Papers by Jia Wu

Enhancing Pre-trained Models with Text Structure Knowledge for Question Generation (2022.coling-1)

Challenge:	Existing methods to recognize entities in text are limited by the diversity of entity types and the lack of high-quality annotations.
Approach:	They propose an in-context learning-based NER approach that can inject in-const NER ability into PLMs and recognize entities of novel types on-the-fly using only a few demonstrative instances.
Outcome:	The proposed method outperforms the PLMs+fine-tuning counterparts on 4 few-shot NER datasets and significantly outperformed the Plms+initialized extractors.

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Zichen Wu, Xin Jia, Fanyi Qu, Yunfang Wu

CamoQuery: Language-Guided Reasoning Camouflaged Object Segmentation (2026.acl-long)

Challenge:	Existing question generation models treat input passage as a sequence-to-sequence generative task, but they are not aware of text structure.
Approach:	They propose to model text structure as answer position and syntactic dependency and propose a mask attention mechanism to make syntaktic structure of input passage accessible.
Outcome:	The proposed model outperforms the strong pre-trained model ProphetNet on a SQuAD dataset and achieves competitive results with the state-of-the-art model.

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Tianxin Han, Qing Dong, Xingwei Wang, Jie Jia, Gang Wu, Bowen Yang, Fu Zhang

Text is All You Need: LLM-enhanced Incremental Social Event Detection (2025.acl-long)

Challenge:	Existing methods for camouflaged object segmentation are limited to vision-only mask prediction under fixed task assumptions.
Approach:	They propose a language-guided reasoning camouflaged object segmentation task that generates an intent-consistent segmentation mask from an image and an implicit query text instruction.
Outcome:	The proposed task can generate an intent-consistent segmentation mask from a camouflaged image and an implicit query text instruction.

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Zitai Qiu, Congbo Ma, Jia Wu, Jian Yang

Information Extraction & Text Mining

Challenge:	Existing state-of-the-art (SOTA) SED models rely on graph neural networks (GNNs) Existing SED frameworks rely heavily on GNNs, which require complex graph construction and time-consuming training processes.
Approach:	They propose a framework that leverages the rich background knowledge of large language models to formalize and disambiguate short texts by completing abbreviations and summarizing informal expressions.
Outcome:	The proposed framework outperforms existing models on two challenging real-world datasets.

Retrieval and Reasoning on KGs: Integrate Knowledge Graphs into Large Language Models for Complex Question Answering (2024.findings-emnlp)

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Yixin Ji, Kaixin Wu, Juntao Li, Wei Chen, Mingjie Zhong, Xu Jia, Min Zhang

Towards Boosting LLMs-driven Relevance Modeling with Progressive Retrieved Behavior-augmented Prompting (2025.coling-industry)

Challenge:	Large Language Models (LLMs) have performed impressively in various NLP tasks, but their inherent hallucination phenomena severely challenge their credibility in complex reasoning.
Approach:	They propose to integrate explainable Knowledge Graphs (KGs) with LLMs to alleviate hallucinations . they construct subgraphs to enhance the retrieval capabilities of KGs via CoT reasoning.
Outcome:	Extensive experiments on two KGQA datasets show that the proposed model achieves convincing performance compared to strong baselines.

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Zeyuan Chen, Haiyan Wu, Kaixin Wu, Wei Chen, Mingjie Zhong, Jia Xu, Zhongyi Liu, Wei Zhang

Jailbreak Open-Sourced Large Language Models via Enforced Decoding (2024.acl-long)

Challenge:	Existing approaches to relevance modeling have lacked generalization and accuracy . recent studies have focused on capturing the semantic relationships between queries and items .
Approach:	They propose a framework that integrates world knowledge stored in LLMs with specialized domain knowledge represented by user behavior data for promising performance.
Outcome:	The proposed framework can handle full-scale search traffics of Alipay with acceptable cost and latency.

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Hangfan Zhang, Zhimeng Guo, Huaisheng Zhu, Bochuan Cao, Lu Lin, Jinyuan Jia, Jinghui Chen, Dinghao Wu

RealChart2Code: Bridging the Gap in Real-World Chart-to-Code Generation via Multi-Task Evaluation (2026.acl-long)

Challenge:	Existing studies show that Large Language Models can be misused to generate undesired content.
Approach:	They propose to use large language models to manipulate the generation process to generate undesired content without heavy computations or prompt designs.
Outcome:	The proposed method shows that open-sourced large language models could be misused to generate undesired content without heavy computations or prompt designs.

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Jiajun Zhang, Yuying Li, Zhixun Li, Xingyu Guo, Jingzhuo Wu, Leqi Zheng, Yiran Yang, Jianke Zhang, Qingbin Li, Shannan Yan, Changguo Jia, Junfei Wu, Zilei Wang, Qiang Liu, Liang Wang

Text Generation

Challenge:	Vision-Language Models (VLMs) have demonstrated impressive capabilities in code generation across various domains, but their ability to replicate complex, multi-panel visualizations remains largely unassessed.
Approach:	They propose a large-scale benchmark to evaluate chart generation from large- scale raw data and assess iterative code refinement in a multi-turn conversational setting.
Outcome:	The new benchmark evaluates 14 leading VLMs on real-world data and shows they struggle with complex plot structures and authentic data.

Demystifying Synthetic Data in LLM Pre-training: A Systematic Study of Scaling Laws, Benefits, and Pitfalls (2025.emnlp-main)

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Feiyang Kang, Newsha Ardalani, Michael Kuchnik, Youssef Emad, Mostafa Elhoushi, Shubhabrata Sengupta, Shang-Wen Li, Ramya Raghavendra, Ruoxi Jia, Carole-Jean Wu

On Fake News Detection with LLM Enhanced Semantics Mining (2024.emnlp-main)

Challenge:	a large-scale empirical study compares natural web data, diverse synthetic types, and mixtures of natural and synthetic data.
Approach:	They conduct a large-scale empirical study on large-volume LLMs using a unified protocol and scaling laws.
Outcome:	The proposed method is faster than pre-training on natural web data, the authors show . their results are consistent with previous studies on rephrased text and textbooks .

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Xiaoxiao Ma, Yuchen Zhang, Kaize Ding, Jian Yang, Jia Wu, Hao Fan

How to Ask Good Questions? Try to Leverage Paraphrases (2020.acl-main)

Challenge:	Existing methods for detecting fake news use only news embeddings to capture the lexical semantics between tokens.
Approach:	They propose a topic-based model with prompts to extract news embeddings from LLMs and a generalized page-rank model to extract local and global semantics.
Outcome:	The proposed model shows superior performance on five benchmark datasets over seven baseline methods.

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Xin Jia, Wenjie Zhou, Xu Sun, Yunfang Wu

Text Generation

Challenge:	Existing methods to generate human-like questions rely on paraphrases to generate good questions.
Approach:	They propose to integrate paraphrase knowledge into question generation to generate human-like questions by combining paraphrases with a back-translation method.
Outcome:	The proposed model achieves obvious performance gain over several strong baselines and human evaluation validates that it can ask questions of high quality by leveraging paraphrase knowledge.

Neural Machine Translation for Agglutinative Languages via Data Rejuvenation (2025.acl-srw)

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Chen Zhao, Yatu Ji, Ren Qing-Dao-Er-Ji, Nier Wu, Lei Shi, Fu Liu, Yepai Jia

Multilinguality

Challenge:	Recent years, advances in Neural Machine Translation (NMT) heavily rely on large-scale parallel corpora.
Approach:	They propose to combine fine-grained inactive sample identification with target-side rejuvenation to improve translation quality from agglutinative languages.
Outcome:	The proposed framework improves on four low-resource agglutinative language tasks.

CAMEC: Complexity-Aware Multi-Expert Collaboration for Reliable Chinese Medical Question Answering (2026.acl-long)

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Yukang Wu, Xiyuan Jia, Jiayi Wu, HongChen Yu, Yuhan Qiu, Wu Guohua

Question Answering

Challenge:	Large language models are promising for medical question answering in china, but remain unreliable due to hallucinations, weak factual grounding and difficulty handling clinically complex cases.
Approach:	They propose a framework that combines hierarchical medical adaptation with complexity-aware expert routing for reliable Chinese medical QA.
Outcome:	The proposed framework outperforms strong general and medical LLM baselines on four Chinese medical benchmarks.

P2P: A Poison-to-Poison Remedy for Reliable Backdoor Defense in LLMs (2026.findings-acl)

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Shuai Zhao, Xinyi Wu, Shiqian Zhao, Xiaobao Wu, Zhongliang Guo, Yanhao Jia, Anh Tuan Luu

Robustness in NLP

Challenge:	Defending Large Language Models (LLMs) against backdoors has long been trapped in a "cat-and-mouse" dilemma where defenders passively react to ever-shifting attack strategies.
Approach:	They propose a general and effective defense algorithm that implants benign triggers to reshape the model’s decision boundary.
Outcome:	The proposed defense algorithm can neutralize malicious backdoors while preserving task performance.

Multi-Programming Language Sandbox for LLMs (2025.acl-demo)

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Shihan Dou, Jiazheng Zhang, Jianxiang Zang, Yunbo Tao, Weikang Zhou, Haoxiang Jia, Shichun Liu, Yuming Yang, Shenxi Wu, Zhiheng Xi, Muling Wu, Rui Zheng, Changze Lv, Limao Xiong, Shaoqing Zhang, Lin Zhang, Wenyu Zhan, Rongxiang Weng, Jingang Wang, Xunliang Cai, Yueming Wu, Ming Wen, Yixin Cao, Tao Gui, Xipeng Qiu, Qi Zhang, Xuanjing Huang

DIDS: Domain Impact-aware Data Sampling for Large Language Model Training (2025.emnlp-main)

Challenge:	MPLSandbox is an out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for Large Language Models (LLMs).
Approach:	They propose a multi-programming language sandbox that provides unified feedback from compilers and analysis tools for Large Language Models.
Outcome:	The proposed multi-language sandbox can provide comprehensive feedback from compilers and analysis tools for large language models (LLMs).

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Weijie Shi, Jipeng Zhang, Yaguang Wu, Jingzhi Fang, Shibo Zhang, Yao Zhao, Hao Chen, Ruiyuan Zhang, Yue Cui, Jia Zhu, Sirui Han, Jiajie Xu, Xiaofang Zhou

The Task Shield: Enforcing Task Alignment to Defend Against Indirect Prompt Injection in LLM Agents (2025.acl-long)

Challenge:	Existing approaches for optimizing domain-level sampling strategies struggle with maintaining intra-domain consistency and accurately measuring domain impact.
Approach:	They propose to use a Fisher-Information Matrix-guided metric to measure domain impact to ensure intra-domain consistency and accuracy.
Outcome:	The proposed model achieves 3.4% higher average performance while maintaining comparable training efficiency.

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Feiran Jia, Tong Wu, Xin Qin, Anna Squicciarini

PhysReason: A Comprehensive Benchmark towards Physics-Based Reasoning (2025.acl-long)

Challenge:	Large Language Model (LLM) agents are becoming conversational assistants . indirect prompt injection attacks pose a critical threat to these systems .
Approach:	They propose a novel and orthogonal perspective that reframes agent security . they propose 'task shield' that verifies whether each instruction and tool call contributes to user objectives .
Outcome:	The proposed defense reduces attack success rates while maintaining high task utility on the AgentDojo benchmark.

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Xinyu Zhang, Yuxuan Dong, Yanrui Wu, Jiaxing Huang, Chengyou Jia, Basura Fernando, Mike Zheng Shou, Lingling Zhang, Jun Liu

HetGCoT: Heterogeneous Graph-Enhanced Chain-of-Thought LLM Reasoning for Academic Question Answering (2025.findings-emnlp)

Challenge:	Large language models demonstrate remarkable capabilities across various domains, including mathematics and logic reasoning.
Approach:	They propose a physics-based reasoning benchmark that includes physics theorems and constraints and a Physics Solution Auto Scoring Framework to evaluate physics based reasoning in large language models.
Outcome:	The proposed framework enables models to achieve less than 60% on answer-level evaluation, with performance dropping from knowledge questions (75.11%) to hard problems (31.99%).

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Runsong Jia, Mengjia Wu, Ying Ding, Jie Lu, Yi Zhang

Question Answering

Challenge:	graph neural networks capture structured graph information, but lack integration at the reasoning level.
Approach:	They propose a framework that leverages graph structural information to reason interpretable academic QA results.
Outcome:	The proposed framework outperforms sota baselines on OpenAlex and DBLP datasets.

Separation and Fusion: A Novel Multiple Token Linking Model for Event Argument Extraction (2024.naacl-long)

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Jing Xu, Dandan Song, Siu Hui, Zhijing Wu, Meihuizi Jia, Hao Wang, Yanru Zhou, Changzhi Zhou, Ziyi Yang

Information Extraction & Text Mining

Challenge:	Existing methods for event argument extraction (EAE) lack cross-event information and require longer role sequences . et al. (2017): outperforms state-of-the-art methods for EE.
Approach:	They propose a separation-and-fusion paradigm to separate the acquisition of cross-event information and fuse it into the argument extraction of a target event.
Outcome:	The proposed model outperforms the state-of-the-art models on four widely used datasets.

Logits-Based Finetuning (2025.emnlp-main)

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Jingyao Li, Senqiao Yang, Sitong Wu, Han Shi, Chuanyang Zheng, Hong Xu, Jiaya Jia

Dynabench: Rethinking Benchmarking in NLP (2021.naacl-main)

Challenge:	Existing methods for developing compact and efficient large language models lack token-level dependencies and linguistic diversity.
Approach:	They propose a logits-based fine-tuning framework that integrates supervised learning and knowledge distillation to build enriched training targets using teacher logits and ground truth labels.
Outcome:	The proposed method outperforms existing methods on a large-scale logits dataset and a series of science-focused models.

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Douwe Kiela, Max Bartolo, Yixin Nie, Divyansh Kaushik, Atticus Geiger, Zhengxuan Wu, Bertie Vidgen, Grusha Prasad, Amanpreet Singh, Pratik Ringshia, Zhiyi Ma, Tristan Thrush, Sebastian Riedel, Zeerak Waseem, Pontus Stenetorp, Robin Jia, Mohit Bansal, Christopher Potts, Adina Williams

Challenge:	Dynabench is an open-source platform for dynamic dataset creation and model benchmarking.
Approach:	They propose an open-source platform for dynamic dataset creation and model benchmarking.
Outcome:	The proposed platform can be used to create models that fail on simple challenges and falter in real-world scenarios.

Sampling Matters! An Empirical Study of Negative Sampling Strategies for Learning of Matching Models in Retrieval-based Dialogue Systems (D19-1)

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Jia Li, Chongyang Tao, Wei Wu, Yansong Feng, Dongyan Zhao, Rui Yan

Natural Language Interfaces

Challenge:	Existing studies focus on constructing a matching model with sophisticated neural architectures, but do little to how to effectively learn such architectures from data.
Approach:	They propose to sample negative examples to automatically construct a training set for effective model learning in retrieval-based dialogue systems by using four sampling strategies.
Outcome:	The proposed learning method improves the performance of matching models on two benchmarks with three matching models.

Language Resource Efficient Learning for Captioning (2021.findings-emnlp)

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Jia Chen, Yike Wu, Shiwan Zhao, Qin Jin

QuickLLaMA: Query-aware Inference Acceleration for Large Language Models (2025.coling-main)

Challenge:	XE loss and SC loss are both considered to be performance degradations for captioning tasks.
Approach:	They propose to generalize the single pairwise comparison in SC loss and use multiple generalized pairwise compares to reduce noise in baseline.
Outcome:	The proposed method outperforms state-of-the-art models on a video caption dataset using only half of the language resources.

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Jingyao Li, Han Shi, Sitong Wu, Chuanyang Zheng, Zhenguo Li, Xin Jiang, Hong Xu, Jiaya Jia

Rethinking Reasoning: A Survey on Reasoning-based Backdoors in LLMs (2026.findings-acl)

Challenge:	Large Language Models (LLMs) struggle with capturing long-distance dependencies within sequences to deeply understand semantics.
Approach:	They propose a system that captures relevant information within a fixed window size and provides precise answers to queries.
Outcome:	The proposed system can read Harry Potter within 30s and accurately answer the questions.

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Man Hu, Xinyi Wu, Zhufeng Suo, Jinbo Feng, Linghui Meng, Yanhao Jia, Anh Tuan Luu, Shuai Zhao

OpenS2S: Advancing Fully Open-Source End-to-End Empathetic Large Speech Language Model (2025.emnlp-demos)

Challenge:	Recent models such as OpenAI o1 and DeepSeek-R1 produce explicit reasoning traces, often via Chain-of-Thought prompting.
Approach:	They propose a taxonomy that offers a unified perspective for summarizing existing approaches and categorizing reasoning-based backdoor attacks into associative, passive, and active.
Outcome:	The proposed taxonomy categorizes reasoning-based backdoor attacks into associative, passive, and active.

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Chen Wang, Tianyu Peng, Wen Yang, YiNan Bai, Guangfu Wang, Jun Lin, Lanpeng Jia, Lingxiang Wu, Jinqiao Wang, Chengqing Zong, Jiajun Zhang

Uni-Retrieval: A Multi-Style Retrieval Framework for STEM’s Education (2025.acl-long)

Challenge:	Empathetic speech models are increasingly closed off, leaving details about the architecture, data and development opaque to researchers.
Approach:	They propose an open-source empathetic speech-to-text model with a streaming interleaved decoding architecture and a data pipeline to enable end-to end training.
Outcome:	The proposed model is open-source and transparent, with no data or data required to build it.

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Yanhao Jia, Xinyi Wu, Li Hao, QinglinZhang QinglinZhang, Yuxiao Hu, Shuai Zhao, Wenqi Fan

Low-Resource NLP

Challenge:	Current retrieval models focus on natural text-image retrieval, which is insufficient for STEM education contexts due to ambiguities in the retrieval process.
Approach:	They propose a diverse expression retrieval task tailored to educational scenarios . they extract query style features as prototypes and build a continuously updated Prompt Bank .
Outcome:	The proposed model outperforms existing retrieval models in most retrieval tasks.

Evidence-Aligned Entity Verification for Hallucination Detection in Retrieval-Augmented Generation (2026.findings-acl)

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Runsong Jia, Zhen Fang, Mengjia Wu, Jie Lu, Yi Zhang

Towards Identification and Intervention of Safety-Critical Parameters in Large Language Models (2026.findings-acl)

Challenge:	Existing methods for hallucination detection depend on internal signals like uncertainty and self-consistency checks to identify unreliable outputs.
Approach:	They propose a retrieval-augmented generation method to enhance hallucination detection by addressing information updating challenges.
Outcome:	The proposed method improves on existing methods with strong generalization capabilities.

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Weiwei Qi, Zefeng Wu, Tianhang Zheng, Zikang Zhang, Xiaojun Jia, Zhan Qin, Kui Ren

Beyond Transcription: Unified Audio Schema for Perception-Aware AudioLLMs (2026.findings-acl)

Challenge:	Existing safety-related methodologies for large language models are lacking . despite advances in safety alignment techniques, safeguarding LLMs during adaptation to various tasks remains a challenge.
Approach:	They propose a framework to quantify how different parameters affect LLM safety . they propose two targeted intervention paradigms for safety enhancement and preservation .
Outcome:	The proposed framework reveals safety-critical patterns across different LLM architectures.

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Linhao Zhang, Yuhan Song, Aiwei Liu, Chuhan Wu, Sijun Zhang, Wei Jia, Yuan Liu, Houfeng Wang, Zhou Xiao

Can Intelligent Agents Revolutionize Scale Generation? (2026.findings-acl)

Challenge:	Recent Audio Large Language Models (AudioLLMs) excel at reasoning tasks, but struggle at elementary auditory perception.
Approach:	They propose a framework that organizes audio information into three explicit components in a unified JSON format.
Outcome:	The proposed framework boosts fine-grained perception by 10.9% on MMSU over state-of-the-art models while preserving robust reasoning capabilities.

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Chenghao Jia, Zhitao Yuan, Zhaokang Zong, YiFei Yin, Zhe Chen, Man Lan, Shengjun Wu

Challenge:	Existing measurement scales require extensive manual labor and require extensive validation and validation.
Approach:	They propose a multi-agent framework that automates scale development by leveraging collaborative AI agents.
Outcome:	The proposed framework automates scale development while maintaining rigorous quality standards.

Enhancing Learning-Based Binary Code Similarity Detection Model through Adversarial Training with Multiple Function Variants (2024.findings-emnlp)

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Lichen Jia, Chenggang Wu, Bowen Tang, Peihua Zhang, Zihan Jiang, Yang Yang, Ning Liu, Jingfeng Zhang, Zhe Wang

OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis (2025.acl-long)

Challenge:	Existing Learning-Based Binary Code Similarity Detection (LB-BCSD) methods exhibit lower accuracy in recognizing functions with the same functionality but different implementations.
Approach:	They propose a gradient-guided adversarial attack method based on critical code called FuncFooler which perturbs critical code to generate multiple variants of the same function.
Outcome:	The proposed method increases the accuracy of the current Learning-Based Binary Code Similarity Detection (LB-BCSD) model by 5%-7%.

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Qiushi Sun, Kanzhi Cheng, Zichen Ding, Chuanyang Jin, Yian Wang, Fangzhi Xu, Zhenyu Wu, Chengyou Jia, Liheng Chen, Zhoumianze Liu, Ben Kao, Guohao Li, Junxian He, Yu Qiao, Zhiyong Wu

Rapid Diffusion: Building Domain-Specific Text-to-Image Synthesizers with Fast Inference Speed (2023.acl-industry)

Challenge:	Graphical User Interface (GUI) agents powered by Vision-Language Models (VLMs) have demonstrated human-like computer control capability.
Approach:	They propose a GUI data synthesis pipeline that reverse engineers GUI trajectory construction process by executing pre-defined tasks.
Outcome:	The proposed GUI data synthesis pipeline overcomes the bottlenecks of previous methods that rely on pre-defined tasks and limited data diversity.

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Bingyan Liu, Weifeng Lin, Zhongjie Duan, Chengyu Wang, Wu Ziheng, Zhang Zipeng, Kui Jia, Lianwen Jin, Cen Chen, Jun Huang

LegalAgentBench: Evaluating LLM Agents in Legal Domain (2025.acl-long)

Challenge:	Text-to-Image Synthesis (TIS) aims to generate images based on textual inputs . but, current diffusion-based models lack entity knowledge and low inference speed .
Approach:	They propose a framework for training and deploying latent diffusion models with rich entity knowledge injected and optimized networks.
Outcome:	The proposed framework improves image quality and inference speed and can be used in industrial applications.

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Haitao Li, Junjie Chen, Jingli Yang, Qingyao Ai, Wei Jia, Youfeng Liu, Kai Lin, Yueyue Wu, Guozhi Yuan, Yiran Hu, Wuyue Wang, Yiqun Liu, Minlie Huang

Challenge:	Existing general-domain benchmarks do not capture complexity of real-world judicial cognition and decision-making.
Approach:	They propose a benchmark specifically designed to evaluate LLM Agents in the legal domain.
Outcome:	The proposed benchmark includes 17 corpora from real-world legal scenarios and provides 37 tools for interacting with external knowledge.

The Agent’s First Day: Benchmarking Learning, Exploration, and Scheduling in the Workplace Scenarios (2026.findings-acl)

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Daocheng Fu, Jianbiao Mei, Rong Wu, Xuemeng Yang, Jia Xu, Ding Wang, Pinlong Cai, Yong Liu, Licheng Wen, Botian Shi

Challenge:	Existing research mainly focuses on performance upper bounds in static environments, overlooking stochastic real-world deployment.
Approach:	They propose a dynamic evaluation environment that simulates a "trainee" agent continuously exploring a novel setting.
Outcome:	The proposed model evaluates agents in a dynamic evaluation environment that simulates a "trainee" agent continuously exploring a novel setting.

Unlearning Backdoor Attacks for LLMs with Weak-to-Strong Knowledge Distillation (2025.findings-acl)

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Shuai Zhao, Xiaobao Wu, Cong-Duy T Nguyen, Yanhao Jia, Meihuizi Jia, Feng Yichao, Anh Tuan Luu

SearchGym: Bootstrapping Real-World Search Agents via Cost-Effective and High-Fidelity Environment Simulation (2026.acl-long)

Challenge:	Parameter-efficient fine-tuning (PEFT) can bridge the gap between large language models and downstream tasks, but is vulnerable to malicious attacks.
Approach:	They propose a weak-to-strong unlearning algorithm based on feature alignment knowledge distillation to defend against backdoor attacks . they first train a small-scale language model through full-parameter fine-tuning to serve as the clean teacher model and then guide the large-scale poisoned student model in unlearning the backdoor.
Outcome:	The proposed method can unlearn backdoor features without compromising model performance.

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Xichen Zhang, Ziyi He, Yinghao Zhu, Sitong Wu, Shaozuo Yu, Meng Chu, Wenhu Zhang, Haoru Tan, Jiaya Jia

Information Retrieval

Challenge:	Search agents are a pivotal paradigm for solving open-ended, knowledge-intensive reasoning tasks.
Approach:	They propose a search agent simulation environment that bootstraps robust search agents using Reinforcement Learning.
Outcome:	The proposed model outperforms the web-enhanced ASearcher model by 10.6%.

TRAC: Teacher-Guided Token Reward with Adaptive Calibration for Robust Policy Optimization (2026.acl-long)

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Sitong Wu, Haoru Tan, Xichen Zhang, Bin Xia, Wenhu Zhang, Xiaojuan Qi, Bei Yu, Jiaya Jia

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

Challenge:	Current reward models for reinforcement learning (RL) rely on outcome rewards that propagate a single scalar value across all tokens based on final correctness.
Approach:	They propose a framework that derives dense token-level supervision from LLMs . they use a multi-granularity calibration mechanism to modulate teacher influence .
Outcome:	The proposed framework evaluates teacher reliability across problem-level expertise, trajectory-level discrimination, and token-level confidence.

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

Natural Language Interfaces

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

In-Context Compositional Generalization for Large Vision-Language Models (2024.emnlp-main)

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Chuanhao Li, Chenchen Jing, Zhen Li, Mingliang Zhai, Yuwei Wu, Yunde Jia

Adversarial Attacks Against Automated Fact-Checking: A Survey (2025.emnlp-main)

Challenge:	Recent work shows that in-context learning for large language models exhibits compositional generalization capacity.
Approach:	They propose a method to exhibit in-context compositional generalization in large vision-language models by combining visual and linguistic modalities.
Outcome:	The proposed method reduces redundancy and complexity in in-context learning with LVLMs.

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Fanzhen Liu, Sharif Abuadbba, Kristen Moore, Surya Nepal, Cecile Paris, Jia Wu, Jian Yang, Quan Z. Sheng

ControlMath: Controllable Data Generation Promotes Math Generalist Models (2024.emnlp-main)

Challenge:	Existing fact-checking systems are vulnerable to adversarial attacks that manipulate or generate claims, evidence, or claim-evidence pairs.
Approach:	They examine the impact of adversarial attacks on existing AFC systems and examine their impact on existing ones.
Outcome:	The findings highlight the need for resilient fact-checking frameworks in limiting misinformation spread and supporting public trust.

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Nuo Chen, Ning Wu, Jianhui Chang, Linjun Shou, Jia Li

Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations (2024.findings-emnlp)

Challenge:	Currently, mathematical reasoning is one of the most challenging areas for closed-source LLMs.
Approach:	They propose an iterative method involving an equation-generator module and two LLM-based agents that generate diverse equations and transform them into math word problems.
Outcome:	The proposed method enables the generation of diverse math problems, not limited to specific domains or distributions.

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Nuo Chen, Zinan Zheng, Ning Wu, Ming Gong, Dongmei Zhang, Jia Li

MultiDx: A Multi-Source Knowledge Integration Framework towards Diagnostic Reasoning (2026.findings-acl)

Challenge:	Existing research focuses on developing powerful large language models for mathematical reasoning within monolingual languages.
Approach:	They propose to use translation to build powerful multilingual math reasoning models . they propose different training strategies to build xMR LLMs that outperform open-source LLM .
Outcome:	The proposed model outperforms open-source LLMs and surpasses ChatGPT in few-shot scenarios.

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Yimin Deng, Zhenxi Lin, Yejing Wang, Guoshuai Zhao, Pengyue Jia, Zichuan Fu, Derong Xu, Yefeng Zheng, Xiangyu Zhao, Li Zhu, Xian Wu, Xueming Qian

Explicit and Implicit Data Augmentation for Social Event Detection (2025.acl-long)

Challenge:	Existing approaches focus on diagnostic reasoning based on internal model knowledge or static knowledge bases.
Approach:	They propose a two-stage diagnostic reasoning framework that integrates multi-perspective evidence to generate a diagnostic prediction.
Outcome:	The proposed method generates suspected diagnoses and reasoning traces from web search, SOAP-formatted case, and clinical case database.

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Congbo Ma, Yuxia Wang, Jia Wu, Jian Yang, Jing Du, Zitai Qiu, Qing Li, Hu Wang, Preslav Nakov

SafeEraser: Enhancing Safety in Multimodal Large Language Models through Multimodal Machine Unlearning (2025.findings-acl)

Challenge:	Social event detection relies on labeled data, but annotation is costly and labor-intensive.
Approach:	They propose a plug-and-play dual augmentation framework that combines explicit text-based and implicit feature-space augmentation to enhance data diversity and model robustness.
Outcome:	The proposed framework outperforms the best baseline model by 17.67% on the Twitter2012 dataset and 15.57% on the twitter2018 dataset in terms of the average F1 score.

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Junkai Chen, Zhijie Deng, Kening Zheng, Yibo Yan, Shuliang Liu, PeiJun Wu, Peijie Jiang, Jia Liu, Xuming Hu

Asking Questions Like Educational Experts: Automatically Generating Question-Answer Pairs on Real-World Examination Data (2021.emnlp-main)

Challenge:	Existing methods for MU forget quality and model utility are not fully explored for safety in MLLMs.
Approach:	They propose a safety unlearning benchmark for MLLMs to measure over-forgetting . they propose MU methods to forget quality and model utility .
Outcome:	The proposed method reduces over-forgetting by 79.5% while maintaining forget quality and model utility.

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Fanyi Qu, Xin Jia, Yunfang Wu

Challenge:	Existing approaches to generate high quality question-answer pairs are limited . a new framework is proposed for the question-answer generation task on real-world examination data.
Approach:	They propose a multi-agent communication model to generate and optimize the question and keyphrases iteratively and then apply the generated question and keys to guide the generation of answers.
Outcome:	The proposed framework makes great breakthroughs in the question-answer pair generation task.

KG-FLIP: Knowledge-guided Fashion-domain Language-Image Pre-training for E-commerce (2023.acl-industry)

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Qinjin Jia, Yang Liu, Daoping Wu, Shaoyuan Xu, Huidong Liu, Jinmiao Fu, Roland Vollgraf, Bryan Wang

RCBSF: A Multi-Agent Framework for Automated Contract Revision via Stackelberg Game (2026.findings-acl)

Challenge:	Various visionlanguage pre-training (VLP) models learn cross-modal alignment from large-scale well-aligned image-text datasets without leveraging external knowledge.
Approach:	They propose a knowledge-guided fashion-domain language-image pre-training framework that learns fine-grained representations in e-commerce domain and utilizes external knowledge to improve the pre-train efficiency.
Outcome:	The proposed framework outperforms state-of-the-art models on Amazon and Fashion-Gen datasets by large margins.

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Shijia Xu, Yu Wang, Xiaolong Jia, Zhou Wu, Kai Liu, April Xiaowen Dong

Challenge:	despite the adoption of Large Language Models (LLMs), contract revision remains impeded because generic models treat strict legal constraints as mere suggestions.
Approach:	They propose a risk-constrained bilevel Stackelberg framework that models high-stakes revision as a strategic interaction rather than an open-ended conversation.
Outcome:	The proposed framework achieves state-of-the-art performance with an average RRR of 84.21% and enhanced token efficiency.

Beyond the Individual: Virtualizing Multi-Disciplinary Reasoning for Clinical Intake via Collaborative Agents (2026.findings-acl)

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Huangwei Chen, Wu Li, Junhao Jia, Yining Chen, Xiaotao Pang, Ya-Long Chen, Li Gonghui, Haishuai Wang, Jiajun Bu, Lei Wu

Weights-Rotated Preference Optimization for Large Language Models (2025.emnlp-main)

Challenge:	Initial outpatient consultations are costly and difficult to scale to real-time intake.
Approach:	They propose a synchronous virtual MDT framework that formalizes the consultation state using a structured SOAP representation, separating evidence collection from diagnostic reasoning to improve traceability and bias control.
Outcome:	The proposed framework outperforms state-of-the-art models on ClinicalBench and a real-world RAPID-IPN dataset in documentation quality and consultation capability.

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Chenxu Yang, Ruipeng Jia, Mingyu Zheng, Naibin Gu, Zheng Lin, Siyuan Chen, Weichong Yin, Hua Wu, Weiping Wang

AudioChatLlama: Towards General-Purpose Speech Abilities for LLMs (2024.naacl-long)

Challenge:	Existing methods to align large language models with high reward hacking are limited by the complexity of the parameter space and the complexity.
Approach:	They propose a weights-rotated preference optimization algorithm that constrains the output layer logits with the KL divergence inherited from DPO and fine-tunes the intermediate hidden states.
Outcome:	The proposed algorithm achieves a 3.27-point improvement on AlpacaEval 2 and surpasses the best baseline by 6.2 to 7.5 points on MT-Bench with merely 0.015% of the trainable parameters.

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Yassir Fathullah, Chunyang Wu, Egor Lakomkin, Ke Li, Junteng Jia, Yuan Shangguan, Jay Mahadeokar, Ozlem Kalinli, Christian Fuegen, Mike Seltzer

Self-Correcting RAG: Enhancing Faithfulness via MMKP Context Selection and NLI-Guided MCTS (2026.findings-acl)

Challenge:	a new model for speech processing and reasoning uses curated data instead of text.
Approach:	They extend the instruction-tuned Llama-2 model with end-to-end speech processing and reasoning abilities without using any carefully curated paired data.
Outcome:	The proposed model outperforms or outperfects existing models on synthesized and recorded speech QA tests.

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Shijia Xu, Zhou Wu, Xiaolong Jia, Yu Wang, Kai Liu, April Xiaowen Dong

A Semantic Uncertainty Sampling Strategy for Back-Translation in Low-Resources Neural Machine Translation (2025.acl-srw)

Challenge:	Existing approaches to retrieval-augmented generation still face problems with low context utilization and frequent hallucinations.
Approach:	They propose a framework that reformulates retrieval and generation as constrained optimization and path planning.
Outcome:	The proposed framework significantly improves reasoning accuracy on complex queries while reducing hallucinations.

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Yepai Jia, Yatu Ji, Xiang Xue, Shilei@imufe.edu.cn Shilei@imufe.edu.cn, Qing-Dao-Er-Ji Ren, Nier Wu, Na Liu, Chen Zhao, Fu Liu

Multilinguality

Challenge:	Back-translation methods rely on large-scale parallel corpora to enhance performance, but ignore the semantic quality of monolingual data.
Approach:	They propose a method which prioritizes sentences with higher semantic uncertainty as training samples by computationally evaluating the complexity of unannotated monolingual data.
Outcome:	The proposed method improves translation accuracy and fluency by +1.7 on all three translation tasks.

FORTAP: Using Formulas for Numerical-Reasoning-Aware Table Pretraining (2022.acl-long)

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Zhoujun Cheng, Haoyu Dong, Ran Jia, Pengfei Wu, Shi Han, Fan Cheng, Dongmei Zhang

Look Both Ways and No Sink: Converting LLMs into Text Encoders without Training (2025.acl-long)

Challenge:	Tables store rich numerical data, but numerical reasoning over tables is still a challenge.
Approach:	They propose a spreadsheet formula is a valuable supervision for numerical reasoning in tables.
Outcome:	The proposed method outperforms state-of-the-art methods on three representative datasets of formula prediction, question answering, and cell type classification.

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Ziyong Lin, Haoyi Wu, Shu Wang, Kewei Tu, Zilong Zheng, Zixia Jia

AgentStore: Scalable Integration of Heterogeneous Agents As Specialized Generalist Computer Assistant (2025.findings-acl)

Challenge:	Existing methods for converting large language models into powerful text encoders require extensive training on large datasets.
Approach:	They propose a training-free approach that enables bidirectional attention and suppresses the attention sink phenomenon, resulting in superior performance.
Outcome:	The proposed approach enables bidirectional attention and suppresses the attention sink phenomenon, resulting in superior performance.

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Chengyou Jia, Minnan Luo, Zhuohang Dang, Qiushi Sun, Fangzhi Xu, Junlin Hu, Tianbao Xie, Zhiyong Wu

Challenge:	Existing agents lack generalization and specialization capabilities for open-ended tasks . specialized generalists are often underdeveloped in real-world environments .
Approach:	They propose a platform to dynamically integrate heterogeneous agents for automating computer tasks . they propose specialized generalist agent MetaAgent with the AgentToken strategy .
Outcome:	The proposed platform expands capabilities of existing agents in generalization and specialization . it can be used to automate open-ended tasks in real-world environments .

ARNOR: Attention Regularization based Noise Reduction for Distant Supervision Relation Classification (P19-1)

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Wei Jia, Dai Dai, Xinyan Xiao, Hua Wu

Explainability & Interpretability in NLP

Challenge:	Distant supervision is used for relation classification but it introduces noisy labels . a novel approach to distant supervision relation classification is proposed .
Approach:	They propose a framework for distant supervision relation classification using attention regularization and attention regularizing . they assume that a trustable relation label should be explained by the neural attention model .
Outcome:	The proposed framework improves on the NYT data and noise reduction effect over state-of-the-art methods.

JailMeter: An Evidence-Based Evaluation Framework for Jailbreak Attacks on Large Language Models (2026.findings-acl)

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Qingjia Huang, Jingyu Zhang, Jianguo Wu, Yakai Li, Weijuan Zhang, Yankai Rong, Junyi Yao, Shengzhi Zhang, Xiaoqi Jia

Golden Touchstone: A Comprehensive Bilingual Benchmark for Evaluating Financial Large Language Models (2025.findings-emnlp)

Challenge:	Currently, evaluation criteria and methods used for jailbreak effectiveness are inconsistent.
Approach:	They propose a framework to measure jailbreak effectiveness using a model that filters out jailbreak noise while preserving the original malicious question.
Outcome:	The proposed framework outperforms existing evaluation methods on a challenging benchmark containing 330 human-labeled, non-rejected jailbreak instances.

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Xiaojun Wu, Junxi Liu, Huan-Yi Su, Zhouchi Lin, Yiyan Qi, Chengjin Xu, Jiajun Su, Jiajie Zhong, Fuwei Wang, Saizhuo Wang, Fengrui Hua, Jia Li, Jian Guo

CPRM: A LLM-based Continual Pre-training Framework for Relevance Modeling in Commercial Search (2025.naacl-industry)

Challenge:	Existing financial benchmarks suffer from limited language and task coverage, low-quality datasets, and inadequate adaptability for LLM evaluation.
Approach:	They propose a bilingual benchmark for financial LLMs that assesses models’ language understanding and generation capabilities.
Outcome:	The proposed bilingual benchmark assesses models’ language understanding and generation capabilities.

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Kaixin Wu, Yixin Ji, Zeyuan Chen, Qiang Wang, Cunxiang Wang, Hong Liu, Baijun Ji, Xu Jia, Zhongyi Liu, Jinjie Gu, Yuan Zhou, Linjian Mo

LIME: Less Is More for MLLM Evaluation (2025.findings-acl)

Challenge:	Relevance modeling between queries and items is a key component of commercial search engines.
Approach:	They propose a framework for continual pre-training of LLMs to enhance domain knowledge . they employ queries and multi-field item to jointly pre-train for enhancing domain knowledge.
Outcome:	The proposed model achieves convincing performance compared to strong baselines.

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King Zhu, Qianbo Zang, Shian Jia, Siwei Wu, Feiteng Fang, Yizhi Li, Shuyue Guo, Tianyu Zheng, Jiawei Guo, Bo Li, Haoning Wu, Xingwei Qu, Jian Yang, Ruibo Liu, Xiang Yue, Jiaheng Liu, Chenghua Lin, Hamid Alinejad-Rokny, Min Yang, Shiwen Ni, Wenhao Huang, Ge Zhang

Revealing Procedural Reasoning Structures in Chain-of-Thought Training via Span-Level Gradient Organization (2026.acl-long)

Challenge:	Existing MLLM benchmarks and unified evaluation frameworks cannot accurately and efficiently reflect the ability of MLMLs.
Approach:	They propose a semi-automated benchmark curated using a pipeline that filters out uninformative samples and eliminates answer leakage by focusing on tasks that require image-based understanding.
Outcome:	The proposed benchmark reduces the number of samples by 76% and evaluation time by 77% while it can more effectively distinguish different models’ abilities.

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Jia Liu, Jiaxin Luo, Weiwen Xu, Jonathan M. Garibaldi, Xiao-Kun Wu, Yixue Hao, Min Chen

MulVul: Retrieval-augmented Multi-Agent Code Vulnerability Detection via Cross-Model Prompt Evolution (2026.acl-long)

Challenge:	Chain-of-Thought (CoT) prompts elicit multi-step reasoning, yet how reasoning related structure is expressed during training remains poorly understood.
Approach:	They propose a framework that tracks span-level gradients during fine-tuning on reasoning benchmarks to understand how models develop structured, step-by-step reasoning capabilities.
Outcome:	The proposed framework tracks span-level gradients during fine-tuning on reasoning benchmarks to understand how models develop structured, step-by-step reasoning capabilities.

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Zihan Wu, Jie Xu, Yun Peng, Chun Yong Chong, Xiaohua Jia

SCOUT: Selective Coupling via Optimal Unbalanced Transport for Interpretable Text Classification (2026.acl-long)

Challenge:	Large Language Models (LLMs) struggle to automate real-world vulnerability detection due to the heterogeneity of vulnerability patterns and manual prompt engineering for massive weakness categories is unscalable.
Approach:	They propose a retrieval-augmented multi-agent framework for precise and broad-coverage vulnerability detection using a coarse-to-fine strategy.
Outcome:	The proposed framework outperforms the baseline model on 130 CWE types and achieves 34.79% Macro-F1 performance.

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Junhao Jia, Hanwen Zheng, Yueyi Wu, Huangwei Chen, Haishuai Wang, Jiajun Bu, Lei Wu

Explainability & Interpretability in NLP

Challenge:	Standard interpretable models often rely on scalar similarities that obscure the true evidentiary basis of a prediction.
Approach:	They propose a new paradigm that grounds prototype reasoning in the selective correspondence of discriminative fragments.
Outcome:	The proposed model outperforms rationale extraction and post-hoc attribution methods on seven benchmarks.

Hi-ToM: A Benchmark for Evaluating Higher-Order Theory of Mind Reasoning in Large Language Models (2023.findings-emnlp)

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Yufan Wu, Yinghui He, Yilin Jia, Rada Mihalcea, Yulong Chen, Naihao Deng