Papers by Dawei Li

What Does Your Smile Mean? Jointly Detecting Multi-Modal Sarcasm and Sentiment Using Quantum Probability (2021.findings-emnlp)

Challenge:	Entity name matching always retrieves irrelevant facts from the view of local entity words.
Approach:	They propose a knowledge selection approach and a generative model that integrates commonsense knowledge into the dialogue response generation by integrating commonsensical knowledge into a query.
Outcome:	The proposed approach improves on the most metrics and comparable to baselines.

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Yaochen Liu, Yazhou Zhang, Qiuchi Li, Benyou Wang, Dawei Song

Hierarchical Memory Organization for Wikipedia Generation (2025.acl-long)

Challenge:	Existing methods to model multi-modal sarcasm and sentiment are based on quantum probability . sarcasm and feelings embody intrinsic uncertainty of human cognition .
Approach:	They propose a quantum probability-driven multi-task learning framework for sarcasm and sentiment recognition using quantum superpositions and quantum interference.
Outcome:	The proposed model achieves state-of-the-art in multi-modal sarcasm and sentiment recognition.

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Eugene J. Yu, Dawei Zhu, Yifan Song, Xiangyu Wong, Jiebin Zhang, Wenxuan Shi, Xiaoguang Li, Qun Liu, Sujian Li

Challenge:	Existing methods for generating Wikipedia articles do not utilize memory directly for outline generation.
Approach:	They propose a method to generate Wikipedia articles autonomously by leveraging a hierarchical memory architecture.
Outcome:	The proposed framework outperforms baseline methods in producing informative and reliable articles.

Large Language Models are not Fair Evaluators (2024.acl-long)

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Peiyi Wang, Lei Li, Liang Chen, Zefan Cai, Dawei Zhu, Binghuai Lin, Yunbo Cao, Lingpeng Kong, Qi Liu, Tianyu Liu, Zhifang Sui

Fine-grained Contrastive Learning for Definition Generation (2022.aacl-main)

Challenge:	Existing evaluation frameworks that use large language models as referees are insufficient for accurately assessing their alignment with human intent.
Approach:	They propose a calibration framework to address positional bias in large language models as evaluators by manually annotating the “win/tie/lose” outcomes of responses from ChatGPT and Vicuna-13B in the Vicun A Benchmark’s question prompt.
Outcome:	The proposed framework alleviates evaluation bias, resulting in closer alignment with human judgments.

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Hengyuan Zhang, Dawei Li, Shiping Yang, Yanran Li

UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generation (2024.acl-long)

Challenge:	Recent pre-trained transformer-based definition generation models lack effective representation learning to contain full semantic components of the given word, leading to under-specific definitions.
Approach:	They propose a novel contrastive learning method that encourages the model to capture more detailed semantic representations from the definition sequence encoding.
Outcome:	The proposed method could generate more specific definitions compared with state-of-the-art models.

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Xun Liang, Shichao Song, Simin Niu, Zhiyu Li, Feiyu Xiong, Bo Tang, Yezhaohui Wang, Dawei He, Cheng Peng, Zhonghao Wang, Haiying Deng

AgentBank: Towards Generalized LLM Agents via Fine-Tuning on 50000+ Interaction Trajectories (2024.findings-emnlp)

Challenge:	Large language models (LLMs) produce hallucinated text, compromising their practical utility in professional contexts.
Approach:	They have developed an unconstrained hallucination generation evaluation benchmark that contains hallucines generated by large language models with minimal restrictions.
Outcome:	The proposed benchmarks are based on a Chinese-language dataset that is lacking in the field.

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Yifan Song, Weimin Xiong, Xiutian Zhao, Dawei Zhu, Wenhao Wu, Ke Wang, Cheng Li, Wei Peng, Sujian Li

Enhancing Retrieval-Augmented Generation via Evidence Tree Search (2025.acl-long)

Challenge:	Existing studies focus on specialized agents designed for particular tasks.
Approach:	They propose to scale annotated interaction trajectories and fine-tune LLMs on AgentBank to get a series of agent models, Samoyed.
Outcome:	The proposed model can scale to get generalized agent capabilities.

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Hao Sun, Hengyi Cai, Yuchen Li, Xuanbo Fan, Xiaochi Wei, Shuaiqiang Wang, Yan Zhang, Dawei Yin

Towards the Law of Capacity Gap in Distilling Language Models (2025.acl-long)

Challenge:	Evidence retrieval is used to enhance Large Language Models (LLMs) but in real-world applications, it often returns lengthy documents with redundant or irrelevant content, confusing downstream readers.
Approach:	They propose a framework that reformulates evidence retrieval as a dynamic tree expansion process.
Outcome:	The proposed framework outperforms existing methods on five datasets.

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Chen Zhang, Qiuchi Li, Dawei Song, Zheyu Ye, Yan Gao, Yao Hu

Reward Alignment Optimization: A Direct Point-wise Alignment Approach (2026.acl-long)

Challenge:	Language model (LM) distillation aims at distilling knowledge in a large teacher LM to a small student one.
Approach:	They propose to use the law of capacity gap to distill knowledge from a large teacher to a small student model.
Outcome:	The proposed model outperforms other language models on a larger scale by using the law of capacity gap inducted from a preliminary study on small-scale (3B) LMs.

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Zelin Li, Jia Leng, Dawei Song, Yangen Hu

Bridging External and Parametric Knowledge: Mitigating Hallucination of LLMs with Shared-Private Semantic Synergy in Dual-Stream Knowledge (2025.emnlp-main)

Challenge:	Existing Direct Alignment Algorithms (DAAs) are limiting in generalizaiton to implicit rewards.
Approach:	They propose a point-wise direct alignment method that uses an explicit reward model to specify exact target generation probabilities and align the policy offline towards them.
Outcome:	The proposed method outperforms existing direct alignment algorithms while enabling controllable target probability distributions.

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Yi Sui, Chaozhuo Li, Chen Zhang, Dawei Song, Qiuchi Li

WIKIGENBENCH:Exploring Full-length Wikipedia Generation under Real-World Scenario (2025.coling-main)

Challenge:	Retrieval-augmented generation (RAG) aims to mitigate the hallucination of Large Language Models (LLMs) however, external knowledge may contain noise and conflict with parametric knowledge of LLMs, leading to degraded performance.
Approach:	They propose a Dual-Stream Knowledge-Augmented Framework for Shared-Private Semantic Synergy that refines the traditional self-attention into a mixed-attention that distinguishes shared and private semantics for a controlled knowledge integration.
Outcome:	Extensive experiments show that the proposed framework achieves a superior performance over baselines.

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Jiebin Zhang, Eugene J. Yu, Qinyu Chen, Chenhao Xiong, Dawei Zhu, Han Qian, Mingbo Song, Weimin Xiong, Xiaoguang Li, Qun Liu, Sujian Li

Think Less, Know More: State-Aware Reasoning Compression with Knowledge Guidance for Efficient Reasoning (2026.findings-acl)

Challenge:	Existing efforts to generate Wikipedia articles for new events fall short of real-world application.
Approach:	They propose a benchmark to generate Wikipedia articles for new events under real-world scenarios . they use systematic metrics and LLM-based metrics to assess verifiability, organization, and other aspects aligned with real-life scenarios.
Outcome:	The proposed benchmarks show that hierarchical-based methods generate more comprehensive content while fine-tuned methods achieve better verifiability.

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Yi Sui, Chaozhuo Li, Dawei Song

DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer’s Disease Questions with Scientific Literature (2024.findings-emnlp)

Challenge:	Existing CoT compression methods struggle to balance accuracy and efficiency . long CoT reasoning also introduces an overthinking phenomenon, authors say .
Approach:	They propose a framework that performs step-wise CoT compression by modeling stage-specific redundancy sources and integrating with a retrieval-augmented guidance.
Outcome:	The proposed framework reduces average response length by 59.9% while improving accuracy by 4.8 points over existing methods.

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Dawei Li, Shu Yang, Zhen Tan, Jae Baik, Sukwon Yun, Joseph Lee, Aaron Chacko, Bojian Hou, Duy Duong-Tran, Ying Ding, Huan Liu, Li Shen, Tianlong Chen

Structured Data in NLP

Challenge:	Recent advances in large language models have achieved promising performances across various applications, but the challenge of integrating long-tail knowledge continues to impede the seamless adoption of LLMs in specialized domains.
Approach:	They propose a dynamic co-augmentation framework for the refinement of large language models and knowledge graphs in the context of Alzheimer's Disease.
Outcome:	The proposed framework can be used to study Alzheimer's Disease (AD) using LLMs and KGs.

How does Attention Affect the Model? (2021.findings-acl)

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Cheng Zhang, Qiuchi Li, Lingyu Hua, Dawei Song

Explainability & Interpretability in NLP

Challenge:	Existing studies on the effectiveness of attention in NLP do not consider changes in semantic capability of different components.
Approach:	They propose a framework that exploits a convex hull representation of sequence semantics in an n-dimensional Semantic Euclidean Space and defines indicators to capture the impact of attention on sequence semantic.
Outcome:	The proposed framework exploits a convex hull representation of sequence semantics in an n-dimensional Semantic Euclidean Space and defines indicators to capture the impact of attention on sequence semantic.

Multi-level Contrastive Learning for Script-based Character Understanding (2023.emnlp-main)

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Dawei Li, Hengyuan Zhang, Yanran Li, Shiping Yang

Adversarial Yet Cooperative: Multi-Perspective Reasoning in Retrieved-Augmented Language Models (2026.findings-acl)

Challenge:	Scripts are written text for plays, movies, or broadcasts.
Approach:	They propose a multi-level contrastive learning framework to capture characters’ global information in a fine-grained manner.
Outcome:	The proposed framework improves on three character understanding sub-tasks by a considerable margin.

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Can Xu, Lingyong Yan, Jiayi Wu, Haosen Wang, Shuaiqiang Wang, Yuchen Li, Jizhou Huang, Dawei Yin, Xiang Li

MILL: Mutual Verification with Large Language Models for Zero-Shot Query Expansion (2024.naacl-long)

Challenge:	Existing training paradigms rely on outcome-oriented rewards, which provide insufficient signal for shaping the complex, multi-step reasoning process.
Approach:	They propose a framework that integrates large reasoning models with retrieval-augmented generation to improve reasoning fidelity and verification rigor.
Outcome:	Experiments on multiple benchmarks demonstrate the effectiveness of the proposed framework.

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Pengyue Jia, Yiding Liu, Xiangyu Zhao, Xiaopeng Li, Changying Hao, Shuaiqiang Wang, Dawei Yin

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

Challenge:	Existing methods for query expansion lack corpus-specific knowledge and cost.
Approach:	They propose a query-query-document generation method that leverages large language models for mutual verification to produce diverse sub-queries and corresponding documents.
Outcome:	The proposed method is fully zero-shot and extensive experiments on three public benchmark datasets demonstrate its effectiveness over existing methods.

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

Information Retrieval

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

DRP: Distilled Reasoning Pruning with Mathematical Skill-aware Step Decomposition for Efficient Large Reasoning Models (2026.findings-acl)

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Yuxuan Jiang, Dawei Li, Francis Ferraro

Green & Sustainable NLP

Challenge:	Existing solutions to this problem are inference-time pruning and tuning-based distillation.
Approach:	They propose a framework that combines inference-time pruning with tuning-based distillation to enable efficient and accurate reasoning.
Outcome:	The proposed framework reduces token usage while improving accuracy on GSM8K and AIME tokens while avoiding performance drop.

ReasonRank: Empowering Passage Ranking with Strong Reasoning Ability (2026.acl-long)

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Wenhan Liu, Xinyu Ma, Weiwei Sun, Yutao Zhu, Yuchen Li, Dawei Yin, Zhicheng Dou

Reasoning

Challenge:	Existing rerankers perform poorly in complex ranking scenarios due to the scarcity of reasoning-intensive training data.
Approach:	They propose an automated reasoning-intensive training framework which generates high-quality training labels from training queries and passages.
Outcome:	The proposed model outperforms baselines significantly and achieves much lower latency than the pointwise reranker.

Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion? (2023.acl-long)

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Juanhui Li, Harry Shomer, Jiayuan Ding, Yiqi Wang, Yao Ma, Neil Shah, Jiliang Tang, Dawei Yin

Structured Data in NLP

Challenge:	Existing knowledge graphs are far from complete with large portions of triplets missing.
Approach:	They propose to use Graph Neural Networks to learn powerful embeddings to improve model performance.
Outcome:	The proposed models achieve comparable performance to MLP models, suggesting that MP may not be as crucial as previously thought.

DATA-CUBE: Data Curriculum for Instruction-based Sentence Representation Learning (2024.findings-acl)

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Yingqian Min, Kun Zhou, Dawei Gao, Xin Zhao, He Hu, Yaliang Li

HyperEdit: Unlocking Instruction-based Text Editing in LLMs via Hypernetworks (2026.findings-acl)

Challenge:	Existing methods to improve sentence representation learning (SRL) ignore the potential interference problems across tasks and instances.
Approach:	They propose a multi-task instruction tuning method that arranges the order of multi- task data for training to minimize interference risks.
Outcome:	The proposed method can boost the performance of state-of-the-art methods.

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Yiming Zeng, Jinghan Cao, Zexin Li, Wanhao Yu, Zhankai Ye, Dawei Xiang, Ting Hua, Xin Liu, Shangqian Gao, Tingting Yu

Text Generation

Challenge:	Existing approaches treat instruction-based text editing as a generic text generation problem. Existing methods either over-edit or fail to apply modifications consistently.
Approach:	They propose a framework that processes each editing request to best align with it.
Outcome:	The proposed framework achieves 9% improvement over the state-of-the-art model.

More Tokens, Lower Precision: Towards the Optimal Token-Precision Trade-off in KV Cache Compression (2025.findings-emnlp)

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Jiebin Zhang, Dawei Zhu, Yifan Song, Wenhao Wu, Chuqiao Kuang, Xiaoguang Li, Lifeng Shang, Qun Liu, Sujian Li

Challenge:	storing more tokens in the KV cache at lower precision can enhance the long-context performance of large language models.
Approach:	They propose a token-precision trade-off strategy to optimize KV cache compression . they also propose storing more tokens in the KV at lower precision .
Outcome:	The proposed method achieves an optimal point within the Information Bottleneck compared to standalone KV pruning or KV quantization.

LongAttn: Selecting Long-context Training Data via Token-level Attention (2025.findings-acl)

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Longyun Wu, Dawei Zhu, Guangxiang Zhao, Zhuocheng Yu, Junfeng Ran, Xiangyu Wong, Lin Sun, Sujian Li

InfoCL: Alleviating Catastrophic Forgetting in Continual Text Classification from An Information Theoretic Perspective (2023.findings-emnlp)

Challenge:	Existing methods to select long-context data often rely on sentence-level analysis, which can be greatly optimized in both performance and efficiency.
Approach:	They propose a token-level framework which quantifies long-range dependencies for LLMs by calculating token-based dependency strength and distribution uniformity of token scores.
Outcome:	The proposed framework quantifies long-range dependencies, enabling more accurate and efficient data selection.

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Yifan Song, Peiyi Wang, Weimin Xiong, Dawei Zhu, Tianyu Liu, Zhifang Sui, Sujian Li

Information Extraction & Text Mining

Challenge:	Recent studies have identified the severe performance decrease on analogous classes as a key factor for catastrophic forgetting.
Approach:	They propose a replay-based continual text classification method that uses fast-slow and current-past contrastive learning to perform mutual information maximization and better recover previously learned representations.
Outcome:	The proposed method achieves state-of-the-art on three text classification tasks.

PA-RAG: RAG Alignment via Multi-Perspective Preference Optimization (2025.naacl-long)

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Jiayi Wu, Hengyi Cai, Lingyong Yan, Hao Sun, Xiang Li, Shuaiqiang Wang, Dawei Yin, Ming Gao

DocLens: A Tool-Augmented Multi-Agent Framework for Long Visual Document Understanding (2026.acl-long)

Challenge:	Existing approaches to optimize RAG generators fail to align with RAG requirements thoroughly.
Approach:	They propose a method for optimizing the RAG generator from multiple preference perspectives to align with RAG requirements comprehensively.
Outcome:	The proposed method improves the performance of RAG generators by incorporating retrieved documents into the prompt.

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Dawei Zhu, Rui Meng, Jiefeng Chen, Sujian Li, Tomas Pfister, Jinsung Yoon

Multimodality

Challenge:	Existing approaches to localizing evidence from long visual documents fail on a fundamental challenge: evidence localization.
Approach:	They propose a tool-augmented multi-agent framework that “zooms in” on evidence like a lens.
Outcome:	The proposed framework achieves state-of-the-art performance on MMLongBench-Doc and FinRAGBench-V, surpassing even human experts.

Task Knowledge Injection via Interpolations and Reinstatement for Large Language Model Generalization (2025.findings-acl)

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Yukun Zhao, Lingyong Yan, Zhenyang Li, Shuaiqiang Wang, Zhumin Chen, Zhaochun Ren, Dawei Yin

ToolPRMBench: Evaluating and Advancing Process Reward Models for Tool-using Agents (2026.findings-acl)

Challenge:	Pre-trained large language models have been widely adopted to elicit their superior performance on downstream tasks, but instruction tuning may overfit them to specific task formats, compromising their generalization on unseen tasks.
Approach:	They propose to inject latent task adaptation and knowledge reinstatement into large language models to mitigate spurious correlations between inputs and targets.
Outcome:	The proposed method improves generalization on in-domain and out-of-domain unseen tasks.

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Dawei Li, Yuguang Yao, Zhen Tan, Huan Liu, Ruocheng Guo

Challenge:	Reward-guided search methods have shown potential in enhancing tool-using agents . however, there is a lack of reliable evaluation benchmarks for PRMs in tool-use settings .
Approach:	They propose a large-scale benchmark specifically designed to evaluate PRMs for tool-using agents.
Outcome:	The proposed benchmark shows that tool reward models perform better in tool-using environments.

CausalEval: Towards Better Causal Reasoning in Language Models (2025.naacl-long)

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Longxuan Yu, Delin Chen, Siheng Xiong, Qingyang Wu, Dawei Li, Zhikai Chen, Xiaoze Liu, Liangming Pan

Exploring Memorization in Fine-tuned Language Models (2024.acl-long)

Challenge:	Large language models (LLMs) have been used for a variety of tasks, including problem-solving, decision-making, and understanding of the world.
Approach:	They propose a review of existing methods aimed at enhancing LMs for causal reasoning . they categorize existing methods as reasoning engines or as helpers providing knowledge or data to traditional methods .
Outcome:	The proposed methods perform better than existing methods on a range of tasks.

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Shenglai Zeng, Yaxin Li, Jie Ren, Yiding Liu, Han Xu, Pengfei He, Yue Xing, Shuaiqiang Wang, Jiliang Tang, Dawei Yin

ViFT: Towards Visual Instruction-Free Fine-tuning for Large Vision-Language Models (2025.findings-emnlp)

Challenge:	Existing studies have shown that pre-trained langauge models tend to memorize and regenerate segments of their pre-training corpus when prompted appropriately.
Approach:	They conduct the first comprehensive analysis to explore language models’ memorization during fine-tuning across tasks.
Outcome:	The proposed analysis shows that memorization presents a strong disparity among different fine-tuning tasks.

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Zikang Liu, Kun Zhou, Xin Zhao, Dawei Gao, Yaliang Li, Ji-Rong Wen

Multimodality

Challenge:	Visual instruction tuning is the predominant technology in eliciting multimodal task-solving capabilities of large vision-language models.
Approach:	They propose a visual instruction-free fine-tuning framework for large vision-language models . they require only text-only instructions and image caption data during training .
Outcome:	The proposed framework is based on visual instruction tuning, but requires images as input . it can achieve state-of-the-art performance on several downstream benchmarks with less training data.

Agentic-R: Learning to Retrieve for Agentic Search (2026.findings-acl)

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Wenhan Liu, Xinyu Ma, Yutao Zhu, Yuchen Li, Daiting Shi, Dawei Yin, Zhicheng Dou

Information Retrieval

Challenge:	Existing retrievers for single-turn retrieval-augmented generation (RAG) rely on similarity-based retrievers, but similar passages are not always useful for final answer generation.
Approach:	They propose a retrieval-augmented-generation retriever that integrates reasoning with retrieval . they use local query-passage relevance and global answer correctness to measure passage utility .
Outcome:	The proposed retriever outperforms existing retrievers on QA benchmarks on seven single-hop and multi-hop searches.

Original Content Is All You Need! an Empirical Study on Leveraging Answer Summary for WikiHowQA Answer Selection Task (2022.coling-1)

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Liang Wen, Juan Li, Houfeng Wang, Yingwei Luo, Xiaolin Wang, Xiaodong Zhang, Zhicong Cheng, Dawei Yin

Tracing the Light of Thought: A Probabilistic Self- and Cross-Consistency Verification Mechanism Improving Mathematical Reasoning in LLMs (2026.findings-acl)

Challenge:	Existing answer selection approaches for community question answering lack additional answer summaries due to redundancy and lengthiness issues of crowdsourced answers.
Approach:	They constructed a dataset which contains a corresponding reference summary for each original lengthy answer.
Outcome:	The proposed model improves the performance of a question and candidate answer on a WikiHowQA dataset.

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Xiaoyang Liu, Dawei Wang, Tian Li, Huizhi Liang, Gary Ushaw, Richard Davison

Reasoning

Challenge:	Existing methods for evaluating reasoning paths are not efficient, but they are prone to errors.
Approach:	They propose a probabilistic self- and cross-consistency framework for mathematical reasoning that employs an accept-reject mechanism to encourage high-quality reasoning paths.
Outcome:	The proposed framework improves on 9 LLMs across 4 challenging benchmarks.

Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens (2026.findings-acl)

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Chengshuai Zhao, Zhen Tan, Pingchuan Ma, Dawei Li, Bohan Jiang, Yancheng Wang, Yingzhen Yang, Huan Liu

Reasoning

Challenge:	Chain-of-Thought (CoT) prompting has been shown to be effective in eliciting structured reasoning from large language models (LLMs).
Approach:	They propose a data distribution lens to understand when and why CoT reasoning fails . they propose 'data-based' training that trains LLMs from scratch .
Outcome:	The proposed model enables models to generate reasoning trajectories that approximate those observed during training.

Contextualization Distillation from Large Language Model for Knowledge Graph Completion (2024.findings-eacl)

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Dawei Li, Zhen Tan, Tianlong Chen, Huan Liu

C3KG: A Chinese Commonsense Conversation Knowledge Graph (2022.findings-acl)

Challenge:	Existing knowledge graph completion models lack textual information, which limits their performance . a plug-in-and-play approach is needed to train small models in descriptive context .
Approach:	They propose a plug-in-and-play approach to knowledge graph completion that prompts LLMs to generate descriptive context.
Outcome:	The proposed method improves performance on Wikipedia articles and synset definitions.

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Dawei Li, Yanran Li, Jiayi Zhang, Ke Li, Chen Wei, Jianwei Cui, Bin Wang

Natural Language Interfaces

Challenge:	Existing commonsense knowledge bases organize tuples in an isolated manner, causing problems for chatbots .
Approach:	They create a Chinese commonsense conversation knowledge graph which integrates social commonsensm and dialog flow information.
Outcome:	The proposed graph incorporates social commonsense knowledge and dialog flow information.

BPO: Towards Balanced Preference Optimization between Knowledge Breadth and Depth in Alignment (2025.naacl-long)

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Sizhe Wang, Yongqi Tong, Hengyuan Zhang, Dawei Li, Xin Zhang, Tianlong Chen

Challenge:	Reinforcement Learning with Human Feedback (RLHF) is the key to the success of large language models (LLMs) in recent years.
Approach:	They propose a method to balance the number of prompts and responses to improve knowledge breadth and knowledge depth by introducing gradient-based clustering to estimate the knowledge informativeness and usefulness of each augmented sample.
Outcome:	The proposed method outperforms baseline methods while maintaining training efficiency.

From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge (2025.emnlp-main)

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Dawei Li, Bohan Jiang, Liangjie Huang, Alimohammad Beigi, Chengshuai Zhao, Zhen Tan, Amrita Bhattacharjee, Yuxuan Jiang, Canyu Chen, Tianhao Wu, Kai Shu, Lu Cheng, Huan Liu

Challenge:	Recent advances in Large Language Models (LLMs) inspire the "LLM-as-a-judge" paradigm . traditional methods of assessment and evaluation fail in dynamic and open-ended scenarios .
Approach:	They propose a paradigm where LLMs are leveraged to perform scoring, ranking, or selection for machine learning evaluation scenarios.
Outcome:	The proposed model-based judgment and evaluation paradigms are based on large language models and are compared to the current model-driven evaluation paradigm.

Section-Aware Commonsense Knowledge-Grounded Dialogue Generation with Pre-trained Language Model (2022.coling-1)

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Sixing Wu, Ying Li, Ping Xue, Dawei Zhang, Zhonghai Wu

Balancing Speciality and Versatility: a Coarse to Fine Framework for Supervised Fine-tuning Large Language Model (2024.findings-acl)

Challenge:	Pre-trained language models can be expected to deepen the fusing of dialogue context and knowledge because of their superior ability of semantic understanding.
Approach:	They propose a two-stage framework to integrate a linearized knowledge into plan text using a ranking network PriorRanking to estimate the relevance of a retrieved knowledge fact.
Outcome:	The proposed framework improves the performance of pre-trained language models by using section-aware strategies to encode the linearized knowledge.

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Hengyuan Zhang, Yanru Wu, Dawei Li, Sak Yang, Rui Zhao, Yong Jiang, Fei Tan

Task-agnostic Distillation of Encoder-Decoder Language Models (2024.lrec-main)

Challenge:	Aligned Large Language Models exhibit remarkable versatility, capable of handling diverse real-world tasks.
Approach:	They propose a coarse to fine framework to fine-tune aligned Large Language Models to achieve a balance between speciality and versatility.
Outcome:	The proposed framework outperforms baseline methods across diverse tasks and model scales.

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Chen Zhang, Yang Yang, Qiuchi Li, Jingang Wang, Dawei Song

Can LLMs Learn from Previous Mistakes? Investigating LLMs’ Errors to Boost for Reasoning (2024.acl-long)

Challenge:	Existing distillation methods that focus on encoder-only LMs fail to handle the distillation of encoder decoder LM.
Approach:	They propose a method that finetunes pretrained language models (LMs) they propose 'MiniEnD' that allows for task-agnostic distillation of LMs.
Outcome:	The proposed distillation method is generally effective and competitive compared to other alternatives.

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Yongqi Tong, Dawei Li, Sizhe Wang, Yujia Wang, Fei Teng, Jingbo Shang

Low-Resource NLP

Challenge:	Recent studies have shown the benefits to LLMs from fine-tuning golden-standard Chain-of-Thought rationales or using them as correct examples in few-shot prompting.
Approach:	They propose a new benchmark to test the effectiveness of large language models by leveraging errors to enhance reasoning capabilities.
Outcome:	The proposed methods can be used to fine-tune models in correct and incorrect domains, rather than tuning models to learn ground truth in traditional methods.

EERPD: Leveraging Emotion and Emotion Regulation for Improving Personality Detection (2025.coling-main)

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Zheng Li, Sujian Li, Dawei Zhu, Qilong Ma, Weimin Xiong

RiTeK: A Dataset for Large Language Models Complex Reasoning over Textual Knowledge Graphs in Medicine (2026.findings-acl)

Challenge:	Existing methods for personality detection ignore the connection between psychological knowledge “emotion regulation” and personality traits.
Approach:	They propose to use emotion regulation and emotion features to retrieve few-shot samples and provide process CoTs for inferring labels from text.
Outcome:	The proposed method outperforms SOTA by 15.05/4.29 on the two benchmark datasets.

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Jiatan Huang, Mingchen Li, Zonghai Yao, Dawei Li, Yuxin Zhang, Zhichao Yang, Yongkang Xiao, Feiyun Ouyang, Xiaohan Li, Shuo Han, Hong yu

ConFiguRe: Exploring Discourse-level Chinese Figures of Speech (2022.coling-1)

Challenge:	Existing methods for retrieving medical textual knowledge Graphs struggle to perform well, a study finds . existing methods struggle to provide accurate answers to complex questions, he says .
Approach:	They synthesize user queries integrating diverse topological structures, relational information, and complex textual descriptions.
Outcome:	a new dataset for medical textual knowledge graphs shows that existing methods struggle to perform well . main bottlenecks lie in the scarcity of existing medical TKGs and the limited expressiveness of their topological structures .

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Dawei Zhu, Qiusi Zhan, Zhejian Zhou, Yifan Song, Jiebin Zhang, Sujian Li

Speech & Audio in NLP

Challenge:	Figures of speech often deviate from their literal meanings to express deeper semantic implications.
Approach:	They propose a concept of figurative unit, which is the carrier of a figure, and build a Chinese corpus for Contextualized Figure Recognition.
Outcome:	The proposed model is based on 12 types of figures commonly used in Chinese . it shows that the proposed tasks are challenging for existing models .

A Multi-task Learning Framework for Opinion Triplet Extraction (2020.findings-emnlp)

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Chen Zhang, Qiuchi Li, Dawei Song, Benyou Wang

Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study (2024.lrec-main)

Challenge:	Existing approaches to Aspect-based sentiment analysis (ABSA) use aspect terms and their corresponding sentiment polarities as a reference, but they lack opinion terms as .
Approach:	They propose a multi-task learning framework to extract aspect terms and opinion terms and parse their sentiment dependencies with a biaffine scorer.
Outcome:	The proposed framework outperforms baseline and state-of-the-art approaches on four SemEval benchmarks.

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Peiyu Liu, Zikang Liu, Ze-Feng Gao, Dawei Gao, Wayne Xin Zhao, Yaliang Li, Bolin Ding, Ji-Rong Wen

LLMs as World Models: Data-Driven and Human-Centered Pre-Event Simulation for Disaster Impact Assessment (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) require significant computational resources for deployment and use.
Approach:	They propose to use low-bit quantization methods to reduce memory footprint and increase inference rate to improve performance of Large Language Models.
Outcome:	The proposed methods can reduce the memory footprint and increase the inference rate of LLMs.

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Lingyao Li, Dawei Li, Zhenhui Ou, Xiaoran Xu, Jingxiao Liu, Zihui Ma, Runlong Yu, Min Deng

Challenge:	Recent advances in large language models (LLMs) show promise in simulating complex scenarios.
Approach:	They examine multiple LLMs to proactively estimate perceived earthquake impacts using multimodal datasets and multimodal imagery.
Outcome:	The framework generates Modified Mercalli Intensity (MMI) predictions at zip code and county scales using multimodal datasets.

KSAM: Infusing Multi-Source Knowledge into Dialogue Generation via Knowledge Source Aware Multi-Head Decoding (2022.findings-acl)

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Sixing Wu, Ying Li, Dawei Zhang, Zhonghai Wu

Stop When Enough: Adaptive Early-Stopping for Chain-of-Thought Reasoning (2026.acl-long)

Challenge:	Existing knowledge-enhanced methods only seek knowledge from a single source, resulting in insufficient coverage of a given knowledge source.
Approach:	They propose to use multiple independent decoder heads to infuse multi-source knowledge into dialogue generation more efficiently by incorporating external knowledge into the dialogue generation.
Outcome:	The proposed approach overcomes three challenges in infusing multi-source knowledge into dialogue generation more efficiently.

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Renliang Sun, Wei Cheng, Dawei Li, Haifeng Chen, Wei Wang

Low-Resource NLP

Challenge:	Chain-of-Thought reasoning has driven recent gains of large language models (LLMs) on reasoning-intensive tasks by externalizing intermediate steps.
Approach:	They propose a training-free framework that adaptively determines when to stop reasoning to mitigate overthinking.
Outcome:	The proposed framework reduces token usage by 20-55% while maintaining or improving accuracy compared to standard CoT prompting.

DORA: A Dual-Objective Reinforcement Learning Framework for Effective and Efficient Multimodal Agentic Search (2026.acl-long)

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Guangming Qin, Yuhao Deng, Yukun Zhao, Zhenyang Li, Junfeng Wang, Dawei Yin, Ye Yuan, Guoren Wang, Yizhou Yan, Chengliang Chai, Lei Cao

Diverse and Informative Dialogue Generation with Context-Specific Commonsense Knowledge Awareness (2020.acl-main)

Challenge:	Existing methods to train large language models overlook quality of intermediate search results . existing methods often invoke search calls during reasoning, making inference inefficient .
Approach:	They propose a dual-objective reinforcement learning framework to improve search strategies of MLLMs . DORA outperforms state-of-the-art methods, achieving up to 8.4% higher accuracy .
Outcome:	The proposed model outperforms state-of-the-art methods while reducing search calls by 9.7%.

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Sixing Wu, Ying Li, Dawei Zhang, Yang Zhou, Zhonghai Wu

Proactive Guidance of Multi-Turn Conversation in Industrial Search (2025.acl-industry)

Challenge:	Generative dialogue systems tend to produce generic and boring responses, causing boring conversations . a novel commonsense knowledge-aware dialogue generation model is proposed to solve this problem .
Approach:	They propose to retrieve and introduce knowledge facts from knowledge graphs to reduce boring conversations . they use a Felicitous Fact mechanism to help the model focus on context-relevant knowledge facts .
Outcome:	The proposed model outperforms the state-of-the-art approach in most experiments.

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Xiaoyu Li, Xiao Li, Li Gao, Yiding Liu, Xiaoyang Wang, Shuaiqiang Wang, Junfeng Wang, Dawei Yin

Natural Language Interfaces

Challenge:	Large Language Models (LLMs) have advanced multi-turn conversation systems, emphasizing the need for proactive guidance to enhance users’ interactions.
Approach:	They propose a goal-adaptive supervised fine-tuning framework that generates proactive guidance for users to click for the next turn of the conversation.
Outcome:	The proposed framework achieves 86.10% accuracy in offline evaluation (+23.95% over baseline) and 25.28% CTR in online deployment (149.06% relative improvement).

PsyPath: Psychologically-guided Self-Exploration for Personality Detection (2026.findings-acl)

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Zheng Li, Hongxin Ding, Chenyu Zhang, Weimin Xiong, Dawei Zhu, Sujian Li

Exploring Pre-trained Language Models for Event Extraction and Generation (P19-1)

Challenge:	Personality detection aims to label traits via identifying linguistic cues from written text.
Approach:	They propose a framework that allows large language models to generate and answer psychologically meaningful questions and a hybrid scoring mechanism to evaluate the generated nodes in the reasoning paths.
Outcome:	The proposed framework outperforms baselines on two benchmark datasets and significantly improves performance and interpretability in downstream tasks.

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Sen Yang, Dawei Feng, Linbo Qiao, Zhigang Kan, Dongsheng Li

Information Extraction & Text Mining

Challenge:	Existing methods to extract event data are laborious to create and limited in size.
Approach:	They propose an event extraction model to overcome the roles overlap problem by separating the argument prediction in terms of roles.
Outcome:	The proposed method surpasses existing methods on the ACE2005 dataset and improves on the previous methods.

Attribute-aware Sequence Network for Review Summarization (D19-1)

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Junjie Li, Xuepeng Wang, Dawei Yin, Chengqing Zong

Summarization

Challenge:	Existing review summarization systems generate summary only based on review content and neglect the authors’ attributes (e.g., gender, age, and occupation).
Approach:	They propose an Attribute-aware Sequence Network (ASN) to take the aforementioned users’ characteristics into account by encoding their attributes over the words.
Outcome:	The proposed model outperforms existing systems on tripAtt and human evaluation by taking the authors' attributes into account and incorporating attribute embedding and word-using habits into word prediction.

LongEmbed: Extending Embedding Models for Long Context Retrieval (2024.emnlp-main)

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Dawei Zhu, Liang Wang, Nan Yang, Yifan Song, Wenhao Wu, Furu Wei, Sujian Li

Representation Learning

Challenge:	Existing embedding models support only 512 input tokens, hindering their application in scenarios requiring long inputs.
Approach:	They evaluate the performance of existing embedding models by using a new benchmark and a training-free context window extension strategy.
Outcome:	The proposed model extends the input window of existing models by several folds.

Large Language Models for Data Annotation and Synthesis: A Survey (2024.emnlp-main)

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Zhen Tan, Dawei Li, Song Wang, Alimohammad Beigi, Bohan Jiang, Amrita Bhattacharjee, Mansooreh Karami, Jundong Li, Lu Cheng, Huan Liu

Diversity and Consistency: Exploring Visual Question-Answer Pair Generation (2021.findings-emnlp)

Challenge:	Existing surveys focus on LLMs' specific utility for data annotation and synthesis.
Approach:	They propose to use large language models to generate annotations from raw data . they also propose to review learning strategies for models utilizing LLM-generated annotations .
Outcome:	The proposed models can be used to improve the efficacy of machine learning models by generating and labeling raw data with relevant information.

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Sen Yang, Qingyu Zhou, Dawei Feng, Yang Liu, Chao Li, Yunbo Cao, Dongsheng Li

Multimodality

Challenge:	Existing tasks to generate question-answer pairs from visual images are under-explored.
Approach:	They propose a task that targets question-answer pair generation from visual images.
Outcome:	The proposed model can generate diverse or consistent QAPs on two benchmarks.

More is Better: Enhancing Open-Domain Dialogue Generation via Multi-Source Heterogeneous Knowledge (2021.emnlp-main)

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Sixing Wu, Ying Li, Minghui Wang, Dawei Zhang, Yang Zhou, Zhonghai Wu

CoUDA: Coherence Evaluation via Unified Data Augmentation (2024.naacl-long)

Challenge:	Existing knowledge-enhanced methods use a single-source homogeneous knowledge base with limited knowledge coverage.
Approach:	They propose a multi-source heterogeneous knowledge-enhanced dialogue generation model that leverages multiple knowledge sources to improve knowledge coverage.
Outcome:	The proposed model outperforms existing knowledge-enhanced models on a Chinese dataset and shows that it can leverage multiple heterogeneous knowledge sources to improve knowledge coverage.

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Dawei Zhu, Wenhao Wu, Yifan Song, Fangwei Zhu, Ziqiang Cao, Sujian Li

Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks (D19-1)

Challenge:	Existing data augmentations for coherence evaluation rely on heuristic rules and lack designing criteria.
Approach:	They propose a data augmentation framework that breaks down coherence into global and local aspects and designs augmentation strategies for both aspects.
Outcome:	The proposed framework surpasses recent models in scoring and ranking tasks with 233M parameters.

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Chen Zhang, Qiuchi Li, Dawei Song

HL-EncDec: A Hybrid-Level Encoder-Decoder for Neural Response Generation (C18-1)

Challenge:	Existing aspects-based sentiment classification models lack a mechanism to account for relevant syntactical constraints and word dependencies.
Approach:	They propose to build a Graph Convolutional Network over the dependency tree of a sentence to exploit syntactical information and word dependencies.
Outcome:	The proposed model is comparable to state-of-the-art models on three benchmarking collections.

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Sixing Wu, Dawei Zhang, Ying Li, Xing Xie, Zhonghai Wu

Text Generation

Challenge:	Existing models for conversation systems operate sentences at word-level . word-based models suffer from Unknown Words Issue and Preference Issue .
Approach:	They propose a hybrid-level Encoder-Decoder model which utilizes word-level features and character-level ones.
Outcome:	The proposed model outperforms non-word-level models in automatic metrics and human annotations on a Chinese corpus.

Expectation Preference Optimization: Reliable Preference Estimation for Improving the Reasoning Capability of Large Language Models (2025.emnlp-main)

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Zelin Li, Dawei Song

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

Challenge:	Pairwise preference optimization is used to improve supervised fine-tuning performance of large language models.
Approach:	They propose an algorithm that takes pairs of sample groups instead of single samples for preference learning.
Outcome:	The proposed algorithm outperforms baseline methods on reasoning benchmarks.

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