Papers by Yan Han

ChuLo: Chunk-Level Key Information Representation for Long Document Understanding (2025.findings-acl)

Challenge:	Existing tabular data synthesis methods fail to account for cross-modal heterogeneity of real-world tables, where structured continuous and discrete attributes coexist with unstructured long-text columns.
Approach:	They propose a framework that synergistically trains an LLM-based text generator and a deep-learning-based non-textual generator to quantify cross-modal semantic alignment.
Outcome:	The proposed framework outperforms state-of-the-art frameworks in fidelity, diversity, and task utility.

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Yan Li, Caren Han, Yue Dai, Feiqi Cao

RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework (2025.acl-long)

Challenge:	Traditional approaches to truncate inputs, sparse self-attention, and chunking often lead to information loss and hinder the model’s ability to capture long-range dependencies.
Approach:	They propose a novel chunk representation method that uses unsupervised keyphrase extraction to group input tokens to retain core document content while reducing input length.
Outcome:	The proposed method minimizes information loss and improves the efficiency of Transformer-based models.

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Kunlun Zhu, Yifan Luo, Dingling Xu, Yukun Yan, Zhenghao Liu, Shi Yu, Ruobing Wang, Shuo Wang, Yishan Li, Nan Zhang, Xu Han, Zhiyuan Liu, Maosong Sun

Text Generation

Challenge:	Existing evaluation metrics for RAG systems are lacking due to high costs of data construction and lack of factual accuracy.
Approach:	They propose a framework to evaluate RAG systems in specialized scenarios . they propose three new metrics to evaluate LLM-generated responses .
Outcome:	The proposed framework outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples.

Rethinking Smoothness for Fast and Adaptable Entity Alignment Decoding (2025.findings-naacl)

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Yuanyi Wang, Han Li, Haifeng Sun, Lei Zhang, Bo He, Wei Tang, Tianhao Yan, Qi Qi, Jingyu Wang

hyperdoc2vec: Distributed Representations of Hypertext Documents (P18-1)

Challenge:	Existing methods for integrating knowledge graphs rely on entity and relation embeddings . Fig. 1 shows how to decode knowledge graph in under 6 seconds .
Approach:	They propose a framework that only utilizes entity embeddings to decode knowledge graphs.
Outcome:	The proposed framework reconstructs KG representation by maximizing smoothness of entity embeddings.

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Jialong Han, Yan Song, Wayne Xin Zhao, Shuming Shi, Haisong Zhang

Representation Learning

Challenge:	Conventional text embedding methods suffer from information loss if directly adapted to hyper-documents.
Approach:	They propose an embedding approach for hyper-documents that incorporates four criteria to preserve necessary information for embeddable models.
Outcome:	The proposed model outperforms several existing models on two tasks in the academic domain.

DeepPresenter: Environment-Grounded Reflection for Agentic Presentation Generation (2026.findings-acl)

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Hao Zheng, Guozhao Mo, Xinru Yan, Qianhao Yuan, Wenkai Zhang, Xuanang Chen, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun

Challenge:	Existing presentation agents rely on predefined workflows and fixed templates to generate presentations.
Approach:	They propose an agentic framework that adapts to diverse user intents and iterative refinement based on observation.
Outcome:	The proposed framework can be used to generate presentations with environmental observations.

SAGE: Steerable Agentic Data Generation for Deep Search with Execution Feedback (2026.findings-eacl)

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Fangyuan Xu, Rujun Han, Yanfei Chen, Zifeng Wang, I-Hung Hsu, Jun Yan, Vishy Tirumalashetty, Eunsol Choi, Tomas Pfister, Chen-Yu Lee

Reasoning

Challenge:	High-quality, complex question-answer pairs are pivotal for training and evaluating capable deep search agents.
Approach:	They propose a pipeline that generates high-quality, difficulty-controlled deep search question-answer pairs for a given corpus and a target difficulty level.
Outcome:	The proposed pipeline generates high-quality, difficulty-controlled deep search question-answer pairs for a given corpus and a target difficulty level.

Syntax-driven Approach for Semantic Role Labeling (2022.lrec-1)

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Yuanhe Tian, Han Qin, Fei Xia, Yan Song

Text Style Transfer with Contrastive Transfer Pattern Mining (2023.acl-long)

Challenge:	Existing studies focus on auto-generated syntactic knowledge to enhance semantic role labeling . experimental results show that map memories can enhance SRL .
Approach:	They propose to map memories to enhance semantic role labeling by encoding auto-generated syntactic knowledge from off-the-shelf toolkits.
Outcome:	The proposed model outperforms baselines and achieves state-of-the-art results on two English benchmark datasets.

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Jingxuan Han, Quan Wang, Licheng Zhang, Weidong Chen, Yan Song, Zhendong Mao

Text Generation

Challenge:	Existing methods for text style transfer only focus on the transformation between styles, yet they do not take into account that this transformation can be achieved via different hidden transfer patterns.
Approach:	They propose a novel approach which automatically mines hidden transfer patterns to improve TST . they use a clustering module to automatically discover hidden transfer pattern from the data .
Outcome:	The proposed method can be applied in a plug-and-play manner to enhance other methods to further improve their performance.

A Systematic Survey of Automatic Prompt Optimization Techniques (2025.emnlp-main)

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Kiran Ramnath, Kang Zhou, Sheng Guan, Soumya Smruti Mishra, Xuan Qi, Zhengyuan Shen, Shuai Wang, Sangmin Woo, Sullam Jeoung, Yawei Wang, Haozhu Wang, Han Ding, Yuzhe Lu, Zhichao Xu, Yun Zhou, Balasubramaniam Srinivasan, Qiaojing Yan, Yueyan Chen, Haibo Ding, Panpan Xu, Lin Lee Cheong

PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning (2026.acl-long)

Challenge:	Recent advances in prompt engineering have created impediments for end users to adopt . however, prompt engineering remains an impedance due to rapid advances in models, tasks, and associated best practices.
Approach:	They propose to define APO as a 5-part unifying framework and categorize all relevant works based on their salient features.
Outcome:	The proposed framework aims to improve the performance of large language models on various tasks.

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Jingcheng Hu, Yinmin Zhang, Shijie Shang, Xiaobo Yang, Yue Peng, Zhewei Huang, Hebin Zhou, Xin Wu, Jie Cheng, Fanqi Wan, Xiangwen Kong, Chengyuan Yao, Kaiwen Yan, Ailin Huang, Hongyu Zhou, Qi Han, Zheng Ge, Xiangyu Zhang, Heung-Yeung Shum

Reasoning

Challenge:	Parallel Coordinated Reasoning (PaCoRe) overcomes a central limitation of contemporary language models: their inability to scale test-time compute (TTC) far beyond sequential reasoning under a fixed context window.
Approach:	They propose a training-and-inference framework to overcome a central limitation of language models: their inability to scale test-time compute (TTC) under a fixed context window.
Outcome:	The proposed model scales to multi-million-token effective TTC without exceeding context limits.

“I’ve Decided to Leak”: Probing Internals Behind Prompt Leakage Intents (2025.emnlp-main)

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Jianshuo Dong, Yutong Zhang, Liu Yan, Zhenyu Zhong, Tao Wei, Ke Xu, Minlie Huang, Chao Zhang, Han Qiu

Responsible & Trustworthy NLP

Challenge:	Large language models (LLMs) exhibit prompt leakage vulnerabilities, raising intellectual property and confidentiality concerns.
Approach:	They use probing techniques to capture LLMs’ intent-related internal representations and show that they internalize prompt leakage intents in their hidden states before generating tokens.
Outcome:	The proposed probes achieve 90%+ AUROC across all tested models, even when applied to new system prompts and attacks.

KBAlign: Efficient Self Adaptation on Specific Textual Knowledge Bases (2025.findings-emnlp)

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Zheni Zeng, Yuxuan Chen, Shi Yu, Ruobing Wang, Yukun Yan, Zhenghao Liu, Shuo Wang, Xu Han, Zhiyuan Liu, Maosong Sun

PyramidInfer: Pyramid KV Cache Compression for High-throughput LLM Inference (2024.findings-acl)

Challenge:	Existing methods for retrieval-augmented generation (RAG) are limited and fine-tuning incurs prohibitive costs of external signals.
Approach:	They propose a self-supervised framework that enhances RAG systems through efficient model adaptation.
Outcome:	The proposed framework achieves 90% of the performance gain obtained through GPT-4-supervised adaptation while relying entirely on self-annotation of much smaller models.

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Dongjie Yang, Xiaodong Han, Yan Gao, Yao Hu, Shilin Zhang, Hai Zhao

Challenge:	Existing methods to reduce memory usage for large language models neglect inter-layer dependency between layers and huge memory consumption in pre-computation.
Approach:	They propose a method that compresses the KV cache by layer-wise retaining crucial context.
Outcome:	The proposed method reduces memory usage by layer-wise retaining crucial context . it can improve 2.2x throughput compared to Accelerate with over 54% memory reduction .

Improving Arabic Diacritization with Regularized Decoding and Adversarial Training (2021.acl-short)

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Han Qin, Guimin Chen, Yuanhe Tian, Yan Song

In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents (2025.acl-long)

Challenge:	Existing studies regard auto-generated knowledge instances as gold references, which limits their effectiveness since they are not always accurate and inferior instances can lead to incorrect predictions.
Approach:	They propose to use regularized decoding and adversarial training to appropriately learn from noisy knowledge instances for Arabic diacritization.
Outcome:	The proposed model outperforms existing models on two benchmark datasets even with flawed auto-generated knowledge.

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Zhen Tan, Jun Yan, I-Hung Hsu, Rujun Han, Zifeng Wang, Long Le, Yiwen Song, Yanfei Chen, Hamid Palangi, George Lee, Anand Rajan Iyer, Tianlong Chen, Huan Liu, Chen-Yu Lee, Tomas Pfister

Natural Language Interfaces

Challenge:	Existing approaches to long-term dialogue memory management fail to capture the natural semantic structure of conversations, leading to fragmented and incomplete representations.
Approach:	They propose a mechanism that integrates forward- and backward-looking reflections into a personalized memory bank for effective future retrieval.
Outcome:	The proposed mechanism outperforms state-of-the-art benchmarks on a long-term dialogue memory model.

Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases (2021.acl-long)

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Boxi Cao, Hongyu Lin, Xianpei Han, Le Sun, Lingyong Yan, Meng Liao, Tong Xue, Jin Xu

TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities (2023.acl-demo)

Challenge:	Recent studies show that pre-trained masked language models can be factual knowledge bases.
Approach:	They conduct a rigorous study to explore the underlying predicting mechanisms of MLMs . they find that previous decent performance mainly owes to the biased prompts which overfit dataset artifacts a .
Outcome:	The proposed model improves on illustrative cases and external contexts . the results question the previous findings that MLMs can be reliable factual knowledge bases .

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Zhe Zhao, Yudong Li, Cheng Hou, Jing Zhao, Rong Tian, Weijie Liu, Yiren Chen, Ningyuan Sun, Haoyan Liu, Weiquan Mao, Han Guo, Weigang Gou, Taiqiang Wu, Tao Zhu, Wenhang Shi, Chen Chen, Shan Huang, Sihong Chen, Liqun Liu, Feifei Li, Xiaoshuai Chen, Xingwu Sun, Zhanhui Kang, Xiaoyong Du, Linlin Shen, Kimmo Yan

Stepwise Perplexity-Guided Refinement for Efficient Chain-of-Thought Reasoning in Large Language Models (2025.findings-acl)

Challenge:	Several pre-training models of different modalities are showing a rising trend of homogeneity in their model structures.
Approach:	They propose a toolkit that supports pre-training models of different modalities.
Outcome:	The proposed toolkit can match the performance of the original implementations on text, vision, and audio benchmarks.

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Yingqian Cui, Pengfei He, Jingying Zeng, Hui Liu, Xianfeng Tang, Zhenwei Dai, Yan Han, Chen Luo, Jing Huang, Zhen Li, Suhang Wang, Yue Xing, Jiliang Tang, Qi He

TS-SQL: Test-driven Self-refinement for Text-to-SQL (2025.findings-emnlp)

Challenge:	Chain-of-Thought (CoT) reasoning has improved the performance of large language models (LLMs) however, the detailed reasoning process in CoT often incurs long generation times and high computational costs due to the inclusion of unnecessary steps.
Approach:	They propose a method to identify critical reasoning steps using perplexity as a measure of their importance.
Outcome:	The proposed method achieves a better balance between reasoning accuracy and efficiency of CoT.

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Wenbo Xu, Haifeng Zhu, Liang Yan, Chuanyi Liu, Peiyi Han, Shaoming Duan, Jeff Z. Pan

Programming Languages in NLP

Challenge:	null
Approach:	null
Outcome:	null

Reinforced Cross-modal Alignment for Radiology Report Generation (2022.findings-acl)

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Han Qin, Yan Song

Unknown Intent Detection Using Gaussian Mixture Model with an Application to Zero-shot Intent Classification (2020.acl-main)

Challenge:	Medical images are widely used in clinical decision-making, where writing radiology reports can be enhanced by automatic solutions to alleviate physicians’ workload.
Approach:	They propose an approach with reinforcement learning over a cross-modal memory to better align visual and textual features for radiology report generation.
Outcome:	The proposed approach improves cross-modal alignment on two English radiology report datasets and human evaluation confirms the results.

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Guangfeng Yan, Lu Fan, Qimai Li, Han Liu, Xiaotong Zhang, Xiao-Ming Wu, Albert Y.S. Lam

Test-Time Code-Switching for Cross-lingual Aspect Sentiment Triplet Extraction (2025.naacl-long)

Challenge:	Existing methods for unknown intent detection are limited by prior knowledge of class labels.
Approach:	They propose to use a Gaussian mixture model to model utterance embeddings with a distribution and inject dynamic class semantic information into Gausssian means.
Outcome:	The proposed model performs well on three real task-oriented dialogue datasets in two languages.

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Dongming Sheng, Kexin Han, Hao Li, Yan Zhang, Yucheng Huang, Jun Lang, Wenqiang Liu

Multilinguality

Challenge:	Aspect Sentiment Triplet Extraction (ASTE) is a thriving research area . current code-switching methods suffer from term boundary detection issues and out-of-dictionary problems.
Approach:	They propose a test-time code-switching framework which bridges the gap between bilingual training and monolingual test- time prediction.
Outcome:	The proposed framework achieves an average improvement of 3.7% on four cross-lingual datasets.

Covariance Matrix-Driven Image Channel Allocation for Multimodal Fake News Detection (2026.findings-acl)

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Zongliang Han, Wenyu Guo, Guoqing Jin, Yang Liu, Yan Song, Dong Yu, Wang Min

A Dual-View Approach to Classifying Radiology Reports by Co-Training (2024.lrec-main)

Challenge:	Existing methods focus on designing efficient multimodal fusion frameworks to bridge the semantic gap between images and texts.
Approach:	They propose a covariance matrix-driven image channel allocation method that expands the number of original channel maps and assigns importance scores to the expanded channel maps.
Outcome:	The proposed method achieves state-of-the-art on three public multimodal fake news detection benchmark datasets.

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Yutong Han, Yan Yuan, Lili Mou

EssayJudge: A Multi-Granular Benchmark for Assessing Automated Essay Scoring Capabilities of Multimodal Large Language Models (2025.findings-acl)

Challenge:	Using the structure of a radiology report, we propose a co-training approach to train two machine learning models using the dual views of MRI and CT data.
Approach:	They propose a co-training approach where two machine learning models are built upon the Findings and Impression sections and use each other's information to boost performance with massive unlabeled data in a semi-supervised manner.
Outcome:	The proposed model outperforms supervised and semi-supervised methods in a public health surveillance study and outperformed existing methods.

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Jiamin Su, Yibo Yan, Fangteng Fu, Zhang Han, Jingheng Ye, Xiang Liu, Jiahao Huo, Huiyu Zhou, Xuming Hu

Relation Extraction with Word Graphs from N-grams (2021.emnlp-main)

Challenge:	Automated Essay Scoring (AES) systems face three major challenges: reliance on handcrafted features that limit generalizability, difficulty in capturing fine-grained traits like coherence and argumentation, and inability to handle multimodal contexts.
Approach:	They propose a multimodal benchmark to evaluate AES capabilities across lexical-, sentence-, and discourse-level traits without manual feature engineering.
Outcome:	The proposed system can evaluate AES capabilities across lexical-, sentence-, and discourse-level traits without manual feature engineering.

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Han Qin, Yuanhe Tian, Yan Song

Speculating LLMs’ Chinese Training Data Pollution from Their Tokens (2025.emnlp-main)

Challenge:	Recent studies for relation extraction (RE) leverage the dependency tree of the input sentence to improve performance.
Approach:	They propose to use a graph convolutional network to build a context graph without dependency parsers.
Outcome:	The proposed approach improves neural RE methods without dependency parsers on English benchmark datasets.

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Qingjie Zhang, Di Wang, Haoting Qian, Liu Yan, Tianwei Zhang, Ke Xu, Qi Li, Minlie Huang, Hewu Li, Han Qiu

DeepNote: Note-Centric Deep Retrieval-Augmented Generation (2025.findings-emnlp)

Challenge:	Experiments on GPT and other 23 LLMs indicate that tokens widely exist while GPT’s vocabulary behaves the worst: more than 23% long Chinese tokens (i.e., a token with more than two Chinese characters) are either porn or online gambling.
Approach:	They propose to locate Polluted Chinese (PoC) tokens in LLMs and build a PoC token detector to label them in vocabularies by considering each token’s semantics and related contents from the search engines.
Outcome:	The proposed method predicts that the ratio of “*” related webpages in GPT-4o's training data is around 0.5%.

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Ruobing Wang, Qingfei Zhao, Yukun Yan, Daren Zha, Yuxuan Chen, Shi Yu, Zhenghao Liu, Yixuan Wang, Shuo Wang, Xu Han, Zhiyuan Liu, Maosong Sun

Information Retrieval

Challenge:	. - (EN)
Approach:	. - (EN)
Outcome:	. - (EN)

MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization (2024.findings-acl)

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Zhiyu Yang, Zihan Zhou, Shuo Wang, Xin Cong, Xu Han, Yukun Yan, Zhenghao Liu, Zhixing Tan, Pengyuan Liu, Dong Yu, Zhiyuan Liu, Xiaodong Shi, Maosong Sun

Enhancing Relation Extraction via Adversarial Multi-task Learning (2022.lrec-1)

Challenge:	Scientific data visualization is an essential process in research, but its use of large language models remains unexplored.
Approach:	They propose a model-agnostic LLM agent framework to automate scientific data visualization tasks.
Outcome:	The proposed framework improves performance of commercial and open-source models.

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Han Qin, Yuanhe Tian, Yan Song

Responsible & Trustworthy NLP

Challenge:	Existing studies have focused on re-modeling the given NEs and thus lead to inferior results when NE is sometimes ambiguous.
Approach:	They propose a relation extraction model with two training stages that uses adversarial multi-task learning to recover the given NEs.
Outcome:	The proposed model improves on two English benchmark datasets and shows state-of-the-art performance.

Adversarial Attack against Cross-lingual Knowledge Graph Alignment (2021.emnlp-main)

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Zeru Zhang, Zijie Zhang, Yang Zhou, Lingfei Wu, Sixing Wu, Xiaoying Han, Dejing Dou, Tianshi Che, Da Yan

Element Intervention for Open Relation Extraction (2021.acl-long)

Challenge:	Existing studies on cross-lingual entity alignment under adversarial attacks have not been conducted.
Approach:	They propose to use adversarial attack techniques to perturb cross-lingual entity alignment under adversarials.
Outcome:	The proposed model hides the attacked entities in dense regions in two KGs, and reduces the gradient vanishing issues in the process of adversarial attacks for further improving the attack effectiveness.

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Fangchao Liu, Lingyong Yan, Hongyu Lin, Xianpei Han, Le Sun

Can LLM Agents Simulate Multi-Turn Human Behavior? Evidence from Real Online Customer Behavior Data (2026.acl-long)

Challenge:	Current OpenRE models are often trained on the datasets generated from distant supervision, which often results in instability and makes the model easily collapsed.
Approach:	They propose to use a causal model to identify relation instances referring to the same relation . they propose to perform Element Interventions on context and entities respectively .
Outcome:	The proposed method outperforms existing methods and is robust across datasets.

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Yuxuan Lu, Jing Huang, Yan Han, Bingsheng Yao, Sisong Bei, Yaochen Xie, Yisi Sang, Qi He, Dakuo Wang

DSQG-Syn: Synthesizing High-quality Data for Text-to-SQL Parsing by Domain Specific Question Generation (2025.findings-naacl)

Challenge:	Recent research shows that LLM Agents can generate “believable” human behaviors via prompt-only methods, leaving open questions of whether they can accurately generate step-by-step actions in multi-turn interaction tasks.
Approach:	They propose to use shopping data to evaluate LLMs' ability to accurately generate step-by-step actions in a multi-turn interaction task.
Outcome:	The proposed model achieves 17.26% action generation accuracy and 33.86% F1 score on final purchase prediction, representing improvements of 5.4% and 13.85% over baselines.

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Shaoming Duan, Youxuan Wu, Chuanyi Liu, Yuhao Zhang, Zirui Wang, Peiyi Han, Shengyuan Yu, Liang Yan, Yingwei Liang

Programming Languages in NLP

Challenge:	Existing methods for generating SQL queries using natural language questions produce inconsistent NLQ-SQL pairs.
Approach:	They propose a text-to-SQL data synthesis framework that generates domain-relevant questions . they synthesize NLQ-SqL pairs that are domain-specific and intent-consistent .
Outcome:	The proposed method outperforms closed-source LLMs on the Text-to-SQL task.

PhiloGPT: A Philology-Oriented Large Language Model for Ancient Chinese Manuscripts with Dunhuang as Case Study (2024.emnlp-main)

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Yuqing Zhang, Baoyi He, Yihan Chen, Hangqi Li, Han Yue, Shengyu Zhang, Huaiyong Dou, Junchi Yan, Zemin Liu, Yongquan Zhang, Fei Wu

Extracting and Understanding the Superficial Knowledge in Alignment (2025.naacl-long)

Challenge:	philology requires years of professional training in extensive knowledge memorization and manual textual retrieval.
Approach:	They curated the PhiloCorpus-ZH, a rich collec-tion of ancient Chinese texts spanning a millennium with 30 diverse topics, including firsthand folk copies.
Outcome:	The PhiloCorpus-ZH corpus facilitated the development of the first LLM tailored for discovering ancient Chinese manuscripts.

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Runjin Chen, Gabriel Jacob Perin, Xuxi Chen, Xilun Chen, Yan Han, Nina S. T. Hirata, Junyuan Hong, Bhavya Kailkhura

Challenge:	Recent studies have shown that alignment of large language models with human values and preferences requires substantial data and computation resources.
Approach:	They propose a method to extract and isolate superficial knowledge from aligned models by focusing on the shallow modifications to the final token selection process.
Outcome:	The proposed method extracts and isolates superficial knowledge from aligned models, focusing on the shallow modifications to the final token selection process.

Understanding the Dark Side of LLMs’ Intrinsic Self-Correction (2025.acl-long)

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Qingjie Zhang, Di Wang, Haoting Qian, Yiming Li, Tianwei Zhang, Minlie Huang, Ke Xu, Hewu Li, Liu Yan, Han Qiu

Challenge:	Recent studies show that LLMs’ intrinsic self-correction fails without oracle labels as feedback.
Approach:	They propose to use one simple task and three complex tasks with state-of-the-art LLMs like ChatGPT, Llama, and DeepSeek to interpret LLM's intrinsic self-correction.
Outcome:	The proposed methods reveal the dark side of LLMs’ intrinsic self-correction for different tasks, especially for those failure cases.

Multi-Scale Spectral Selection and Entropy-Guided Uncertainty Fusion for Multimodal Rumor Detection (2026.findings-acl)

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Zongliang Han, Wenyu Guo, Guoqing Jin, Yang Liu, Fan Li, Dong Yu, Yan Song, null Zhangfengzhen

Decomposing Complex Questions Makes Multi-Hop QA Easier and More Interpretable (2021.findings-emnlp)

Challenge:	Existing methods for multimodal content detection fail to capture cross-modal semantic inconsistencies and ignore inherent noise in multimodal features.
Approach:	They propose a multimodal rumor detection method based on a frequency domain spectral selection method and entropy-guided uncertainty fusion method to capture cross-modal semantic inconsistencies.
Outcome:	The proposed method outperforms state-of-the-art methods in multimodal rumor detection . it shows stronger detection capability and robustness on multiple datasets .

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Ruiliu Fu, Han Wang, Xuejun Zhang, Jun Zhou, Yonghong Yan

Responsible & Trustworthy NLP

Challenge:	Multi-hop QA requires the machine to answer complex questions through finding multiple clues and reasoning, and provide explanatory evidence to demonstrate the reasoning process.
Approach:	They propose a three-stage framework based on complex question decomposition that decomposes the complex question, then reads the sub-questions and then performs numerical comparison to get the final answer.
Outcome:	The proposed framework achieves state-of-the-art in the 2WikiMultiHopQA dataset, with a winning joint F1 score of 53.58 on the leaderboard.

MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)

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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie

Modeling Instance Interactions for Joint Information Extraction with Neural High-Order Conditional Random Field (2023.acl-long)

Challenge:	Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models.
Approach:	They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts.
Outcome:	The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings .

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Zixia Jia, Zhaohui Yan, Wenjuan Han, Zilong Zheng, Kewei Tu

EVOTOOL: Self-Evolving Tool-Use Policy Optimization in LLM Agents via Blame-Aware Mutation and Diversity-Aware Selection (2026.acl-long)

Challenge:	Existing approaches to joint Information Extraction (IE) neglect cross-instance or cross-task dependencies.
Approach:	They propose a joint IE framework that formulates joint 'conditional random field' to model cross-instance interactions . they incorporate a high-order neural decoder that is unfolded from a mean-field variational inference method .
Outcome:	The proposed approach improves on three IE tasks compared with baseline and prior work.

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Shuo Yang, Caren Han, Xueqi Ma, Yan Li, Mohammad Reza Ghasemi Madani, Eduard Hovy

Challenge:	Existing approaches to optimize tool-use policies are monolithic and prone to entangling behaviors.
Approach:	They propose a framework that decomposes agent’stool-use policy into four modules and improves them via three mechanisms.
Outcome:	The proposed framework outperforms strong baselines on bothGPT-4.1 and Qwen3-8B while maintaining superior efficiency and transferability.

HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation (2022.acl-long)

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Zhoujun Cheng, Haoyu Dong, Zhiruo Wang, Ran Jia, Jiaqi Guo, Yan Gao, Shi Han, Jian-Guang Lou, Dongmei Zhang

Natural Language Interfaces

Challenge:	Existing studies on table reasoning focus on flat tables and hierarchical tables . a new dataset, HiTab, aims to examine numerical reasoning over hierarchic tables based on hierarchically structured tables - a strong challenge for existing baselines and a valuable benchmark for future research.
Approach:	They propose a hierarchical question answering and natural language generation dataset to study hierarchic tables.
Outcome:	The proposed model shows that it is effective in QA and natural language generation over hierarchical tables.

MIDAS: Multi-level Intent, Domain, And Slot Knowledge Distillation for Multi-turn NLU (2025.findings-naacl)

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Yan Li, So-Eon Kim, Seong-Bae Park, Caren Han

WebCPM: Interactive Web Search for Chinese Long-form Question Answering (2023.acl-long)

Challenge:	Existing Large Language Models (LLMs) can generate coherent text, but they struggle to recognise user intent behind queries.
Approach:	They propose a novel approach leveraging multi-level intent, domain, and slot knowledge distillation for multi-turn NLU.
Outcome:	The proposed model improves multi-turn conversation understanding by integrating teacher teachers into a student model.

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Yujia Qin, Zihan Cai, Dian Jin, Lan Yan, Shihao Liang, Kunlun Zhu, Yankai Lin, Xu Han, Ning Ding, Huadong Wang, Ruobing Xie, Fanchao Qi, Zhiyuan Liu, Maosong Sun, Jie Zhou

Information Retrieval

Challenge:	Long-form question answering requires two procedures: information retrieval and information synthesis.
Approach:	They propose a Chinese long-form question answering dataset called WebCPM . the dataset is based on a web search interface that engages with a search engine in real time .
Outcome:	The proposed dataset generates answers that are no worse than human-written ones . the dataset is the first Chinese LFQA dataset .

MM-Doc-R1: Training Agents for Long Document Visual Question Answering through Multi-turn Reinforcement Learning (2026.findings-acl)

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Jiahang Lin, Kai Hu, Binghai Wang, Yuhao Zhou, Zhiheng Xi, Honglin Guo, Shichun Liu, Junzhe Wang, Shihan Dou, Enyu Zhou, Hang Yan, Zhenhua Han, Tao Gui, Qi Zhang, Xuanjing Huang

Visual Data in NLP

Challenge:	Existing work on long document visual question answering is based on Retrieval-Augmented Generation (RAG) where textual or visual content is encoded into embeddings and relevance is determined by similarity scores with respect to the original query.
Approach:	They propose a framework that employs an agentic, vision-aware workflow to address long document visual question answering through iterative information discovery and synthesis.
Outcome:	The proposed framework outperforms existing RL systems by 10.4% on the MMLongbench-Doc benchmark and demonstrates superior training performance over GRPO.

Efficient Paths and Dense Rewards: Probabilistic Flow Reasoning for Large Language Models (2026.acl-long)

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Yan Liu, Feng Zhang, Zhanyu Ma, Jun Xu, Jiuchong Gao, Jinghua Hao, Renqing He, Han Liu, Yangdong Deng

CheckRLM: Effective Knowledge–Thought Coherence Checking in Retrieval-Augmented Reasoning (2026.acl-long)

Challenge:	Existing approaches to mitigate inference inefficiency and optimization difficulty are fragmented and constrained by inherent trade-offs.
Approach:	They propose a framework that reconceptualizes discrete reasoning steps as a continuous probabilistic flow, quantifying the contribution of each step toward the ground-truth answer.
Outcome:	The proposed framework achieves a superior balance between inference efficiency and reasoning performance on challenging benchmarks.

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Dingling Xu, Ruobing Wang, Qingfei Zhao, Yukun Yan, Zhichun Wang, Daren Zha, Shi Yu, Zhenghao Liu, Shuo Wang, Xu Han, Maosong Sun

Reasoning

Challenge:	Reasoning Language Models (RLMs) have improved performance on complex tasks by extending the reasoning chain, but they are prone to factual errors, especially in knowledge-intensive tasks.
Approach:	They propose a framework that improves the reliability of the reasoning process by timely checking and correcting factual errors.
Outcome:	The proposed framework outperforms baselines and shows that it mitigates error accumulation with lower costs.

Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories (2021.emnlp-main)

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Han Qin, Guimin Chen, Yuanhe Tian, Yan Song

Sentiment Analysis

Challenge:	Aspect-based sentiment analysis (ABSA) predicts sentiment polarity for aspect term in sentences . labeled data stored at different locations and inaccessible due to privacy or legal concerns .
Approach:	They propose a model with federated learning to combine labeled data across different domains . they incorporate topic memory to take data from diverse domains into consideration .
Outcome:	The proposed model outperforms baselines on a simulated environment with three nodes.

Learning to Bootstrap for Entity Set Expansion (D19-1)

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Lingyong Yan, Xianpei Han, Le Sun, Ben He

Revisiting the Reliability of Language Models in Instruction-Following (2026.acl-long)

Challenge:	Existing bootstrapping methods for Entity Set Expansion suffer from two problems: 1) delayed feedback and sparse supervision.
Approach:	They propose a method that estimates delayed feedback and adaptively scores entities given sparse supervision signals.
Outcome:	The proposed method can estimate delayed feedback for pattern evaluation and adaptively score entities given sparse supervision signals.

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Jianshuo Dong, Yutong Zhang, Liu Yan, Zhenyu Zhong, Tao Wei, Chao Zhang, Han Qiu

MUSE: MCTS-Driven Red Teaming Framework for Enhanced Multi-Turn Dialogue Safety in Large Language Models (2025.emnlp-main)

Challenge:	Several benchmarks have been proposed to measure instruction-following accuracy, but these scores do not translate to reliable services in real-world use.
Approach:	They propose a new metric reliable@k and develop an automated pipeline to generate cousin prompts.
Outcome:	The proposed model can be instantiated with cousin prompts and generates high-quality cousin prompt data.

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Siyu Yan, Long Zeng, Xuecheng Wu, Chengcheng Han, Kongcheng Zhang, Chong Peng, Xuezhi Cao, Xunliang Cai, Chenjuan Guo

UltraLink: An Open-Source Knowledge-Enhanced Multilingual Supervised Fine-tuning Dataset (2024.acl-long)

Challenge:	Existing defenses target single-turn attacks, but real-world usage involves multi-turn dialogues, exposing models to attacks that exploit conversational context to bypass safety measures.
Approach:	They propose a framework that tackles multi-turn jailbreaks from both attack and defense angles.
Outcome:	Experiments on large language models show that MUSE effectively mitigates multi-turn jailbreaks.

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Haoyu Wang, Shuo Wang, Yukun Yan, Xujia Wang, Zhiyu Yang, Yuzhuang Xu, Zhenghao Liu, Liner Yang, Ning Ding, Xu Han, Zhiyuan Liu, Maosong Sun

Unlocking the Effectiveness of LoRA-FP for Seamless Transfer Implantation of Fingerprints in Downstream Models (2025.findings-emnlp)

Challenge:	Open-source large language models (LLMs) have gained strength across diverse fields, but the majority of studies focus on English.
Approach:	They propose a knowledge-grounded data augmentation approach to elicit more language-specific knowledge of LLMs by enhancing their ability to serve users from different countries.
Outcome:	The proposed method can prune the language-agnostic supervised fine-tuning dataset without any performance degradation.

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Zhenhua Xu, Zhaokun Yan, Binhan Xu, Xin Tong, Haitao Xu, Yourong Chen, Meng Han

A Hybrid Approach to Automatic Corpus Generation for Chinese Spelling Check (D18-1)

Challenge:	lightweight plug-and-play framework that encodes backdoor fingerprints into LoRA adapters .
Approach:	proposed framework encodes backdoor fingerprints into LoRA adapters via constrained fine-tuning . enables seamless fingerprint transplantation through parameter fusion, eliminating full-parameter updates while maintaining integrity.
Outcome:	The proposed framework achieves superior robustness against various scenarios while reducing computational overhead compared to traditional approaches.

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Dingmin Wang, Yan Song, Jing Li, Jialong Han, Haisong Zhang

Challenge:	Chinese spelling check (CSC) is a challenging but meaningful task that serves as a preprocessing in many natural language processing(NLP) applications.
Approach:	They propose to construct Chinese spelling check corpus with automatically generated spelling errors, which are either visually or phonologically resembled characters, corresponding to OCR- and ASR-based methods. Experimental results demonstrate the effectiveness of the approach.
Outcome:	The proposed method is based on visual or phonologically similar spelling errors, and is validated with respect to three standard test sets.

From Style to Story: A Curriculum Learning Approach for Imitative Novel Generation (2026.findings-acl)

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Xueran Han, Yuhan Liu, Mingzhe Li, Wei Liu, Sen Hu, Rui Yan, Zhiqiang xu, Xiuying Chen

ChiMST: A Chinese Medical Corpus for Word Segmentation and Medical Term Recognition (2022.lrec-1)

Challenge:	Novels create rich, immersive worlds with intricate plots and distinct styles, captivating readers through complex storytelling.
Approach:	They propose a novel generation system that imitates novel elements by predicting plot developments and writing concrete details using vivid, expressive language.
Outcome:	The novel imitative novel generation system is trained through a curriculum learning paradigm, progressing from low-level stylistic mastery to high-level narrative coherence.

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Yuanhe Tian, Han Qin, Fei Xia, Yan Song

Federated Chinese Word Segmentation with Global Character Associations (2021.findings-acl)

Challenge:	Chinese word segmentation and named entity recognition are important tasks in natural language processing.
Approach:	They develop a Chinese medical corpus annotated with Chinese word boundary and medical term information to address this problem.
Outcome:	The proposed corpus will be a valuable resource for Chinese word segmentation and named entity recognition research on the medical domain.

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Yuanhe Tian, Guimin Chen, Han Qin, Yan Song

Syntactic Text Processing

Challenge:	Chinese word segmentation (CWS) is a fundamental task for natural language processing.
Approach:	They propose a neural model for Chinese word segmentation with federated learning to help CWS deal with data isolation.
Outcome:	The proposed model outperforms baselines on a simulated environment with five nodes.

An Empirical Study of LLM Reasoning Ability Under Strict Output Length Constraint (2025.emnlp-main)

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Yi Sun, Han Wang, Jiaqiang Li, Jiacheng Liu, Xiangyu Li, Hao Wen, Yizhen Yuan, Huiwen Zheng, Yan Liang, Yuanchun Li, Yunxin Liu

Low-Resource NLP

Challenge:	Large Language Models (LLMs) are a powerful tool for test-time scaling, but they are often used under time constraints.
Approach:	They propose to use LLMs to make models think before answering questions . they also use self-correction and best-of-N decoding to encourage deeper thinking .
Outcome:	The proposed models are able to achieve higher inference accuracy with extra inference computation under time constraints.

Global Bootstrapping Neural Network for Entity Set Expansion (2020.findings-emnlp)

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Lingyong Yan, Xianpei Han, Ben He, Le Sun

A Training-Free Length Extrapolation Approach for LLMs: Greedy Attention Logit Interpolation (2025.emnlp-main)

Challenge:	Recent studies have shown that end-to-end bootstrapping methods only leverage local semantics rather than global semantics.
Approach:	They propose a global-sighted encoder to capture and encode local and global semantics into entity embedding and an attention-guided decoder to sequentially expand new entities based on these embeddables.
Outcome:	The proposed network achieves state-of-the-art on two bootstrapping datasets.

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Yan Li, Tianyi Zhang, Zechuan Li, Caren Han

Progressive Adversarial Learning for Bootstrapping: A Case Study on Entity Set Expansion (2021.emnlp-main)

Challenge:	Existing solutions to problem of positional out-of-distribution (O.O.D.) are inefficient, redundant, and lack local positional information.
Approach:	They propose a training-free method that greedily reuses pretrained positional intervals and interpolates attention logits to eliminate outliers.
Outcome:	The proposed method achieves stable and superior performance across long-context tasks without requiring input-length-specific tuning.

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Lingyong Yan, Xianpei Han, Le Sun