Papers by Dawei Wang

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

Challenge:	Existing methods for reinforcement learning with verifiable rewards suffer from limited exploration diversity and inefficient reasoning.
Approach:	They propose a method that rewards concise and correct reasoning while penalizing unnecessarily long reasoning chains.
Outcome:	Extensive experiments on Qwen and Llama models validate the effectiveness and efficiency of ROSE.

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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

CoRanking: Collaborative Ranking with Small and Large Ranking Agents (2025.findings-emnlp)

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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Wenhan Liu, Xinyu Ma, Yutao Zhu, Lixin Su, Shuaiqiang Wang, Dawei Yin, Zhicheng Dou

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

Challenge:	Listwise ranking based on Large Language Models (LLMs) has achieved state-of-the-art performance in Information Retrieval (IR) however, their effectiveness often depends on LLMs with massive parameter scales and computationally expensive sliding window processing, leading to substantial efficiency bottlenecks.
Approach:	They propose a Collaborative Ranking framework (CoRanking) for LLM-based listwise ranking based on large language models with massive parameter scales and computationally expensive sliding window processing.
Outcome:	The proposed framework reduces ranking latency by approximately 70% while improving effectiveness compared to the standalone large reranker.

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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

GENUINE: Graph Enhanced Multi-level Uncertainty Estimation for Large Language Models (2025.findings-emnlp)

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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Tuo Wang, Adithya Kulkarni, Tyler Cody, Peter A. Beling, Yujun Yan, Dawei Zhou

MasRouter: Learning to Route LLMs for Multi-Agent Systems (2025.acl-long)

Challenge:	Existing methods for estimation of uncertainty overlook semantic dependencies, authors say . genUINE: Graph ENhanced mUlti-level uncertainty Estimation for Large Language Models leverages dependency parse trees and hierarchical graph pooling .
Approach:	They propose a graph-enhanced mUlti-level uncertaINty estimation framework that leverages dependency parse trees and hierarchical graph pooling to refine uncertainty quantification.
Outcome:	The proposed framework achieves higher AUROC and lower calibration errors than existing methods.

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Yanwei Yue, Guibin Zhang, Boyang Liu, Guancheng Wan, Kun Wang, Dawei Cheng, Yiyan Qi

An Efficient Framework for Whole-Page Reranking via Single-Modal Supervision (2026.acl-industry)

Challenge:	Multi-agent systems (MAS) powered by Large Language Models (LLMs) have been demonstrated to push the boundaries of LLM capabilities, yet they often face significant costs and challenges in dynamic LLM selection.
Approach:	They propose a multi-agent system routing solution that integrates all components of MAS into a unified routing framework.
Outcome:	The proposed solution is high-performing, cost-effective, and efficient . it reduces overhead by up to 52.07 compared to current methods on HumanEval .

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Zishuai Zhang, Sihao Yu, null Xiewenyi, Ying Nie, Junfeng Wang, Zhiming Zheng, Dawei Yin, Hainan Zhang

Green & Sustainable NLP

Challenge:	Existing whole-page reranking methods require large-scale expert annotations to achieve high-quality results.
Approach:	They propose a whole-page reranking framework that converts single-modal rankers into page-level guidance by constructing budget-aware candidates for cross-modal annotations and distilling intra-modality preferences to align relevance scales across modalities.
Outcome:	The proposed framework reduces annotation costs by 70-90% while outperforming fully-annotated reranking baselines.

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

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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.

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.

Structural Bias for Aspect Sentiment Triplet Extraction (2022.coling-1)

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Chen Zhang, Lei Ren, Fang Ma, Jingang Wang, Wei Wu, Dawei Song

Sentiment Analysis

Challenge:	Existing structural bias adapters for aspect sentiment triplet extraction are under-confident . a large-scale dataset for ASTE shows the adapter is effective and efficient to a larger scale.
Approach:	They propose to use a structural adapter to integrate structural bias into pretrained language models . they propose to add a relative position structure in place of the syntactic dependency structure .
Outcome:	The proposed adapter achieves state-of-the-art performance over strong baselines, but with a light parameter demand and low latency.

MARS-RA: Rank Aggregation for Credit Assignment via Multimodal Comparisons in Embodied Multi-Agent Cooperation (2026.acl-long)

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Dawei Wang, Di Zhao, Xinyuan Liu, Marci Chi Ma, Xiaoyang Liu, Chengming Zhou, Gary Ushaw, Richard Davison

Robustness in NLP

Challenge:	Embodied AI systems are open, where agents may leave or enter mid-task due to hardware failures or task-related errors.
Approach:	They propose a framework that reformulates credit assignment as a rank aggregation problem using contribution-based pairwise comparisons among agents generated by large multimodal models.
Outcome:	The proposed framework can guide agents toward effective cooperation in complex tasks of different types.

Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents (2023.emnlp-main)

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Weiwei Sun, Lingyong Yan, Xinyu Ma, Shuaiqiang Wang, Pengjie Ren, Zhumin Chen, Dawei Yin, Zhaochun Ren

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

Challenge:	Existing work utilizes generative LLMs for Information Retrieval (IR) rather than direct passage ranking.
Approach:	They investigate generative LLMs such as ChatGPT and GPT-4 for relevance ranking in IR and use a test set to verify the model’s ability to rank unknown knowledge.
Outcome:	The proposed model outperforms a 3B supervised model on the BEIR benchmark.

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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

TP-RAG: Benchmarking Retrieval-Augmented Large Language Model Agents for Spatiotemporal-Aware Travel Planning (2025.emnlp-main)

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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Hang Ni, Fan Liu, Xinyu Ma, Lixin Su, Shuaiqiang Wang, Dawei Yin, Hui Xiong, Hao Liu

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

Challenge:	Existing studies on large language models (LLMs) focus on basic plan validity, but neglect critical aspects such as route efficiency, POI appeal, and real-time adaptability.
Approach:	They propose a benchmark for retrieval-augmented, spatiotemporal-aware travel planning that integrates retrieved trajectories with LLMs’ intrinsic reasoning.
Outcome:	The proposed framework improves spatial efficiency and POI rationality while challenging universality and robustness due to conflicting references and noisy data.

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

AdaSwitch: Adaptive Switching between Small and Large Agents for Effective Cloud-Local Collaborative Learning (2024.emnlp-main)

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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Hao Sun, Jiayi Wu, Hengyi Cai, Xiaochi Wei, Yue Feng, Bo Wang, Shuaiqiang Wang, Yan Zhang, Dawei Yin

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

Challenge:	Recent advances in large language models have been remarkable . users face a choice between using cloud-based LLMs for generation quality or local-based ones for lower computational cost .
Approach:	They propose a new LLM utilization paradigm that facilitates collaborative operation . they evaluate AdaSwitch across 7 benchmarks and compare it to other LLMs .
Outcome:	The proposed model improves performance of local and cloud agents across 7 benchmarks . it achieves competitive results compared to the cloud agent while utilizing less computational overhead.

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

The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG) (2024.findings-acl)

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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Shenglai Zeng, Jiankun Zhang, Pengfei He, Yiding Liu, Yue Xing, Han Xu, Jie Ren, Yi Chang, Shuaiqiang Wang, Dawei Yin, Jiliang Tang

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

Challenge:	Retrieval-augmented generation (RAG) is a powerful technique to facilitate language model generation with proprietary and private data, where data privacy is . a privacy issue that is currently under-explored, is posed by RAG.
Approach:	They propose to use retrieval-augmented generation (RAG) to facilitate language model generation with proprietary and private data where data privacy is a pivotal concern.
Outcome:	The proposed attack methods demonstrate that RAG can mitigate the old risks, i.e., leakage of the LLMs’ training data.

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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.

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

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

SDAR: A Synergistic Diffusion-AutoRegression Paradigm for Scalable Sequence Generation (2026.findings-acl)

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.

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Shuang Cheng, Yihan Bian, Dawei Liu, Yuhua Jiang, Yihao Liu, Linfeng Zhang, Qian Yao, Zhongbo Tian, Wenhai Wang, Qipeng Guo, Kai Chen, Biqing Qi, Bowen Zhou

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

Challenge:	Autoregressive (AR) language models are a dominant paradigm in the field of parallelism and non-causal modeling.
Approach:	They propose a blockwise discrete diffusion model that preserves AR-compatible serving while enabling parallel intra-block generation.
Outcome:	The proposed model achieves theoretical speedups over 5 and wall-clock speedup of 2.3 on H200 GPUs in latency-critical regimes.

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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.

Towards Verifiable Text Generation with Evolving Memory and Self-Reflection (2024.emnlp-main)

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Hao Sun, Hengyi Cai, Bo Wang, Yingyan Hou, Xiaochi Wei, Shuaiqiang Wang, Yan Zhang, Dawei Yin

Information Retrieval

Challenge:	Large language models (LLMs) often produce factually incorrect information, also known as hallucination.
Approach:	They propose a framework for verifiable text generation with evolving memory and self-reflection that incorporates long-term memory to retain documents and recent documents.
Outcome:	The proposed framework outperforms baselines on five datasets across three knowledge-intensive tasks.

Improving the Robustness of Large Language Models via Consistency Alignment (2024.lrec-main)

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Yukun Zhao, Lingyong Yan, Weiwei Sun, Guoliang Xing, Shuaiqiang Wang, Chong Meng, Zhicong Cheng, Zhaochun Ren, Dawei Yin

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

Challenge:	Large language models have shown tremendous success in following user instructions and generating helpful responses, but their robustness is still far from optimal.
Approach:	They propose a two-stage training framework that helps a model generalize on following instructions via similar instruction augmentations.
Outcome:	The proposed training framework improves diversity and aligns the model with human expectations by differentiating subtle differences in similar responses.

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

PhyVer: Physics-Grounded Material Claim Verification with Multi-Fidelity Physical Evidence (2026.acl-demo)

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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Jianpeng Chen, Wangzhi Zhan, Haohui Wang, Brian Mayer, Dongqi Fu, Dawei Zhou

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

Challenge:	Existing claim verification pipelines operate over text, producing ungrounded judgments.
Approach:	They propose a physics-grounded material claim verification system that can be used to verify claims with physical evidence.
Outcome:	The proposed system reduces MAE and sign-offs with experts over text-only GPT-5.1.

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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.

Sliding Windows Are Not the End: Exploring Full Ranking with Long-Context Large Language Models (2025.acl-long)

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Wenhan Liu, Xinyu Ma, Yutao Zhu, Ziliang Zhao, Shuaiqiang Wang, Dawei Yin, Zhicheng Dou

Learning Knowledge-Enhanced Contextual Language Representations for Domain Natural Language Understanding (2023.emnlp-main)

Challenge:	Existing methods for listwise passage ranking use sliding window approach, which is inefficient as it requires repetitive and serialized processing.
Approach:	They propose a listwise label construction approach and importance-aware learning objective for full ranking.
Outcome:	The proposed method outperforms existing methods in listwise ranking tasks.

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Taolin Zhang, Ruyao Xu, Chengyu Wang, Zhongjie Duan, Cen Chen, Minghui Qiu, Dawei Cheng, Xiaofeng He, Weining Qian

MoDification: Mixture of Depths Made Easy (2025.naacl-long)

Challenge:	Existing methods for pre-training KEPLMs with relational triples are difficult to adapt to close domains due to the lack of sufficient domain graph semantics.
Approach:	They propose a Knowledge-enhanced language representation learning framework for various closed domains that captures the implicit graph structure among the entities.
Outcome:	The proposed framework outperforms existing methods for pre-training KEPLMs in closed domains significantly.

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Chen Zhang, Meizhi Zhong, Qimeng Wang, Xuantao Lu, Zheyu Ye, Chengqiang Lu, Yan Gao, Yao Hu, Kehai Chen, Min Zhang, Dawei Song

Green & Sustainable NLP

Challenge:	Long-context efficiency is a trending topic in large language model (LLM) serving.
Approach:	They propose a method to combine long-context efficiency and mixture of depths to bring down both latency and memory.
Outcome:	The proposed method achieves 1.2 speedup in latency and 1.8 reduction in memory compared to original LLMs especially in long-context applications.

Beyond Literal Mapping: Benchmarking and Improving Non-Literal Translation Evaluation (2026.acl-long)

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Yanzhi Tian, Cunxiang Wang, Zeming Liu, Heyan Huang, Wenbo Yu, Dawei Song, Jie Tang, Yuhang Guo

Machine Translation

Challenge:	Large Language Models (LLMs) have advanced machine translation (MT) a meta-evaluation dataset focused on non-literal translations is lacking . experimental results show the inaccuracies of traditional MT metrics and the limitations of LLM-as-a-Judge.
Approach:	They propose a meta-evaluation framework that leverages sub-agents to evaluate machine translation metrics.
Outcome:	The proposed framework improves on the knowledge cutoff and score inconsistency problem.

XPrompt: Exploring the Extreme of Prompt Tuning (2022.emnlp-main)

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Fang Ma, Chen Zhang, Lei Ren, Jingang Wang, Qifan Wang, Wei Wu, Xiaojun Quan, Dawei Song

A Robust Semantics-based Watermark for Large Language Model against Paraphrasing (2024.findings-naacl)

Challenge:	Prompt tuning learns soft prompts to condition pre-trained Language Models for performing downstream tasks in a parameter-efficient manner.
Approach:	They propose a Prompt tuning model with an eXtremely small scale that learns soft prompts to condition the frozen Pre-trained Language Models for performing downstream tasks in a parameter-efficient manner.
Outcome:	The proposed model outperforms the vanilla Prompt-Tuning and can significantly improve across tasks and model scales.

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Jie Ren, Han Xu, Yiding Liu, Yingqian Cui, Shuaiqiang Wang, Dawei Yin, Jiliang Tang

Robustness in NLP

Challenge:	Existing methods to detect LLM-generated content use simple hashes of precedent tokens to partition vocabulary.
Approach:	They propose a semantics-based watermark framework to enhance the robustness against paraphrase.
Outcome:	The proposed framework is robust under different paraphrases and the semantic meaning of the sentences will be likely preserved under paraphrase.

SDAR-VL: Stable and Efficient Block-wise Diffusion for Vision-Language Understanding (2026.acl-long)

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Shuang Cheng, Yuhua Jiang, Zineng Zhou, Dawei Liu, Tao Wang, Linfeng Zhang, Biqing Qi, Bowen Zhou

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

Challenge:	Existing block-wise discrete diffusion models lack robust autoregressive (AR) decoders.
Approach:	They propose a block-wise discrete diffusion framework for large-scale vision-language understanding with a progressive beta noise curriculum.
Outcome:	The proposed framework improves training efficiency, convergence stability, and task performance over conventional block diffusion.

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Chen Zhang, Qiuchi Li, Dawei Song, Benyou Wang

Sentiment Analysis

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.

Minimal Distillation Schedule for Extreme Language Model Compression (2024.findings-eacl)

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Chen Zhang, Yang Yang, Qifan Wang, Jiahao Liu, Jingang Wang, Wei Wu, Dawei Song

Lifting the Curse of Capacity Gap in Distilling Language Models (2023.acl-long)

Challenge:	Existing methods for teacher assistant-based distillation require multiple trials to find the optimal teacher assistant.
Approach:	They propose a method that allows scheduling of an optimal teacher assistant in just one trial . they show that student performance is positively correlated with the scale-performance tradeoff .
Outcome:	The proposed method can select the optimal teacher assistant in just one trial . it can be used to compare performance of student and teacher assistants on GLUE benchmarks.

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Chen Zhang, Yang Yang, Jiahao Liu, Jingang Wang, Yunsen Xian, Benyou Wang, Dawei Song

MatchTIR: Fine-Grained Supervision for Tool-Integrated Reasoning via Bipartite Matching (2026.acl-long)

Challenge:	Existing studies have shown that pretrained language models require a tremendous amount of inference compute to perform.
Approach:	They propose to compress pretrained language models to small ones with a teacher-student paradigm to fill the capacity gap.
Outcome:	The proposed model achieves state-of-the-art performance at small FLOPs compared with competitive baselines.

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Changle Qu, Sunhao Dai, Hengyi Cai, Jun Xu, Shuaiqiang Wang, Dawei Yin

Reasoning

Challenge:	Existing reinforcement learning methods rely on outcome- or trajectory-level rewards, assigning uniform advantages to all steps within a trajectory.
Approach:	They propose a framework that introduces fine-grained supervision via bipartite matching-based turn-level reward assignment and dual-level advantage estimation.
Outcome:	The proposed framework surpasses the majority of 8B competitors on three benchmarks.

Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method (2024.naacl-long)

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Yukun Zhao, Lingyong Yan, Weiwei Sun, Guoliang Xing, Chong Meng, Shuaiqiang Wang, Zhicong Cheng, Zhaochun Ren, Dawei Yin

Uplift-RAG: Uplift-Driven Knowledge Preference Alignment for Retrieval-Augmented Generation (2025.findings-emnlp)

Challenge:	Recent literature reveals that Large Language Models (LLMs) hallucinate intermittently, which impedes their reliability for further utilization.
Approach:	They propose a self-detection method to detect which questions an LLM does not know by combining the two components to identify whether the model generates a non-factual response to the question.
Outcome:	The proposed method can detect which questions an LLM does not know across factoid question-answering, arithmetic reasoning, and commonsense reasoning tasks.

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Changle Qu, Sunhao Dai, Hengyi Cai, Yiyang Cheng, Jun Xu, Shuaiqiang Wang, Dawei Yin

Information Retrieval

Challenge:	Existing efforts to estimate document utility rely on downstream generation performance, which conflates the influence of external documents with the intrinsic knowledge of the LLM.
Approach:	They propose an uplift-based definition of document utility that quantifies each document’s marginal benefit over the LLM’s internal knowledge.
Outcome:	The proposed framework improves the performance of the LLM by incorporating external retrieved documents into the model.

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

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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.

CTR-Guided Generative Query Suggestion in Conversational Search (2025.emnlp-industry)

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Erxue Min, Hsiu-Yuan Huang, Xihong Yang, Min Yang, Xin Jia, Yunfang Wu, Hengyi Cai, Junfeng Wang, Shuaiqiang Wang, Dawei Yin

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

Challenge:	Generating effective query suggestions requires aligning model outputs with user click preferences.
Approach:	They propose a generative framework that leverages click modeling to denoise implicit feedback and enables reliable preference optimization for improving real-world user engagement.
Outcome:	The proposed framework outperforms strong baselines in CTR, relevance, diversity and diversity.

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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

Mitigating Hallucinations in Large Vision-Language Models via Entity-Centric Multimodal Preference Optimization (2025.emnlp-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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Jiulong Wu, Zhengliang Shi, Shuaiqiang Wang, Jizhou Huang, Dawei Yin, Lingyong Yan, Min Cao, Min Zhang

DiQAD: A Benchmark Dataset for Open-domain Dialogue Quality Assessment (2023.findings-emnlp)

Challenge:	Existing preference alignment methods focus on aligning model responses with human preferences while neglecting image-text modality alignment.
Approach:	They propose Entity-centric Multimodal Preference Optimization to improve modality alignment . they use open-source instruction datasets to automatically construct high-quality preference data .
Outcome:	The proposed approach reduces hallucination rates by 80.4% on Object HalBench and 52.6% on MM HalBech.

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Yukun Zhao, Lingyong Yan, Weiwei Sun, Chong Meng, Shuaiqiang Wang, Zhicong Cheng, Zhaochun Ren, Dawei Yin

Boosting Event Extraction with Denoised Structure-to-Text Augmentation (2023.findings-acl)

Challenge:	Existing studies on dialogue quality assessment are uncapable of providing an end-to-end and human-epistemic assessment dataset . open-domain dialogue assessment is complicated and costly, but it can be done by recruiting human evaluators.
Approach:	They propose a large-scale dialogue quality assessment dataset for automatically assessing open-domain dialogue quality.
Outcome:	The proposed dataset is openly accessible at https://github.com/yukunZhao/Dialogue_quality_evaluation.

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Bo Wang, Heyan Huang, Xiaochi Wei, Ge Shi, Xiao Liu, Chong Feng, Tong Zhou, Shuaiqiang Wang, Dawei Yin

Information Extraction & Text Mining

Challenge:	Existing methods for event extraction neglect grammatical incorrectness, structure misalignment, and semantic drifting . et al., 2004; Ahn, 2006) show that the proposed method generates more diverse text representations for event extracting compared with the state-of-the-art.
Approach:	They propose a framework for event extraction that generates additional training data and iteratively selects the effective subset from the generated training data.
Outcome:	The proposed method generates more diverse representations of training data and achieves comparable results with the state-of-the-art.

Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation (2025.acl-long)

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Shijie Wang, Wenqi Fan, Yue Feng, Lin Shanru, Xinyu Ma, Shuaiqiang Wang, Dawei Yin

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

Challenge:	Large Language Models (LLMs) have produced significant advances in the field of recommender systems.
Approach:	They propose to retrieve up-to-date structure information from the knowledge graph to augment recommendations by leveraging external knowledge sources.
Outcome:	Experiments on a large dataset show that the proposed method is effective in enhancing LLM-based recommendations.

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

InternLM-Law: An Open-Sourced Chinese Legal Large Language Model (2025.coling-main)

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).

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Zhiwei Fei, Songyang Zhang, Xiaoyu Shen, Dawei Zhu, Xiao Wang, Jidong Ge, Vincent Ng

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

Challenge:	InternLM-Law is a large language model (LLM) tailored for addressing diverse legal tasks related to Chinese laws.
Approach:	They introduce a large language model (LLM) tailored for addressing diverse legal tasks related to Chinese laws.
Outcome:	The proposed model performs better than existing models in a variety of legal tasks related to Chinese laws.

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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.

UEGP: Unified Expert-Guided Pre-training for Knowledge Rekindle (2024.findings-naacl)

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Yutao Mou, Kexiang Wang, Jianhe Lin, Dehong Ma, Jun Fan, Daiting Shi, Zhicong Cheng, Gu Simiu, Dawei Yin, Weiran Xu

LLMs + Persona-Plug = Personalized LLMs (2025.acl-long)

Challenge:	Existing paradigms for pre-training and fine-tuning have limitations . knowledge rekindle aims to break through performance upper bounds of experts without introducing additional annotated data.
Approach:	They propose a new paradigm for pre-training and fine-tuning that aims to re-incorporate the fine- tuned expert model into the training cycle and break through performance upper bounds of experts.
Outcome:	The proposed model breaks through performance upper bounds of experts without additional annotated data.

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Jiongnan Liu, Yutao Zhu, Shuting Wang, Xiaochi Wei, Erxue Min, Yu Lu, Shuaiqiang Wang, Dawei Yin, Zhicheng Dou

KnowTuning: Knowledge-aware Fine-tuning for Large Language Models (2024.emnlp-main)

Challenge:	Large language models (LLMs) have demonstrated extraordinary capabilities in natural language understanding, generation, and reasoning.
Approach:	They propose a plug-and-play LLM model that embeds a user-specific embedding for each individual by modeling her historical contexts through a lightweight plug-in user embedder module.
Outcome:	Experiments on various tasks in the language model personalization (LaMP) benchmark show that the proposed model significantly outperforms existing personalized LLM approaches.

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Yougang Lyu, Lingyong Yan, Shuaiqiang Wang, Haibo Shi, Dawei Yin, Pengjie Ren, Zhumin Chen, Maarten Rijke, Zhaochun Ren

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

Challenge:	Large language models (LLMs) are a default solution for many natural language processing tasks.
Approach:	They propose a knowledge-aware fine-tuning method to improve LLMs' knowledge awareness . they propose augmentation and comparison stages to improve accuracy and reliability .
Outcome:	The proposed method generates more facts with less factual error rate under fine-grained facts evaluation.

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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

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

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

Dialogue Systems & Conversational Agents

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.

GOVERN: Gradient Orientation Vote Ensemble for Multi-Teacher Reinforced Distillation (2024.emnlp-industry)

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Wenjie Zhou, Zhenxin Ding, Xiaodong Zhang, Haibo Shi, Junfeng Wang, Dawei Yin

GSQ-Tuning: Group-Shared Exponents Integer in Fully Quantized Training for LLMs On-Device Fine-tuning (2025.findings-acl)

Challenge:	Pre-trained language models have achieved remarkable performance in OpenQA, but for practical deployment, knowledge distillation is crucial to maintain high performance while operating under computational constraints.
Approach:	They propose an algorithm to perform unsupervised knowledge distillation without the guidance of labels to achieve 99.5% of performance.
Outcome:	The proposed algorithm achieves 99.5% of performance in a commercial question-answering system.

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Sifan Zhou, Shuo Wang, Zhihang Yuan, Mingjia Shi, Yuzhang Shang, Dawei Yang

Making Pretrained Language Models Good Long-tailed Learners (2022.emnlp-main)

Challenge:	Large Language Models (LLMs) fine-tuning techniques require large Floating Point(FP) computation and are impractical for resource-constrained edge devices.
Approach:	They propose a framework for on-device LLM fine-tuning that eliminates the need for floating-point operations in both inference and training.
Outcome:	The proposed framework reduces memory and compute costs while reducing memory usage.

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Chen Zhang, Lei Ren, Jingang Wang, Wei Wu, Dawei Song

Divide-Then-Aggregate: An Efficient Tool Learning Method via Parallel Tool Invocation (2025.acl-long)

Challenge:	Prompt-tuning has shown appealing performance in few-shot classification . however, it is less promising in long-tailed classification due to long tail .
Approach:	They propose to use prompt-tuning to make pretrained language models at least good long-tailed learners by bridging the gap between prompt- and commonly used finetun.
Outcome:	The proposed method makes pretrained language models at least good long-tailed learners, bridging the gap between prompt-tuning and finetunation.

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Dongsheng Zhu, Weixian Shi, Zhengliang Shi, Zhaochun Ren, Shuaiqiang Wang, Lingyong Yan, Dawei Yin

Green & Sustainable NLP

Challenge:	Large Language Models (LLMs) demonstrate remarkable capabilities but their ability to autonomously execute complex real-world tasks remains limited.
Approach:	They propose a parallel tool invocation framework that decomposes tasks into parallel tool-using subtasks while aggregating results for subsequent decisions.
Outcome:	The proposed method significantly improves task performance while reducing token consumption and inference time.

VisLingInstruct: Elevating Zero-Shot Learning in Multi-Modal Language Models with Autonomous Instruction Optimization (2024.naacl-long)

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Dongsheng Zhu, Daniel Tang, Weidong Han, Jinghui Lu, Yukun Zhao, Guoliang Xing, Junfeng Wang, Dawei Yin