Papers by Chu Wang

L2Dir: Integrating L_2-Norm and Directional Alignment for Unsupervised Contrastive Representation Learning in Multimodal Retrieval (2026.acl-long)

Challenge:	Positional bias (PB) manifests as non-uniform sensitivity across contextual locations . previous studies have addressed PB by modifying the underlying architectures or employing extensive contextual awareness training.
Approach:	They propose a position-to-position knowledge distillation framework that leverages position-induced disparities to counteract PB.
Outcome:	The proposed framework reduces positional bias and improves performance on retrieval and reasoning tasks.

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Tianyu Zong, Rui Dai, Hongzhu Yi, Yuanxiang Wang, Zhenghao Zhang, Zhenyu Guan, Yujia Yang, Bingkang Shi, Yueyang Ding, Xiangxiang Chu, Kaikui Liu, Jungang Xu

Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning (2026.findings-acl)

Challenge:	Existing approaches to multimodal representation learning focus on directional alignment and embedding magnitudes (L2-norm) however, these methods often fail to account for the intrinsic role of L2-norm in the contrastive process.
Approach:	They propose a plug-and-play framework that optimizes L2-norm alignment and Directional consistency jointly.
Outcome:	The proposed framework achieves consistent and significant performance gains over established baselines across 95 tasks using UniIR and VLM2Vec-V2 frameworks.

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Yibo Yan, Shen Wang, Jiahao Huo, Jingheng Ye, Zhendong Chu, Xuming Hu, Philip S. Yu, Carla P Gomes, Bart Selman, Qingsong Wen

TC–RAG: Turing–Complete RAG’s Case study on Medical LLM Systems (2025.acl-long)

Challenge:	Current scientific reasoning models struggle with generalization across domains and fall short of multimodal perception.
Approach:	They propose to use multimodal large language models to integrate text, images, and other modalities to enhance scientific reasoning.
Outcome:	The proposed models can integrate text, images, and other modalities and improve reasoning across disciplines.

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Xinke Jiang, Yue Fang, Rihong Qiu, Haoyu Zhang, Yongxin Xu, Hao Chen, Wentao Zhang, Ruizhe Zhang, Yuchen Fang, Xinyu Ma, Xu Chu, Junfeng Zhao, Yasha Wang

Information Retrieval

Challenge:	Existing approaches to RAG neglect system state variables, resulting in poor performance and erroneous knowledge accumulation.
Approach:	They propose a framework that incorporates a Turing Complete System to manage state variables and manage retrieval halting.
Outcome:	The proposed framework improves on seven real-world healthcare datasets and shows that it is more accurate than existing methods.

Learning from Mistakes: Negative Reasoning Samples Enhance Out-of-Domain Generalization (2026.acl-long)

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Tian Xueyun, MingHua Ma, Bingbing Xu, Nuoyan Lyu, Wei Li, Heng Dong, Zheng Chu, Yuanzhuo Wang, Huawei Shen

An Information Bottleneck Perspective for Effective Noise Filtering on Retrieval-Augmented Generation (2024.acl-long)

Challenge:	Recent studies show that supervised fine-tuning (SFT) is a common approach for reasoning in large language models.
Approach:	They propose to use supervised fine-tuning (SFT) on chain-of-thought trajectories demonstrations . they find that incorporating negative traxories yields substantial OOD generalization gains .
Outcome:	The proposed scheme yields 5.51% OOD gain over positive-only training.

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Kun Zhu, Xiaocheng Feng, Xiyuan Du, Yuxuan Gu, Weijiang Yu, Haotian Wang, Qianglong Chen, Zheng Chu, Jingchang Chen, Bing Qin

Information Retrieval

Challenge:	Experimental results show that retrieval-augmented generation improves accuracy and relevance of large language models.
Approach:	They propose to introduce the information bottleneck theory into retrieval-augmented generation by maximizing mutual information between compression and ground output while minimizing mutual information .
Outcome:	The proposed approach improves accuracy and correctness of answer generation and conciseness with 2.5% compression rate.

UniEDU: Toward Unified and Efficient Large Multimodal Models for Educational Tasks (2025.emnlp-industry)

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Zhendong Chu, Jian Xie, Shen Wang, Zichao Wang, Qingsong Wen

Towards Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning (2020.coling-main)

Challenge:	Existing research has focused on plain text, while real-world K-12 scenarios often involve multimodal data.
Approach:	They propose a unified language and vision assistant called UniEDU for educational applications . it excels across multiple educational tasks while maintaining strong generalization capabilities . authors propose to use UniEDu for industry-scale deployment .
Outcome:	The proposed model excels across multiple educational tasks while maintaining strong generalization capabilities.

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Weipeng Huang, Xingyi Cheng, Kunlong Chen, Taifeng Wang, Wei Chu

Green & Sustainable NLP

Challenge:	Chinese word segmentation datasets have ambiguous annotation criteria resulting in multi-grained compounds.
Approach:	They propose a domain adaptive segmenter to exploit diverse annotation criteria of datasets . they use bidirectional encoder representations from transformers to introduce open-domain knowledge .
Outcome:	The proposed model outperforms the state-of-the-art models on 10 Chinese word datasets with superior efficiency.

FOREVER: Forgetting Curve-Inspired Memory Replay for Language Model Continual Learning (2026.acl-long)

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Yujie Feng, Hao Wang, Jian Li, Xu Chu, Zhaolu Kang, Yiran Liu, Yasha Wang, Philip S. Yu, Xiao-Ming Wu

Perplexity-Aware Data Scaling Law: Perplexity Landscapes Predict Performance for Continual Pre-training (2026.acl-long)

Challenge:	Continual learning (CL) for large language models (LLMs) aims to enable sequential knowledge acquisition without catastrophic forgetting.
Approach:	They propose a framework that aligns replay schedules with a model-centric notion of time.
Outcome:	Experiments on three benchmarks show that FOREVER consistently mitigates catastrophic forgetting.

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Lei Liu, Hao Zhu, Xiaoyan Yang, Yue Shen, Zhixuan Chu, Jian Wang, Jinjie Gu, Kui Ren

3DS: Medical Domain Adaptation of LLMs via Decomposed Difficulty-based Data Selection (2025.emnlp-main)

Challenge:	Large language models (LLMs) have impressive capabilities across a wide range of domains, but their generalpurpose pre-training objectives often leave them illsuited for specialized applications such as healthcare.
Approach:	They propose a perplexity-aware data scaling law that establishes a predictive relationship between the perplexities of domain-specific data and the test loss.
Outcome:	Experiments on medical and general-domain benchmarks show that the proposed scaling law consistently identifies near-optimal training subsets with significantly reduced data consumption.

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Hongxin Ding, Yue Fang, Runchuan Zhu, Xinke Jiang, Jinyang Zhang, Yongxin Xu, Weibin Liao, Xu Chu, Junfeng Zhao, Yasha Wang

Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework (2021.findings-emnlp)

Challenge:	Effective domain adaptation typically involves supervised fine-tuning on carefully selected instruction-tuned data.
Approach:	They propose a model-centric data selection framework that aligns data selection with the model’s knowledge distribution to improve model performance.
Outcome:	The proposed framework outperforms existing methods by up to 2.97% accuracy in the healthcare domain.

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Yaqing Wang, Haoda Chu, Chao Zhang, Jing Gao

Low-Resource NLP

Challenge:	Named entity recognition (NER) is a language understanding task that requires large amounts of in-domain labeled data to perform well.
Approach:	They propose a framework which learns from natural language supervision and enables the identification of never-seen entity classes without using in-domain labeled data.
Outcome:	The proposed method brings 10%, 23% and 26% improvements over baselines in few-shot learning, domain transfer and zero-shot settings respectively.

Analyzing Chain-of-thought Prompting in Black-Box Large Language Models via Estimated V-information (2024.lrec-main)

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Zecheng Wang, Chunshan Li, Zhao Yang, Qingbin Liu, Yanchao Hao, Xi Chen, Dianhui Chu, Dianbo Sui

MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)

Challenge:	Chain-of-Thought (CoT) prompting and large language models (LLMs) have shown great potential in improving performance on challenging reasoning tasks.
Approach:	They propose a new metric which extends the concept of pointwise V-information to black-box models and quantifies label-relevant new information introduced by CoT prompting.
Outcome:	The proposed metric extends the concept of pointwise V-information to black-box models, quantifying label-relevant new information introduced by CoT prompting beyond pre-existing label information.

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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He

Green & Sustainable NLP

Challenge:	Document images are characterized by higher resolutions, denser content, and more complex structural layouts.
Approach:	They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition.
Outcome:	The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead.

Self-Para-Consistency: Improving Reasoning Tasks at Low Cost for Large Language Models (2024.findings-acl)

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Wenqing Chen, Weicheng Wang, Zhixuan Chu, Kui Ren, Zibin Zheng, Zhichao Lu

A Unified Framework for Modeling Heterogeneous Financial Data via Dual-Granularity Prompting (2026.acl-industry)

Challenge:	Recent studies have shown that self-consistency decoding can improve performance for complex reasoning tasks with large language models.
Approach:	They propose a self-consistency decoding strategy that generates multiple paraphrases for each test question and then generates reasoning paths for the original and all the paraphrased questions based on greedy decoding.
Outcome:	The proposed strategy reduces the sampling number and improves performance on complex reasoning tasks.

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Yu Lei, Zixuan Wang, Yiqing Feng, Junru Zhang, Yahui Li, Liu Chu, Wang Tongyao, Dongyang Li

Infinity-Parser: Layout-Aware Reinforcement Learning with High-quality Document Parsing Dataset (2026.findings-acl)

Challenge:	Recent industrial credit scoring models rely heavily on manually tuned statistical learning methods due to the complexity of heterogeneous financial data and the challenge of modeling evolving creditworthiness.
Approach:	They propose a framework that reformulates credit scoring as a multi-scale sequential learning problem.
Outcome:	FinLangNet improves KS and bad debt rate by 6.3 pp in real world deployments.

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Baode Wang, Biao Wu, Weizhen Li, Meng Fang, Zuming Huang, Jun Huang, Yanjie Liang, Haozhe Wang, Ling Chen, Wei Chu, Yuan Qi

Graph Reasoning Paradigm: Structured and Symbolic Reasoning with Topology-Aware Reinforcement Learning for Large Language Models (2026.acl-long)

Challenge:	Existing supervised fine-tuning methods struggle to generalize across document types, leading to poor performance.
Approach:	They propose layoutRL, a reinforcement learning framework that optimizes layout understanding through composite rewards integrating normalized edit distance, paragraph count accuracy, and reading order preservation.
Outcome:	The proposed model outperforms specialized document parsing systems and general-purpose vision-language models on a broad range of document types, languages, and structural complexities.

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Runxuan Liu, Xianhao Ou, Xinyan Ma, Jiyuan Wang, Jiafeng Liang, Jiaqi Li, Tao He, Zheng Chu, Rongchuan Mu, Zekun Wang, Baoxin Wang, Dayong Wu, Ming Liu, Shijin Wang, Guoping Hu, Bing Qin

MuSe: Multi-Stage Graph Reasoning via Vision-Language Models (2026.acl-long)

Challenge:	Existing methods for long chain-of-thought (LCoT) are coarse-grained, reward hacking, and poor generalization.
Approach:	They propose a Long Chain-of-Thought (LCoT) model that integrates reinforcement learning with verifiable rewards with a process-aware verification approach.
Outcome:	The proposed model improves reasoning and code generation tasks while reducing the cost of training and performance bottlenecks.

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Guanyu Wang, Xu Chu, Zhijie Tan, Xinrong Chen, Tong Mo, Weiping Li

Towards Order Fairness: Mitigating LLMs Order Sensitivity through Dual Group Advantage Optimization (2026.acl-long)

Challenge:	Graph Neural Networks (GNNs) and graph transformers are inadequate for tasks with limited generalization.
Approach:	They propose a multi-stage graph reasoning framework based on vision-language models that incrementally samples and visualizes task-relevant subgraphs.
Outcome:	The proposed framework outperforms existing benchmarks in Graph-related tasks.

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Xu Chu, Guanyu Wang, Zhijie Tan, Xinrong Chen, Ziyu Li, Tong Mo, Weiping Li

HyKGE: A Hypothesis Knowledge Graph Enhanced RAG Framework for Accurate and Reliable Medical LLMs Responses (2025.acl-long)

Challenge:	Recent studies attempt to obtain optimal or suboptimal arrangements based on statistical results or using dataset-based search, but these methods increase inference overhead while leaving the model’s inherent order bias unresolved.
Approach:	They propose Dual Group Advantage Optimization (DGAO) which aims to improve model accuracy and order stability simultaneously.
Outcome:	The proposed method improves model accuracy and order stability while penalizing order-sensitive or incorrect responses.

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Xinke Jiang, Ruizhe Zhang, Yongxin Xu, Rihong Qiu, Yue Fang, Zhiyuan Wang, Jinyi Tang, Hongxin Ding, Xu Chu, Junfeng Zhao, Yasha Wang

Knowledge Representation

Challenge:	Recent approaches suffer from insufficient and repetitive knowledge retrieval, tedious and time-consuming query parsing, and monotonous knowledge utilization.
Approach:	They propose a retrieval-augmented generation framework which leverages LLMs’ powerful reasoning capacity to compensate for the incompleteness of user queries.
Outcome:	The proposed framework improves the accuracy and reliability of Large Language Models (LLMs) by combining the rich knowledge of LLMs with Hypothesis Outputs.

ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection (2026.findings-acl)

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Yibo Yan, Shen Wang, Jiahao Huo, Hang Li, Boyan Li, Jiamin Su, Xiong Gao, YiFan Zhang, Tianlong Xu, Zhendong Chu, Aoxiao Zhong, Kun Wang, Hui Xiong, Philip S. Yu, Xuming Hu, Qingsong Wen

Memory-Guided Hard Data Augmentation for Multimodal Named Entity Recognition (2026.findings-acl)

Challenge:	Current mathematical benchmarks focus on evaluating MLLMs’ problem-solving ability, yet there is a crucial gap in addressing more complex scenarios such as error detection.
Approach:	They propose to evaluate multimodal error detection by evaluating two sub-tasks error step identification and error categorization.
Outcome:	The proposed task evaluates MLLMs' ability to handle multimodal questions compared to text-only models.

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Xinyu Liu, Kai fu, Yinghan Shi, Quanyou Chu, Ming Du, Hongya Wang, Xiaojun Meng, Jiansheng Wei, Yanghua Xiao, Bo Xu

RADO: Reasoning Audit-Driven Optimization for Rigorous Reasoning in High-Stakes Domains (2026.acl-long)

Challenge:	Existing methods for Named Entity Recognition (NER) ignore the internal state of the target model.
Approach:	They propose a framework to repair model-specific errors by using a model-based approach . they employ cross-validation to identify model- specific Hard Data and a memory tree to induce macro-level error patterns from micro-level failures.
Outcome:	The proposed framework yields significant performance gains on Twitter and other platforms.

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Zhijie Tan, Xu Chu, Guanyu Wang, Ziyu Li, Weiping Li, Tong Mo

Visually-Guided Policy Optimization for Multimodal Reasoning (2026.acl-long)

Challenge:	Current reinforcement learning paradigms rely on outcome-based rewards, overlooking latent logical fallacies in intermediate steps.
Approach:	They propose a specialized audit model augmented with external tools to identify local logical ruptures and calibrate reward signals.
Outcome:	The proposed framework improves accuracy and logical rigor in high-stakes domains.

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Zengbin Wang, Feng Xiong, Liang Lin, Xuecai Hu, Yong Wang, Yanlin Wang, Man Zhang, Xiangxiang Chu

LLM Agents for Education: Advances and Applications (2025.findings-emnlp)

Challenge:	Existing RLVRs lack visual faithfulness due to text-dominated reasoning . a novel framework to reinforce visual focus during policy optimization is proposed .
Approach:	They propose a framework to reinforce visual focus during policy optimization using visual attention compensation mechanism.
Outcome:	The proposed framework exhibits better visual activation and superior performance in multimodal reasoning and visual-dependent tasks.

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Zhendong Chu, Shen Wang, Jian Xie, Tinghui Zhu, Yibo Yan, Jingheng Ye, Aoxiao Zhong, Xuming Hu, Jing Liang, Philip S. Yu, Qingsong Wen

Recurrent Knowledge Identification and Fusion for Language Model Continual Learning (2025.acl-long)

Challenge:	Large Language Model (LLM) agents are transforming education by automating complex tasks and enhancing both teaching and learning processes.
Approach:	This survey analyzes recent advances in applying Large Language Model agents to educational settings . it highlights ethical issues, hallucination and overreliance, and integration with existing ecosystems .
Outcome:	The authors analyze the technologies enabling LLM agents and highlight key challenges in deploying them in educational settings.

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Yujie Feng, Xujia Wang, Zexin Lu, Shenghong Fu, Guangyuan Shi, Yongxin Xu, Yasha Wang, Philip S. Yu, Xu Chu, Xiao-Ming Wu

Vision-Enhanced Semantic Entity Recognition in Document Images via Visually-Asymmetric Consistency Learning (2023.emnlp-main)

Challenge:	Continual learning (CL) is crucial for large language models without costly retraining.
Approach:	They propose a framework for recurrent knowledge identification and fusion that enables dynamic estimation of parameter importance distributions to enhance knowledge transfer.
Outcome:	The proposed framework mitigates catastrophic forgetting and enhances knowledge transfer.

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Hao Wang, Xiahua Chen, Rui Wang, Chenhui Chu

Visual Data in NLP

Challenge:	Existing models train a visual encoder with weak cross-modal supervision signals, resulting in a limited capacity to capture non-textual features and suboptimal performance.
Approach:	They propose a Visually-Asymmetric coNsistenCy Learning approach that enhances the model’s ability to capture fine-grained visual and layout features through the incorporation of color priors.
Outcome:	The proposed approach outperforms the strong LayoutLM series baseline on benchmark datasets and provides insights for optimizing model performance.

Enhancing Neural Topic Model with Multi-Level Supervisions from Seed Words (2023.findings-acl)

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Yang Lin, Xin Gao, Xu Chu, Yasha Wang, Junfeng Zhao, Chao Chen

TESTEVAL: Benchmarking Large Language Models for Test Case Generation (2025.findings-naacl)

Challenge:	Existing topic seed words are difficult to incorporate into topic models due to the semantic diversity of natural language.
Approach:	They propose a neural topic model enhanced with supervisions from seed words on word and document levels.
Outcome:	The proposed model outperforms the state-of-the-art seeded topic models in terms of topic quality and classification accuracy.

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Wenhan Wang, Chenyuan Yang, Zhijie Wang, Yuheng Huang, Zhaoyang Chu, Da Song, Lingming Zhang, An Ran Chen, Lei Ma

A Survey of Domain Adaptation for Neural Machine Translation (C18-1)

Challenge:	Existing methods to generate test cases using large language models are limited in their ability to generate unit test cases.
Approach:	They propose a test case generation benchmark that uses large language models to generate unit test cases.
Outcome:	The proposed test case generation benchmarks compare LLMs with commercial and open-source LLM platforms and find that they lack the ability to comprehend program logic and execution paths.

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Chenhui Chu, Rui Wang

Text Generation

Challenge:	Neural machine translation (NMT) is a deep learning based approach for machine translation.
Approach:	They propose to use a deep learning approach to train machine translation in scenarios where large-scale parallel corpora are available.
Outcome:	The proposed approach yields the state-of-the-art translation performance in resource rich scenarios.

Speech-Hands: A Self-Reflection Voice Agentic Approach to Speech Recognition and Audio Reasoning with Omni Perception (2026.acl-long)

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Zhen Wan, Chao-Han Huck Yang, Jinchuan Tian, Hanrong Ye, Ankita Pasad, Szu-Wei Fu, Arushi Goel, Ryo Hachiuma, Shizhe Diao, Kunal Dhawan, Sreyan Ghosh, Yusuke Hirota, Zhehuai Chen, Rafael Valle, Chenhui Chu, Shinji Watanabe, Boris Ginsburg, Yu-Chiang Frank Wang

Thinking with Map: Reinforced Parallel Map-Augmented Agent for Geolocalization (2026.findings-acl)

Challenge:	naively fine-tuning an omni-model on speech recognition and external sound understanding tasks often degrades performance . Xie and Wu's framework, Speech-Hands, recasts the problem as an explicit self-reflection decision.
Approach:	They propose a voice-agentic framework that learns one critical omni-understanding skill: trusting itself versus external audio perception.
Outcome:	The proposed framework outperforms baseline models on the OpenASR leaderboard by 12.1% WER and high F1 on audio QA decisions.

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Yuxiang Ji, Yong Wang, Ziyu Ma, Yiming Hu, Hailang Huang, Xuecai Hu, Guanhua Chen, Liaoni Wu, Xiangxiang Chu

Visual Data in NLP

Challenge:	Existing large vision-language model (LVLM) approaches overlook a common strategy used by humans — using maps.
Approach:	They propose a method to equip a vision-language model with the ability to think with maps and optimize it using agentic reinforcement learning and parallel test-time scaling.
Outcome:	The proposed method outperforms open- and closed-source models on most metrics.

Pruning via Merging: Compressing LLMs via Manifold Alignment Based Layer Merging (2024.emnlp-main)

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Deyuan Liu, Zhanyue Qin, Hairu Wang, Zhao Yang, Zecheng Wang, Fangying Rong, Qingbin Liu, Yanchao Hao, Bo Li, Xi Chen, Cunhang Fan, Zhao Lv, Dianhui Chu, Zhiying Tu, Dianbo Sui

UnrealLLM: Towards Highly Controllable and Interactable 3D Scene Generation by LLM-powered Procedural Content Generation (2025.findings-acl)

Challenge:	Existing methods for parameter pruning fail to utilize the knowledge from pruned parameters.
Approach:	They propose a method that uses manifold learning and the Information Bottleneck measure to merge similar layers to preserve model performance.
Outcome:	The proposed method outperforms pruning methods on multiple datasets and LLMs with quantization and achieves substantial compression ratios.

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SongTang SongTang, Kaiyong Zhao, Lei Wang, Yuliang Li, Xuebo Liu, Junyi Zou, Qiang Wang, Xiaowen Chu

Challenge:	UnrealLLM is a novel framework that connects natural language descriptions with the professional PCG system (Unreal Engine 5) to automate scene generation.
Approach:	They propose a novel multi-agent framework that connects natural language descriptions with the professional PCG system (Unreal Engine 5) to automate scene generation.
Outcome:	The proposed framework achieves competitive performance in technical metrics and aesthetic quality, offering unique advantages in generation scale and interactivity.

Parenting: Optimizing Knowledge Selection of Retrieval-Augmented Language Models with Parameter Decoupling and Tailored Tuning (2025.acl-long)

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Yongxin Xu, Ruizhe Zhang, Xinke Jiang, Yujie Feng, Yuzhen Xiao, Xinyu Ma, Runchuan Zhu, Xu Chu, Junfeng Zhao, Yasha Wang

Keywords and Instances: A Hierarchical Contrastive Learning Framework Unifying Hybrid Granularities for Text Generation (2022.acl-long)

Challenge:	Existing methods for integrating internal and external knowledge lack effective control mechanisms for generating hallucinations and dealing with outdated knowledge.
Approach:	They propose a framework that decouples, identifies, and purposefully optimizes parameter subspaces related to adherence and robustness.
Outcome:	The proposed framework decouples, identifies, and purposefully optimizes parameter subspaces related to adherence and robustness.

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Mingzhe Li, XieXiong Lin, Xiuying Chen, Jinxiong Chang, Qishen Zhang, Feng Wang, Taifeng Wang, Zhongyi Liu, Wei Chu, Dongyan Zhao, Rui Yan

HS-STaR: Hierarchical Sampling for Self-Taught Reasoners via Difficulty Estimation and Budget Reallocation (2025.emnlp-main)

Challenge:	Existing studies focus on contrastive learning on the instance level without discriminating the contribution of each word.
Approach:	They propose a hierarchical contrastive learning mechanism which can unify semantic meaning in the input text.
Outcome:	The proposed model outperforms baselines on storytelling, paraphrasing, dialogue generation, and storytelling tasks.

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Feng Xiong, Hongling Xu, Yifei Wang, Runxi Cheng, Yong Wang, Xiangxiang Chu

Generating Informative Conversational Response using Recurrent Knowledge-Interaction and Knowledge-Copy (2020.acl-main)

Challenge:	Recent studies have incorporated reward models to guide response selection or decoding, aiming to obtain higher-quality data.
Approach:	They propose a Hierarchical Sampling framework for self-taught reasoners that allocates a fixed sampling budget to problem boundary-level problems and then reallocates the remaining budget toward high-utility problems during a re-sampling phase.
Outcome:	The proposed framework outperforms baseline models without additional sampling budgets across multiple reasoning benchmarks and backbone LLMs.

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Xiexiong Lin, Weiyu Jian, Jianshan He, Taifeng Wang, Wei Chu

Dialogue Systems & Conversational Agents

Challenge:	Knowledge-driven conversation approaches have attracted considerable research attention in recent years.
Approach:	They propose a method that integrates recurrent knowledge interaction among response decoding steps to incorporate appropriate knowledge.
Outcome:	The proposed method improves on two datasets Wizard-of-Wikipedia and DuConv with different knowledge formats and different languages.

Safe-SAIL: Towards a Fine-grained Safety Landscape of Large Language Models via Sparse Autoencoder Interpretation Framework (2026.findings-acl)

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Jiaqi Weng, Han Zheng, Hanyu Zhang, Ej Zhou, Qinqin He, Jialing Tao, Hui Xue, Zhixuan Chu, Xiting Wang

Explainability & Interpretability in NLP

Challenge:	Existing studies on how SAEs derive most fine-grained latent features for safety remain unexplored.
Approach:	They propose a framework for interpreting SAE features in safety-critical domains . they train a suite of SAEs with human-readable explanations and systematic evaluations based on pornography, politics, violence, and terror .
Outcome:	The proposed framework reduces interpretation cost by 55% and improves safety-critical features.

Domaino1s: Guiding LLM Reasoning for Explainable Answers in High-Stakes Domains (2025.findings-acl)

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Xu Chu, Zhijie Tan, Hanlin Xue, Guanyu Wang, Tong Mo, Weiping Li

Question Directed Graph Attention Network for Numerical Reasoning over Text (2020.emnlp-main)

Challenge:	Existing Large Language Models (LLMs) generate brief answers without reasoning processes and explanations.
Approach:	They propose supervised fine-tuning and tree search to enhance LLMs’ reasoning capabilities on domain tasks.
Outcome:	The proposed model improves on stock investment recommendation and legal reasoning QA tasks.

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Kunlong Chen, Weidi Xu, Xingyi Cheng, Zou Xiaochuan, Yuyu Zhang, Le Song, Taifeng Wang, Yuan Qi, Wei Chu

CoEvolve: Training LLM Agents via Agent-Data Mutual Evolution (2026.acl-long)

Challenge:	Numerical reasoning requires both natural language understanding and arithmetic computation.
Approach:	They propose a graph representation for the context of the passage and question needed for numerical reasoning.
Outcome:	The proposed model achieves remarkable results in benchmark datasets such as DROP.

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Shidong Yang, Ziyu Ma, Tongwen Huang, Yiming Hu, Yong Wang, Xiangxiang Chu

Challenge:	Extensive experiments on AppWorld and BFCL demonstrate consistent and significant improvements over strong base models, yielding absolute gains of 19.43%, 15.58%, and 18.14%, respectively.
Approach:	They propose a framework that extracts feedback signals such as forgetting and uncertainty from rollout trajectories and utilizes them to guide LLM-based task synthesis.
Outcome:	Extensive experiments on AppWorld and BFCL show that the proposed framework improves over strong base models.

Probing the Plasticity and Correlation of LLM Value Systems: LLM Value Rankings are Not Stable (2026.findings-acl)

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Zhenheng Tang, Qihua Pan, Jingya Shen, Xiang Liu, Qian Wang, Bo Li, Xiaowen Chu

To Code or not to Code? Adaptive Tool Integration for Math Language Models via Expectation-Maximization (2025.findings-acl)

Challenge:	Large Language Models (LLMs) have similar value rankings but little is known about how susceptible they are to external influence and how different values are correlated with each other.
Approach:	They propose to use 6 different value transformation prompting methods to examine the plasticity of LLM value systems by comparing them with 8 LLMs.
Outcome:	The proposed methods are effective on 8 LLMs and 3 families.

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Haozhe Wang, Long Li, Chao Qu, Weidi Xu, Fengming Zhu, Wei Chu, Fangzhen Lin

Towards Human-Like Machine Comprehension: Few-Shot Relational Learning in Visually-Rich Documents (2024.lrec-main)

Challenge:	Existing tools that integrate chain-of-thought reasoning and code execution lack metacognitive awareness to integrate tools.
Approach:	They propose a framework that synergizes structured exploration with off-policy RL optimization to create a cycle between metacognitive tool-use decisions and evolving capabilities.
Outcome:	The proposed framework improves over 11% on MATH500 and 9.4% on AIME without o1-like CoT.

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Hao Wang, Tang Li, Chenhui Chu, Rui Wang, Pinpin Zhu

Self-Critique Guided Iterative Reasoning for Multi-hop Question Answering (2025.findings-acl)

Challenge:	Existing document AI approaches fail to consider key-value relations in visually-rich documents . a few-shot approach is proposed to extract key- value relation triplets in VRDs .
Approach:	They propose a few-shot relational learning approach targeting the extraction of key-value relation triplets in Visually-Rich Documents.
Outcome:	The proposed method outperforms existing methods in visually-rich documents.

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Zheng Chu, Huiming Fan, Jingchang Chen, Qianyu Wang, Mingda Yang, Jiafeng Liang, Zhongjie Wang, Hao Li, Guo Tang, Ming Liu, Bing Qin

Natural Language Interfaces

Challenge:	Large language models (LLMs) have demonstrated remarkable reasoning capabilities, but they still face challenges in knowledge-intensive multi-hop reasoning.
Approach:	They propose a method that uses self-critique feedback to guide iterative reasoning by enabling iteration and self-evaluation of its intermediate reasoning steps.
Outcome:	The proposed method surpasses the previous SOTA by 8.6% on three multi-hop reasoning datasets.

LPZero: Language Model Zero-cost Proxy Search from Zero (2024.findings-emnlp)

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Peijie Dong, Lujun Li, Xiang Liu, Zhenheng Tang, Xuebo Liu, Qiang Wang, Xiaowen Chu

Extracting Trigger-sharing Events via an Event Matrix (2022.findings-emnlp)

Challenge:	Existing zero-cost (ZC) proxies rely on expert knowledge and incur significant trial-and-error costs.
Approach:	They propose a framework that automatically designs zero-cost (ZC) proxies for various tasks and incorporates genetic programming to find the optimal symbolic composition.
Outcome:	The proposed framework achieves higher ranking consistency than human-designed proxies on NLP tasks.

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Jun Xu, Weidi Xu, Mengshu Sun, Taifeng Wang, Wei Chu

PairRE: Knowledge Graph Embeddings via Paired Relation Vectors (2021.acl-long)

Challenge:	Existing methods to extract multiple events with triggers and arguments are invalid as there may be multiple events.
Approach:	They propose a framework for event extraction which models the relations between arguments by an event matrix.
Outcome:	The proposed framework beats all the advanced competitors on 3 widely-used datasets.

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Linlin Chao, Jianshan He, Taifeng Wang, Wei Chu

Breaking Block Boundaries: Anchor-based History-stable Decoding for Diffusion Large Language Models (2026.acl-long)

Challenge:	Existing knowledge graph embedding methods fail to solve two major problems at the same time, leading to unsatisfactory results.
Approach:	They propose a model with paired vectors for each relation representation that can be adaptively adjusted to fit for different complex relations.
Outcome:	Experiments on two knowledge graph datasets show the proposed model can handle complex relations and encode relation patterns.

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Shun Zou, Yong Wang, Zehui Chen, Lin Chen, Chongyang Tao, Feng Zhao, Xiangxiang Chu

Combating Label Sparsity in Short Text Topic Modeling via Nearest Neighbor Augmentation (2024.findings-acl)

Challenge:	Semi-autoregressive (Semi-AR) decoding suffers from inherent block constraints . naive lookahead decoding is unreliable, token stability closely correlates with convergence trend, and historical information is isolated.
Approach:	They propose a training-free, plug-and-play dynamic decoding strategy that monitors the stability of tokens in real time through dynamic anchors.
Outcome:	The proposed approach reduces decoding steps by 80% while improving performance by 3.67% on the BBH benchmark.

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Yang Lin, Xinyu Ma, Xin Gao, Ruiqing Li, Yasha Wang, Xu Chu

DocTrack: A Visually-Rich Document Dataset Really Aligned with Human Eye Movement for Machine Reading (2023.findings-emnlp)

Challenge:	Existing topic models suffer from poor performance when applied to short text contents due to the limited length of a single topic.
Approach:	They propose a neural short text topic model that augments reconstruction labels with k-nearest documents to complement relevant but unobserved words.
Outcome:	The proposed model outperforms the state-of-the-art models on multiple public short-text datasets and can derive high-quality topics and document representations.

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Hao Wang, Qingxuan Wang, Yue Li, Changqing Wang, Chenhui Chu, Rui Wang

OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models (2025.acl-long)

Challenge:	Document AI models that can read visually rich documents have a long way to go before they can read them as accurately, continuously, and flexibly as humans do.
Approach:	They propose a visually-rich document dataset that aligns with human eye-movement information using eye-tracking technology.
Outcome:	The proposed dataset can help in designing better document AI models and human reading robots in the future.

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Siming Huang, Tianhao Cheng, Jason Klein Liu, Weidi Xu, Jiaran Hao, Liuyihan Song, Yang Xu, Jian Yang, Jiaheng Liu, Chenchen Zhang, Linzheng Chai, Ruifeng Yuan, Xianzhen Luo, Qiufeng Wang, YuanTao Fan, Qingfu Zhu, Zhaoxiang Zhang, Yang Gao, Jie Fu, Qian Liu, Houyi Li, Ge Zhang, Yuan Qi, Xu Yinghui, Wei Chu, Zili Wang

UNO Arena for Evaluating Sequential Decision-Making Capability of Large Language Models (2024.emnlp-main)

Challenge:	Code LLMs lack reproducible data pipelines and training protocols for reproducible advancements in code intelligence.
Approach:	They propose a top-tier code LLM that releases model weights and inference code . reproducible data pipelines, rigorous experimental ablation results and training protocols are included .
Outcome:	The proposed model achieves comparable performance to leading models and serves as an "open cookbook" reproducible training data, rigorous experimental ablation results, and detailed training protocols are also included in the model.

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Zhanyue Qin, Haochuan Wang, Deyuan Liu, Ziyang Song, Cunhang Fan, Zhao Lv, Jinlin Wu, Zhen Lei, Zhiying Tu, Dianhui Chu, Xiaoyan Yu, Dianbo Sui

TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in Large Language Models (2024.acl-long)

Challenge:	Existing LLMs demonstrate powerful capabilities between tasks, but can they make sequential decisions?
Approach:	They propose to evaluate sequential decision-making capability of large language models (LLMs) using novel metrics based Monte Carlo methods.
Outcome:	The proposed benchmark improves sequential decision-making performance compared to the vanilla LLM player.

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Zheng Chu, Jingchang Chen, Qianglong Chen, Weijiang Yu, Haotian Wang, Ming Liu, Bing Qin

Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future (2024.acl-long)

Challenge:	Grasping the concept of time is a fundamental facet of human cognition.
Approach:	They propose a hierarchical temporal reasoning benchmark that covers a broad spectrum of temporal phenomena.
Outcome:	The proposed benchmark shows that state-of-the-art LLMs are still far behind humans in temporal reasoning .

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Zheng Chu, Jingchang Chen, Qianglong Chen, Weijiang Yu, Tao He, Haotian Wang, Weihua Peng, Ming Liu, Bing Qin, Ting Liu

SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check (2020.acl-main)

Challenge:	Recent studies have revealed that chain-of-thought prompting significantly enhances LLM’s reasoning capabilities, which attracts widespread attention from both academics and industry.
Approach:	They propose to summarize advanced methods through a taxonomy that offers novel perspectives.
Outcome:	The proposed method delineates the challenges and future directions, thereby shedding light on future research.

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Xingyi Cheng, Weidi Xu, Kunlong Chen, Shaohua Jiang, Feng Wang, Taifeng Wang, Wei Chu, Yuan Qi

DFAMS: Dynamic-flow guided Federated Alignment based Multi-prototype Search (2026.acl-long)

Challenge:	Existing methods to detect and correct spelling errors in Chinese take external input or just heuristic rules.
Approach:	They propose to incorporate phonological and visual similarity knowledge into Chinese language models by using a specialized graph convolutional network.
Outcome:	The proposed method outperforms existing models on three human-annotated datasets.

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Zhibang Yang, Xinke Jiang, Rihong Qiu, Ruiqing Li, Yihang Zhang, Yue Fang, Yongxin Xu, Hongxin Ding, Xu Chu, Junfeng Zhao, Yasha Wang

Information Retrieval

Challenge:	Existing methods for ambiguous queries struggle to retrieve high-quality documents . DFAMS outperforms advanced FR methods by 14.37% in knowledge classification accuracy .
Approach:	They propose a framework that leverages dynamic information flow to identify latent query intents and construct semantically aligned knowledge partitions for accurate retrieval across heterogeneous sources.
Outcome:	The proposed framework outperforms existing methods in classification accuracy and retrieval recall tests.

Towards Faithful Multi-step Reasoning through Fine-Grained Causal-aware Attribution Reasoning Distillation (2025.coling-main)

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Zheng Chu, Jingchang Chen, Zhongjie Wang, Guo Tang, Qianglong Chen, Ming Liu, Bing Qin

InSerter: Speech Instruction Following with Unsupervised Interleaved Pre-training (2025.acl-long)

Challenge:	Recent advances have witnessed large language models (LLMs) achieving significant milestones across various domains of natural language processing.
Approach:	They introduce fine-grained attribution reasoning distillation (FARD) which incorporates grounded citations to consolidate the relationships between reasoning steps.
Outcome:	The proposed method outperforms CoT distillation methods on mathematical and general reasoning benchmarks.

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Dingdong Wang, Jin Xu, Ruihang Chu, Zhifang Guo, Xiong Wang, Jincenzi Wu, Dongchao Yang, Shengpeng Ji, Junyang Lin

GeoEdit: Geometric Knowledge Editing for Large Language Models (2025.emnlp-main)

Challenge:	Recent advances in speech large language models exhibit suboptimal performance in adhering to speech instructions.
Approach:	They propose a method to pre-train large-scale unsupervised speech-text sequences . they use text-to-speech conversion to generate textual continuations corresponding to provided speech segments .
Outcome:	The proposed model achieves superior or competitive results across diverse speech processing tasks.

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Yujie Feng, Li-Ming Zhan, Zexin Lu, Yongxin Xu, Xu Chu, Yasha Wang, Jiannong Cao, Philip S. Yu, Xiao-Ming Wu

Wait, We Don’t Need to “Wait”! Removing Thinking Tokens Improves Reasoning Efficiency (2025.findings-emnlp)

Challenge:	Existing training-based model editing methods struggle to incorporate new knowledge while preserving unrelated general knowledge.
Approach:	They propose a framework that uses geometric relationships to differentiate between neurons associated with new knowledge updates and those related to general knowledge perturbations.
Outcome:	The proposed framework avoids updating neurons with directions approximately orthogonal to existing knowledge, thus preserving the model’s generalization ability.

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Chenlong Wang, Yuanning Feng, Dongping Chen, Zhaoyang Chu, Ranjay Krishna, Tianyi Zhou

Guess What I am Thinking: A Benchmark for Inner Thought Reasoning of Role-Playing Language Agents (2025.findings-emnlp)

Challenge:	Recent advances in large reasoning models often introduce significant overthinking . this leads to verbose and redundant outputs that hinder efficiency.
Approach:	They propose a plug-and-play solution that disables explicit self-reflection . it suppresses tokens such as "Wait" and "Hmm" during inference .
Outcome:	The proposed approach reduces chain-of-thought trajectory length by up to 27%–51% in five R1-style model series without compromising model utility.

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Rui Xu, Mingyu Wang, Xintao Wang, Dakuan Lu, Xiaoyu Tan, Wei Chu, Xu Yinghui

AIMMerging: Adaptive Iterative Model Merging Using Training Trajectories for Language Model Continual Learning (2025.emnlp-main)

Challenge:	Recent advances in Large Language Models (LLMs)-based Role-Playing Language Agents (RPLAs) have attracted broad attention in various applications.
Approach:	They propose a benchmark for evaluating character thought generation using literature . they propose 'MIRROR' which generates character thoughts by retrieving memories, predicting character reactions, and synthesizing motivations.
Outcome:	The proposed benchmark outperforms existing methods in evaluating character thought generation.

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Yujie Feng, Jian Li, Xiaoyu Dong, Pengfei Xu, Xiaohui Zhou, Yujia Zhang, Zexin Lu, Yasha Wang, Alan Zhao, Xu Chu, Xiao-Ming Wu

BeamAggR: Beam Aggregation Reasoning over Multi-source Knowledge for Multi-hop Question Answering (2024.acl-long)

Challenge:	Recent model merging-based methods struggle to effectively manage the trade-off between learning new knowledge and preventing catastrophic forgetting.
Approach:	They propose a model merging framework that utilizes learning and forgetting signals from the training trajectory to dynamically monitor the model’s training status.
Outcome:	The proposed framework achieves significant performance improvements over existing state-of-the-art methods on three CL benchmarks with various model sizes (from 770M to 13B).

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Zheng Chu, Jingchang Chen, Qianglong Chen, Haotian Wang, Kun Zhu, Xiyuan Du, Weijiang Yu, Ming Liu, Bing Qin

Natural Language Interfaces

Challenge:	Large language models (LLMs) have demonstrated strong reasoning capabilities, but they still suffer from factual errors when tackling knowledge-intensive tasks.
Approach:	They propose a reasoning framework for knowledge-intensive multi-hop QA that prioritizes promising answers at each hop of question.
Outcome:	The proposed framework outperforms SOTA methods on four open-domain multi-hop reasoning datasets by 8.5%.

Investigating and Enhancing the Robustness of Large Multimodal Models Against Temporal Inconsistency (2025.acl-long)

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Jiafeng Liang, Shixin Jiang, Xuan Dong, Ning Wang, Zheng Chu, Hui Su, Jinlan Fu, Ming Liu, See-Kiong Ng, Bing Qin

Robustness in NLP

Challenge:	Large Multimodal Models (LMMs) have demonstrated impressive performance on general video comprehension benchmarks, but their robustness needs to be thoroughly investigated for broader applications.
Approach:	They propose a temporal robustness benchmark which introduces temporal inconsistency perturbations separately at the visual and textual modalities to assess the robustness of models.
Outcome:	The proposed method improves the model’s robustness and reliability in temporal analysis.

ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extraction (2021.acl-short)

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Chen-Yu Lee, Chun-Liang Li, Chu Wang, Renshen Wang, Yasuhisa Fujii, Siyang Qin, Ashok Popat, Tomas Pfister

Why Steering Works: Toward a Unified View of Language Model Parameter Dynamics (2026.acl-long)

Challenge:	Graph Convolutional Networks (GCNs) have limited ability to capture reading orders of given word-level node representations in a graph.
Approach:	They propose a new positional encoding technique to capture word-level nodes in a graph.
Outcome:	The proposed method improves existing GCNs with an 8.4% F1 score on two datasets and a large-scale payment dataset.

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Ziwen Xu, Chenyan WU, Hengyu Sun, Haiwen Hong, Mengru Wang, Yunzhi Yao, Longtao Huang, Hui Xue, Shumin Deng, Zhixuan Chu, Huajun Chen, Ningyu Zhang

MTGER: Multi-view Temporal Graph Enhanced Temporal Reasoning over Time-Involved Document (2023.findings-emnlp)

Challenge:	Methods for controlling large language models (LLMs) are often studied in isolation, obscuring connections and making comparison difficult.
Approach:	They propose a preference-utility analysis that separates control effects into preference and utility, and measures both on a shared log-odds scale using polarity-paired contrastive examples.
Outcome:	The proposed approach improves preference while preserving utility.

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Zheng Chu, Zekun Wang, Jiafeng Liang, Ming Liu, Bing Qin

LLaTiSA: Towards Difficulty-Stratified Time Series Reasoning from Visual Perception to Semantics (2026.findings-acl)

Challenge:	Existing work models time implicitly, making it difficult to handle complex relationships . a novel temporal reasoning framework explicitly models the temporal relationships among facts by multi-view temporal graphs .
Approach:	They propose a multi-view temporal graph-based temporal reasoning framework that explicitly models the temporal relationships among facts by multi-visit temporal charts.
Outcome:	The proposed framework gives more consistent answers under question perturbations.

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Yueyang Ding, HaoPeng Zhang, Rui Dai, Yi Wang, Tianyu Zong, Kaikui Liu, Xiangxiang Chu