
Approach:	They propose a general capability integration approach to integrate general capabilities and domain knowledge within a single instance.
Outcome:	The proposed method improves performance on domain-specific tasks by integrating general capabilities and domain knowledge.

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Kun Li, Yunxiang Li, Tianhua Zhang, Hongyin Luo, Xixin Wu, James R. Glass, Helen M. Meng

Low-Resource NLP

Challenge:	Existing evaluation frameworks rely on direct prompting of resource-intensive models with complex multi-stage prompts, introducing significant computational cost and underutilizing models’ reasoning capabilities.
Approach:	They propose a framework that trains evaluators with reinforcement learning to generate comprehensive and sound assessments with detailed explanation in one-pass.
Outcome:	The proposed framework outperforms baseline evaluation frameworks that rely on LLMs with 10-100 more parameters and achieves the strongest correlation with human judgments.

Calibrating Inference Time Alignment with Sequence-level Risk Accumulation (2026.acl-long)

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Shanwen Tan, Ziyang Dong, Wei Ju, Yiwei Fu, Hao Wu, Kun Wang, Yifan Wang, Ziyue Qiao

Word Mover’s Embedding: From Word2Vec to Document Embedding (D18-1)

Challenge:	Existing approaches to decode large language models (LLMs) often over-reject benign information, limiting their generalizability in real-world scenarios where harmful and benign information coexist.
Approach:	They propose a framework to regulate decoding alignments for Large Language Models (LLMs) they employ a reward-guided branch decoding paradigm to incorporate safety awareness during generation.
Outcome:	The proposed framework achieves superior performance on four attack benchmarks and two neutral datasets.

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Lingfei Wu, Ian En-Hsu Yen, Kun Xu, Fangli Xu, Avinash Balakrishnan, Pin-Yu Chen, Pradeep Ravikumar, Michael J. Witbrock

Representation Learning

Challenge:	Recent work has demonstrated that Word Mover’s Distance (WMD) that aligns semantically similar words yields unprecedented KNN classification accuracy.
Approach:	They propose a Word Mover’s Distance (WMD) method that aligns semantically similar words to generate unsupervised sentences or documents embeddings.
Outcome:	The proposed method consistently outperforms state-of-the-art techniques on 9 benchmark text classification datasets and 22 textual similarity tasks.

CoEvo: Coevolution of LLM and Retrieval Model for Domain-Specific Information Retrieval (2025.emnlp-main)

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Ang Li, Yiquan Wu, Yinghao Hu, Lizhi Qing, Shihang Wang, Chengyuan Liu, Tao Wu, Adam Jatowt, Ming Cai, Fei Wu, Kun Kuang

Information Retrieval

Challenge:	Recent methods to enhance queries by generating intermediary elements can degrade retrieval performance . combining LLMs and retrievers can be difficult, resulting in unreliable or irrelevant intermediaries .
Approach:	They propose a framework that facilitates the coevolution of large language models and retrieval models.
Outcome:	The proposed framework facilitates the coevolution of LLMs and retrieval models.

Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration (2023.emnlp-main)

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Yiquan Wu, Siying Zhou, Yifei Liu, Weiming Lu, Xiaozhong Liu, Yating Zhang, Changlong Sun, Fei Wu, Kun Kuang

Green & Sustainable NLP

Challenge:	Recent advances in deep learning have enabled a variety of techniques to be used to solve the LJP task.
Approach:	They propose a framework that leverages the strength of both LLMs and domain-specific models in the context of precedents.
Outcome:	The proposed framework leverages the strength of both LLM and domain models in the context of precedents.

Question Tells You Where the Answer Is: Intention-aware Long-Context KV Cache Compression (2026.acl-long)

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Liang Zhao, Xiaocheng Feng, Weihong Zhong, Lei Huang, Kun Zhu, Baoxin Wang, Dayong Wu, Guoping Hu, Ting Liu, Bing Qin

Challenge:	Recent methods to reduce the KV cache size fail to identify crucial KVs for generation while excluding others accurately, resulting in severe information loss.
Approach:	They propose an intention-aware KV cache eviction method that identifies and retains crucial KVs according to the attention distribution of intention, which semantically reflects the user’s goal and determines which part of the context is relevant.
Outcome:	The proposed method can maintain the model performance while reducing the KV cache size from 128K to 2K, leading to a 6.3x increase in decoding speed and 7.8x enhancement in memory efficiency compared to the default setting.

Generative Frame Sampler for Long Video Understanding (2025.findings-acl)

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Linli Yao, Haoning Wu, Kun Ouyang, Yuanxing Zhang, Caiming Xiong, Bei Chen, Xu Sun, Junnan Li

SoMeLVLM: A Large Vision Language Model for Social Media Processing (2024.findings-acl)

Challenge:	Existing video large language models (LMMs) employ an impedance of thousands of frames to understand long videos.
Approach:	They propose a plug-and-play module integrated with VideoLLMs to facilitate efficient lengthy video perception.
Outcome:	The proposed module boosts the performance of open-source VideoLLMs and proprietary assistants on long-form video benchmarks.

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Xinnong Zhang, Haoyu Kuang, Xinyi Mou, Hanjia Lyu, Kun Wu, Siming Chen, Jiebo Luo, Xuanjing Huang, Zhongyu Wei

Unsupervised Context Rewriting for Open Domain Conversation (D19-1)

Challenge:	Genereal domain large models lack nuanced multimodal understanding of social media . general domain models focus more on text than other modalities, which is not consistent with real-world user habits.
Approach:	They propose a Large Vision Language Model for Social Media Processing that combines five key capabilities to understand and generate real social media behavior.
Outcome:	The proposed model achieves state-of-the-art performance in multiple social media tasks.

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Kun Zhou, Kai Zhang, Yu Wu, Shujie Liu, Jingsong Yu

Text Generation

Challenge:	Existing approaches to model conversation context have drawbacks, such as lack of coreferences and long dependency.
Approach:	They propose a context rewriting method which explicitly rewrites the last utterance by considering context history.
Outcome:	The proposed method outperforms baselines in terms of rewriting quality, multi-turn response generation, and end-to-end retrieval-based chatbots.

Zero-shot Cross-lingual Conversational Semantic Role Labeling (2022.findings-naacl)

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Han Wu, Haochen Tan, Kun Xu, Shuqi Liu, Lianwei Wu, Linqi Song

Decoding on Graphs: Faithful and Sound Reasoning on Knowledge Graphs through Generation of Well-Formed Chains (2025.acl-long)

Challenge:	Xu et al., 2021: conversational semantic role labeling is under-explored in non-Chinese languages due to the lack of multilingual CSRL annotations for the parser training.
Approach:	They propose a model that implicitly learns conversational structure-aware representations with hierarchical encoders and elaborately designed pre-training objectives.
Outcome:	The proposed model outperforms baselines on English CSRL tests by large margins . it will facilitate the research of non-Chinese dialogue tasks which suffer from ellipsis and anaphora .

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Kun Li, Tianhua Zhang, Xixin Wu, Hongyin Luo, James R. Glass, Helen M. Meng

Structured Data in NLP

Challenge:	Existing research on the utilization of Knowledge Graphs (KGs) for large language models (LLMs) relies on subgraph retriever or iterative prompting, overlooking the potential synergy of LLMs’ step-wise reasoning capabilities and KGs’ structural nature.
Approach:	They propose a graph-aware constrained decoding framework that facilitates a deep synergy between LLMs and KGs by constraint derived from the topology of the KG.
Outcome:	The proposed framework can provide faithful and sound reasoning for KGQA.

Memformer: A Memory-Augmented Transformer for Sequence Modeling (2022.findings-aacl)

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Qingyang Wu, Zhenzhong Lan, Kun Qian, Jing Gu, Alborz Geramifard, Zhou Yu

Inductive-Deductive Strategy Reuse for Multi-Turn Instructional Dialogues (2024.emnlp-main)

Challenge:	Experimental results show that Memformer uses 8.1x less memory space and 3.2x faster on inference.
Approach:	They propose an efficient neural network that utilizes an external dynamic memory to encode and retrieve past information.
Outcome:	The proposed model achieves comparable performance against baselines with 8.1x less memory space and 3.2x faster on inference.

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Jiao Ou, Jiayu Wu, Che Liu, Fuzheng Zhang, Di Zhang, Kun Gai

ClaimGen-CN: A Large-scale Chinese Dataset for Legal Claim Generation (2025.findings-emnlp)

Challenge:	Existing methods target instruction dialogues as learning goal and fine-tune user simulators to pose instructions.
Approach:	They propose to use real instruction dialogues to model complex dialogue flows and pose high-quality instructions.
Outcome:	The proposed method generates diverse, in-depth, and insightful instructions for a given dialogue history.

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Siying Zhou, Yiquan Wu, Hui Chen, Xueyu Hu, Kun Kuang, Adam Jatowt, Chunyan Zheng, Fei Wu

Challenge:	Currently, legal claims are not being used by non-professionals.
Approach:	They construct a dataset for Chinese legal claim generation task and then use it to evaluate the generated claims.
Outcome:	The proposed dataset is the first for the Chinese legal claim generation task and will be made publicly available.

TokenSelect: Efficient Long-Context Inference and Length Extrapolation for LLMs via Dynamic Token-Level KV Cache Selection (2025.emnlp-main)

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Wei Wu, Zhuoshi Pan, Kun Fu, Chao Wang, Liyi Chen, Yunchu Bai, Tianfu Wang, Zheng Wang, Hui Xiong

MoRE: A Mixture of Low-Rank Experts for Adaptive Multi-Task Learning (2025.findings-acl)

Challenge:	Rapid advances in Large Language Models have spurred demand for processing extended context sequences . however, performance degradation due to sequence lengths out-of-distribution and excessively long inference times are limiting LLMs in long-context scenarios.
Approach:	They propose a training-free method for efficient and accurate long-context inference . they selectively involves a few critical KV cache tokens in attention calculation .
Outcome:	The proposed method speeds up attention computation and accelerates inference time while reducing selection overhead.

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Dacao Zhang, Kun Zhang, Shimao Chu, Le Wu, Xin Li, Si Wei

Fine-tuning Large Language Models for Improving Factuality in Legal Question Answering (2025.coling-main)

Challenge:	Recent advances in Large Language Models (LLMs) have revolutionized various domains, offering unprecedented performance across numerous tasks.
Approach:	They propose a new Mixture of Low-Rank Experts (MoRE) for multi-task PEFT to improve performance of LLMs with fewer parameters.
Outcome:	The proposed method improves performance over multiple tasks and no additional inference cost.

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Yinghao Hu, Leilei Gan, Wenyi Xiao, Kun Kuang, Fei Wu

Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis (2022.findings-acl)

Challenge:	Hallucination remains a critical challenge in large language models (LLMs) in high-stake domains such as legal question answering.
Approach:	They propose a method to mitigate hallucination in legal question answering by using behavior cloning and a novel Hard Sample-aware Direct Preference Optimization.
Outcome:	The proposed method improves non-hallucinated Statute Rate, Statute Relevance Rate, Legal Claim Truthfulness, and traditional metrics.

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Kai Zhang, Kun Zhang, Mengdi Zhang, Hongke Zhao, Qi Liu, Wei Wu, Enhong Chen

Sentiment Analysis

Challenge:	Aspect-based sentiment analysis (ABSA) predicts sentiment polarity towards a specific aspect in a sentence.
Approach:	They propose to use a dynamic aspect-oriented semantics-based method to learn ABSA.
Outcome:	The proposed method can learn dynamic aspect-oriented semantics for ABSA on three benchmark datasets.

KMatrix: A Flexible Heterogeneous Knowledge Enhancement Toolkit for Large Language Model (2024.emnlp-demo)

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Shun Wu, Di Wu, Kun Luo, XueYou Zhang, Jun Zhao, Kang Liu

KMatrix-2: A Comprehensive Heterogeneous Knowledge Collaborative Enhancement Toolkit for Large Language Model (2025.emnlp-demos)

Challenge:	Existing Knowledge-Enhanced Large Language Models (K-LLMs) toolkits focus on free-textual knowledge and lack robust datasets, models, and user-friendly experience.
Approach:	They propose a flexible heterogeneous knowledge enhancement toolkit to enhance Large Language Models (LLMs) using external knowledge.
Outcome:	KMatrix: a flexible heterogeneous knowledge enhancement toolkit for LLMs includes verbalizing-retrieval and parsing-query methods.

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Shun Wu, Di Wu, Wangtao Sun, Ziyang Huang, Xiaowei Yuan, Kun Luo, XueYou Zhang, Shizhu He, Jun Zhao, Kang Liu

SplitThenMerge: Token-Level Skill-Compositional Sparse Mixture-of-Experts for Complex Domain-Specific Tasks (2026.findings-acl)

Challenge:	Existing studies on K-LLMs systems focus on declarative knowledge and procedural knowledge (rules) .
Approach:	They propose to build a toolkit that supports comprehensive heterogeneous knowledge collaborative enhancement for Large Language Models (LLMs).
Outcome:	The proposed toolkit provides unified knowledge integration and joint knowledge retrieval methods to achieve more comprehensive heterogeneous knowledge collaborative enhancement.

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Yuting Huang, Jiawen Zhang, Yiquan Wu, Yinghao Hu, Fei Wu, Kun Kuang

Challenge:	Existing domain adaptation methods train heterogeneous skills together, making it difficult to reliably coordinate multiple skills when solving complex tasks.
Approach:	They propose a framework that decomposes domain competence into atomic skills and composes them dynamically during generation.
Outcome:	The proposed framework decomposes domain competence into atomic skills, trains them independently, and composes them dynamically during generation.

Composable Cross-prompt Essay Scoring by Merging Models (2025.emnlp-main)

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Sanwoo Lee, Kun Liang, Yunfang Wu

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

Challenge:	Existing approaches to cross-prompt automated essay scoring use all available sources . however, using multiple sources for continual adaptation raises privacy concerns .
Approach:	They propose a source-free adaptation approach that selectively merges the parameters of individual models without further access to the source datasets.
Outcome:	The proposed method outperforms joint-training methods on all sources while maintaining computational efficiency.

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

BertGCN: Transductive Text Classification by Combining GNN and BERT (2021.findings-acl)

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

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Yuxiao Lin, Yuxian Meng, Xiaofei Sun, Qinghong Han, Kun Kuang, Jiwei Li, Fei Wu

Information Retrieval

Challenge:	Text classification is a core task in natural language processing (NLP) Graph neural networks (GNNs) serve as an effective approach for transductive learning.
Approach:	They propose a model that combines large scale pretraining and transductive learning for text classification.
Outcome:	The proposed model achieves SOTA performance on a wide range of datasets.

On the Generation of Medical Dialogs for COVID-19 (2021.acl-short)

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Meng Zhou, Zechen Li, Bowen Tan, Guangtao Zeng, Wenmian Yang, Xuehai He, Zeqian Ju, Subrato Chakravorty, Shu Chen, Xingyi Yang, Yichen Zhang, Qingyang Wu, Zhou Yu, Kun Xu, Eric Xing, Pengtao Xie

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

Challenge:	under the pandemic of COVID-19, people experiencing COVI D19-related symptoms have a pressing need to consult doctors.
Approach:	They develop a medical dialog system that can provide COVID19-related consultations . they use two dialog datasets containing conversations between doctors and patients .
Outcome:	The proposed system can provide COVID19-related consultations, but is too small compared with general-domain dialog datasets.

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

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

Chain-of-Quizzes: Pedagogy-inspired Example Selection in In-Context-Learning (2024.findings-acl)

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Yiquan Wu, Anlai Zhou, Yuhang Liu, Yifei Liu, Adam Jatowt, Weiming Lu, Jun Xiao, Kun Kuang

Low-Resource NLP

Challenge:	In-context learning (ICL) is a powerful tool for enhancing large language models (LLMs) by mimicking the human learning process.
Approach:	They propose a Chain-of-Quizzes framework that uses LLMs to answer a quiz to sift 'good' examples, combine them iteratively with the increasing complexity, and utilize a final exam to gauge the combined example chains.
Outcome:	The proposed framework outperforms baseline models on diverse reasoning datasets and shows that it is scalable and can be used in future research.

Exploiting Contrastive Learning and Numerical Evidence for Confusing Legal Judgment Prediction (2023.findings-emnlp)

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Leilei Gan, Baokui Li, Kun Kuang, Yating Zhang, Lei Wang, Anh Luu, Yi Yang, Fei Wu

CIA: Inferring the Communication Topology from LLM-based Multi-Agent Systems (2026.acl-long)

Challenge:	Existing studies fail to distinguish different classification errors with a standard cross-entropy classification loss and ignore the numbers in the fact description for predicting the term of penalty.
Approach:	They propose to extract crime amounts from fact description and use them to learn distinguishable representations to exploit the numbers in the fact description for predicting the term of penalty.
Outcome:	The proposed method achieves state-of-the-art results on real-world datasets and ablation studies demonstrate the effectiveness of each component.

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Yongxuan Wu, Xixun Lin, He Zhang, Nan Sun, Kun Wang, Chuan Zhou, Shirui Pan, Yanan Cao

Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model (D18-1)

Challenge:	LLM-based multi-agent systems (MAS) have demonstrated remarkable capabilities in solving complex tasks.
Approach:	They propose a communication inference attack that constructs new adversarial queries to induce intermediate agents’ reasoning outputs and models their semantic correlations through the global bias disentanglement and LLM-guided weak supervision.
Outcome:	The proposed attack achieves an average AUC of 0.87 and a peak AUC up to 0.99, revealing the privacy risk in MAS.

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Kun Xu, Lingfei Wu, Zhiguo Wang, Mo Yu, Liwei Chen, Vadim Sheinin

Latent Learningscape Guided In-context Learning (2024.findings-acl)

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.

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Anlai Zhou, Sunshine Jiang, Yifei Liu, Yiquan Wu, Kun Kuang, Jun Xiao

Legal Judgment Prediction based on Knowledge-enhanced Multi-Task and Multi-Label Text Classification (2025.naacl-long)

Challenge:	Existing methods to select demonstrations based on surface-level semantic similarities fall short of identifying the most fitting ones.
Approach:	They propose a method that characterizes latent learningscape features of demonstrations and uses them to create more effective prompts.
Outcome:	The proposed method outperforms leading models in arithmetic, commonsense, and symbolic reasoning tasks showing an average increase in scores by 7.4 percentage points.

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Ang Li, Yiquan Wu, Ming Cai, Adam Jatowt, Xiang Zhou, Weiming Lu, Changlong Sun, Fei Wu, Kun Kuang

DuSQL: A Large-Scale and Pragmatic Chinese Text-to-SQL Dataset (2020.emnlp-main)

Challenge:	Legal judgment prediction (LJP) is an essential task for legal AI, aiming at predicting judgments based on the facts of a case.
Approach:	They propose a knowledge-enhanced approach that incorporates 'label-level knowledge' to enhance the representation of case facts for each task and 'task-level' knowledge to improve synergy.
Outcome:	The proposed method is effective in comparison to state-of-the-art (SOTA) baselines.

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Lijie Wang, Ao Zhang, Kun Wu, Ke Sun, Zhenghua Li, Hua Wu, Min Zhang, Haifeng Wang

Programming Languages in NLP

Challenge:	Existing text-to-SQL parsing methods mainly focus on English, but there is no labeled data available for the language . a larges-scale and pragmatic Chinese dataset is used for cross-domain text- to-Sql task .
Approach:	They propose a larges-scale Chinese dataset for a cross-domain text-to-SQL task . they analyze questions from several representative applications and use an effective data construction framework .
Outcome:	The proposed dataset contains 200 databases, 813 tables, and 23,797 question/SQL pairs.

PASUM: A Pre-training Architecture for Social Media User Modeling Based on Text Graph (2024.lrec-main)

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Kun Wu, Xinyi Mou, Lanqing Xue, Zhenzhe Ying, Weiqiang Wang, Qi Zhang, Xuanjing Huang, Zhongyu Wei

Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing (2021.emnlp-main)

Challenge:	Existing studies have incorporated different digital traces to better learn the representations of social media users, limited by overloaded text information and hard-to-collect social network information.
Approach:	They propose a Pre-training Architecture for Social Media User Modeling based on Text Graph and combine microblogs to represent social media users based upon the text graph model.
Outcome:	The proposed framework can represent users based on text even without social network information on microblogs.

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Kun Wu, Lijie Wang, Zhenghua Li, Ao Zhang, Xinyan Xiao, Hua Wu, Min Zhang, Haifeng Wang

Programming Languages in NLP

Challenge:	Existing approaches to parse text-to-SQL data are lacking labeled data for unseen evaluation databases.
Approach:	They propose a framework for enhancing SQL queries by automatically producing large numbers of SQL queries based on an abstract syntax tree grammar.
Outcome:	The proposed framework can produce high-quality natural language questions over strong baselines.

Domain-Adaptive Pretraining Methods for Dialogue Understanding (2021.acl-short)

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Han Wu, Kun Xu, Linfeng Song, Lifeng Jin, Haisong Zhang, Linqi Song

Embracing Imperfection: Simulating Students with Diverse Cognitive Levels Using LLM-based Agents (2025.acl-long)

Challenge:	Recent advances in pretraining methods have achieved promising results on NLP tasks . however, it is unclear which pretraining objective is the most effective for each downstream task .
Approach:	They evaluate the effectiveness of domain-adaptive pretraining objectives on downstream tasks . they use open-domain data to pretrain language models like BERT and SpanBERT .
Outcome:	The proposed model improves on two dialogue understanding tasks with domain-adaptive pretraining objectives.

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Tao Wu, Jingyuan Chen, Wang Lin, Mengze Li, Yumeng Zhu, Ang Li, Kun Kuang, Fei Wu

Challenge:	Large language models (LLMs) are becoming increasingly popular in education, enabling researchers to simulate students' learning patterns and learning patterns.
Approach:	They propose a training-free framework for student simulation that takes into account student cognitive diversity and realism.
Outcome:	The proposed model outperforms baseline models and achieves 100% improvement in simulation accuracy and realism.

Focus-aware Response Generation in Inquiry Conversation (2023.findings-acl)

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Yiquan Wu, Weiming Lu, Yating Zhang, Adam Jatowt, Jun Feng, Changlong Sun, Fei Wu, Kun Kuang

Evaluating Test-Time Scaling LLMs for Legal Reasoning: OpenAI o1, DeepSeek-R1, and Beyond (2025.findings-emnlp)

Challenge:	Existing studies on response generation focus on relevance and fluency, rarely paying attention to the focus.
Approach:	They propose a Focus-aware response generation method that takes the focus into consideration and optimizes a multi-level encoder and focal decoder to generate multiple candidate responses.
Outcome:	The proposed method generates candidate responses that correspond to different focuses and performs better on two orthogonal inquiry conversation datasets.

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Yinghao Hu, Yaoyao Yu, Leilei Gan, Bin Wei, Kun Kuang, Fei Wu

"I Don’t Know What to Say": A Fact-Filling Questionnaire Method to Help Non-Experts Talk to LegalAI Assistant (2026.findings-acl)

Challenge:	Experimental results show that Legal-R1 delivers competitive performance across diverse tasks.
Approach:	They propose to evaluate 12 large language models across 17 legal tasks across statutory and case-law traditions to determine their general reasoning performance.
Outcome:	The proposed model performs well across 17 legal tasks across statutory and case-law traditions.

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Yuting Huang, Yiquan Wu, Meitong Guo, Ang Li, Xiaozhong Liu, Keting Yin, Fei Wu, Kun Kuang

OS Agents: A Survey on MLLM-based Agents for Computer, Phone and Browser Use (2025.acl-long)

Challenge:	Recent advances in large language models (LLMs) have greatly expanded the scope of legal AI.
Approach:	They propose a method that generates questionnaires to help users refine queries . they leverage an iterative training process that collects valuable questionnaires .
Outcome:	The proposed method improves the completeness of queries and ensures the performance of domain-specific models in downstream legal tasks.

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Xueyu Hu, Tao Xiong, Biao Yi, Zishu Wei, Ruixuan Xiao, Yurun Chen, Jiasheng Ye, Meiling Tao, Xiangxin Zhou, Ziyu Zhao, Yuhuai Li, Shengze Xu, Shenzhi Wang, Xinchen Xu, Shuofei Qiao, Zhaokai Wang, Kun Kuang, Tieyong Zeng, Liang Wang, Jiwei Li, Yuchen Eleanor Jiang, Wangchunshu Zhou, Guoyin Wang, Keting Yin, Zhou Zhao, Hongxia Yang, Fan Wu, Shengyu Zhang, Fei Wu

BackdoorAgent: A Unified Framework for Backdoor Attacks on LLM-based Agents (2026.findings-acl)

Challenge:	a new generation of (M)LLMs is enabling the creation of superintelligent AI assistants . OS Agents can complete tasks autonomously and have the potential to significantly enhance the lives of billions of users worldwide.
Approach:	They propose to build OS Agents that operate within operating systems' GUIs and GUIs . they examine evaluation metrics and benchmarks to identify promising directions .
Outcome:	The proposed agents are based on operating systems (OS) and operating systems frameworks.

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Yunhao Feng, Yige Li, Yutao Wu, Yingshui Tan, Yanming Guo, Yifan Ding, Kun Zhai, Xingjun Ma, Yu-Gang Jiang

Robustness in NLP

Challenge:	Large language model (LLM) agents execute tasks through multi-step workflows that combine planning, memory, and tool use.
Approach:	They propose a modular framework that provides a unified view of backdoor threats in LLM agents.
Outcome:	The proposed framework provides a unified, agent-centric view of backdoor threats in LLM agents.

OneRec-Think: In-Text Reasoning for Generative Recommendation (2026.acl-long)

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Zhanyu Liu, Shiyao Wang, Xingmei Wang, Rongzhou Zhang, Jiaxin Deng, Honghui Bao, Jinghao Zhang, Wuchao Li, PengFei Zheng, Xiangyu Wu, Yifei Hu, Qigen Hu, Xinchen Luo, Lejian Ren, Zhang Zixing, Qianqian Wang, Kuo Cai, Yunfan Wu, Hongtao Cheng, Zexuan Cheng, Lu Ren, Huanjie Wang, Yi Su, Ruiming Tang, Kun Gai, Guorui Zhou