Papers by Jingyuan Li

Chart-MRAG: Benchmarking Multimodal Retrieval Augmented Generation on Chart-based Documents (2026.acl-long)

Challenge:	Existing domain adaptation rumor detection methods ignore the data generalization differences and rely on a large amount of unlabeled target domain samples to achieve domain adaptation.
Approach:	They propose a Gradient Coherence guided Meta-Learning approach for emerging topics rumor detection that selectively learns more "generalizable" tasks that are more beneficial in adapting to the target domain.
Outcome:	The proposed method outperforms baselines on real-world datasets and significantly outperformed traditional methods on the in-domain condition.

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null Ymyang, Jiang Zhong, Li Jin, Xiao Sun, Jingwang Huang, null Gaojinpeng, Qing Liu, Yang Bai, Jingyuan Zhang, Rui Jiang, Qin Lei, Kaiwen Wei

Reasoning as Gradient: Scaling MLE Agents Beyond Tree Search (2026.findings-acl)

Challenge:	Existing benchmarks focus on simple image-text interactions, overlooking complex visual formats like charts.
Approach:	They propose a semi-automatic framework for generating evaluation samples through multi-modal keypoint extraction, knowledge graph construction, and qa pair synthesis.
Outcome:	The proposed framework generates 4,738 question-answering pairs across 8 domains from real-world documents.

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Yifei Zhang, Xu Yang, Xiao Yang, Bowen Xian, Qizheng Li, Shikai Fang, Jingyuan Li, Jian Wang, Minrui Xu, Yuge Zhang, Weiqing Liu, Jiang Bian

Out-of-Distribution Detection via LLM-Guided Outlier Generation for Text-attributed Graph (2025.findings-acl)

Challenge:	LLM-based agents for machine learning engineering rely on tree search to rank candidates.
Approach:	They propose an LLM-based agent that operationalizes gradient-based optimization.
Outcome:	The proposed agent achieves a state-of-the-art 35.1% any-medal rate on MLE-Bench with a limited budget on a single GPU.

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Xiangwei Lv, Mengze Li, Jingyuan Chen, Zhiang Dong, Sirui Han, Beishui Liao

ShieldLM: Empowering LLMs as Aligned, Customizable and Explainable Safety Detectors (2024.findings-emnlp)

Challenge:	Text-Attributed Graphs (TAGs) are widely used in the real world.
Approach:	They propose to use Large Language Models to generate OOD-nodes with high quality . they also use LLMs to integrate existing nodes with LLM-generated edges .
Outcome:	The proposed method performs well on samples outside the In-Distribution (ID) data, but it is difficult to obtain high-quality OOD samples in the real world.

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Zhexin Zhang, Yida Lu, Jingyuan Ma, Di Zhang, Rui Li, Pei Ke, Hao Sun, Lei Sha, Zhifang Sui, Hongning Wang, Minlie Huang

Tailoring Diagnostic Modeling to Individual Learners: Personalized Distractor Generation via MCTS-Guided Reasoning Reconstruction (2026.acl-long)

Challenge:	Existing tools for detecting safety issues in LLMs are expensive and inefficient.
Approach:	They propose an LLM-based safety detector which annotates the safety of queries and provides explanations for its decisions.
Outcome:	The proposed detector outperforms baselines on four sets of query-response pairs and is effective as a safety evaluator for advanced LLMs.

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Tao Wu, Jingyuan Chen, Wang Lin, Jian Zhan, Mengze Li, Fangzhou Jin, Min Zhang, Kun Kuang, Fei Wu

Natural Language Interfaces

Challenge:	Current distractor generation methods produce shared distractors for all students, ignoring individual variations in reasoning, which limits their diagnostic effectiveness.
Approach:	They propose a method which tailors distractors to each student’s specific cognitive flaws, inferred from their past question-answering (QA) history.
Outcome:	The proposed framework outperforms existing methods in generating plausible distractors and adapts to group-level settings.

DMON: A Simple Yet Effective Approach for Argument Structure Learning (2024.lrec-main)

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Sun Wei, Mingxiao Li, Jingyuan Sun, Jesse Davis, Marie-Francine Moens

Reasoning

Challenge:	Argument structure learning (ASL) involves examining relationships between sentences in unstructured text.
Approach:	They propose a dual-tower multi-scale cOnvolution neural network to analyze relationships between arguments in a text.
Outcome:	The proposed approach outperforms state-of-the-art models on three domain argument mining datasets.

SelfBudgeter: Adaptive Token Allocation for Efficient LLM Reasoning (2026.findings-acl)

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Zheng Li, Qingxiu Dong, Jingyuan Ma, Di Zhang, Kai Jia, Zhifang Sui

Reasoning

Challenge:	Recent large reasoning models have shown exceptional performance on various tasks, but they consume excessive tokens even for simple queries, leading to resource waste and prolonged user latency.
Approach:	They propose a self-adaptive reasoning strategy that automatically allocates budgets according to problem complexity and introduces GRPO for reinforcement learning to reduce output length.
Outcome:	The proposed model achieves an average response length compression of 61% on math reasoning tasks while maintaining accuracy.

Guide the Many-to-One Assignment: Open Information Extraction via IoU-aware Optimal Transport (2023.acl-long)

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Kaiwen Wei, Yiran Yang, Li Jin, Xian Sun, Zequn Zhang, Jingyuan Zhang, Xiao Li, Linhao Zhang, Jintao Liu, Guo Zhi

Information Extraction & Text Mining

Challenge:	Open Information Extraction (OIE) aims to extract structured information from text without the limitations of close ontology.
Approach:	They propose a method to assign ground truth labels to parallelly generated tuple proposals . they leverage intersection-over-union (IoU) as assignment quality measurement .
Outcome:	The proposed method outperforms the state-of-the-art models on three benchmarks.

FactVerse: A Benchmark for Factual Consistency in Interleaved Image–Text Generation (2026.acl-long)

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Yubo Shan, Kun Zhang, Qiming Xu, Liping Cao, Yingying Cao, Jian Zhang, Yu Wang, Jingyuan Li, Yuanzhuo Wang

LLM-based Rumor Detection via Influence Guided Sample Selection and Game-based Perspective Analysis (2025.acl-long)

Challenge:	Existing benchmarks lack effective mechanisms to evaluate factual consistency in interleaved image-text generation.
Approach:	They propose a benchmark dedicated to evaluating factual consistency in interleaved image-text generation.
Outcome:	The proposed framework outperforms existing evaluation methods in evaluating factual consistency in interleaved image-text generation.

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Zhiliang Tian, Jingyuan Huang, Zejiang He, Zhen Huang, Menglong Lu, Linbo Qiao, Songzhu Mei, Yijie Wang, Dongsheng Li

Challenge:	Existing methods for rumor detection on social media are limited by limited modeling capacity and insufficient training corpora.
Approach:	They propose an SFT-based rumor detection model with Influence guided Sample selection and Game-based multi-perspective analysis to address these issues.
Outcome:	The proposed model outperforms existing SOTA on three datasets.

P²Net: Parallel Pointer-based Network for Key Information Extraction with Complex Layouts (2025.findings-acl)

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Kaiwen Wei, Jie Yao, Jiang Zhong, Yangyang Kang, Jingyuan Zhang, Changlong Sun, Xin Zhang, Fengmao Lv, Li Jin

Information Extraction & Text Mining

Challenge:	Existing methods for key information extraction are based on a limited set of entity categories and fixed layouts.
Approach:	They propose a large-scale, human-annotated dataset for key information extraction . it is based on a human-annotated layout and 1,162 entity categories . they propose 'parallel pointer-based network' that leverages implicit relationships .
Outcome:	Experiments on widely-used datasets show that the proposed model outperforms state-of-the-art methods while maintaining fast inference speeds.

Integration of Knowledge Graph Embedding Into Topic Modeling with Hierarchical Dirichlet Process (N19-1)

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Dingcheng Li, Siamak Zamani, Jingyuan Zhang, Ping Li

Chain-of-Specificity: Enhancing Task-Specific Constraint Adherence in Large Language Models (2025.coling-main)

Challenge:	Topic models are used to extract topical structures from document-word frequency representations of the text corpus without supervision.
Approach:	They propose a Bayesian nonparametric topic modeling with knowledge graph embedding to employ knowledge graphs to extract more coherent topics.
Outcome:	The proposed model performs better on three public datasets than state-of-the-art models on topic coherence and document classification accuracy.

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Kaiwen Wei, Jiang Zhong, Hongzhi Zhang, Fuzheng Zhang, Di Zhang, Li Jin, Yue Yu, Jingyuan Zhang

A Syntactically Constrained Bidirectional-Asynchronous Approach for Emotional Conversation Generation (D18-1)

Challenge:	Existing approaches to enhancing large language models fail to emphasize specific constraints and unlock the underlying knowledge.
Approach:	They propose a method that emphasizes specific constraints and unlocks knowledge within LLMs by iteratively emphasising on specific constraints.
Outcome:	The proposed method outperforms existing methods in enhancing generated content, especially in terms of specificity.

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Jingyuan Li, Xiao Sun

Dialogue Systems & Conversational Agents

Challenge:	Existing neural language models generate generic responses with poor logic and no emotion.
Approach:	They propose a syntactically constrained bidirectional-asynchronous approach for emotional conversation generation using pre-generated emotion keywords and topic keywords.
Outcome:	The proposed approach improves the diversity of responses and boosts logic and emotion compared with baselines.

Trigger is Not Sufficient: Exploiting Frame-aware Knowledge for Implicit Event Argument Extraction (2021.acl-long)

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Kaiwen Wei, Xian Sun, Zequn Zhang, Jingyuan Zhang, Guo Zhi, Li Jin

Reasoning

Challenge:	Existing methods to extract event arguments focus on learning pair-wise information between arguments and the given trigger.
Approach:	They propose a framework to extract event-related arguments from a given event frame-level scope.
Outcome:	The proposed method achieves state-of-the-art on the RAMS dataset.

Does Acceleration Cause Hidden Instability in Vision Language Models? Uncovering Instance-Level Divergence Through a Large-Scale Empirical Study (2025.emnlp-main)

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Yizheng Sun, Hao Li, Chang Xu, Hongpeng Zhou, Chenghua Lin, Riza Batista-Navarro, Jingyuan Sun

LVPruning: An Effective yet Simple Language-Guided Vision Token Pruning Approach for Multi-modal Large Language Models (2025.findings-naacl)

Challenge:	Current acceleration evaluations focus on minimal overall performance degradation . however, accelerated models can exhibit significant changes in instance-level predictions .
Approach:	They investigate whether accelerated vision-Language Models can still give the same answers as before . they found that accelerated models changed original answers up to 20% of the time .
Outcome:	The results show that accelerated models changed their original answers up to 20% of the time.

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Yizheng Sun, Yanze Xin, Hao Li, Jingyuan Sun, Chenghua Lin, Riza Batista-Navarro

A Survey on In-context Learning (2024.emnlp-main)

Challenge:	Multi-modal Large Language Models (MLLMs) incur significant computational overhead due to the large number of vision tokens processed, limiting their practicality in resource-constrained environments.
Approach:	They propose a language-guided vision token pruning method that can be integrated into existing MLLMs with minimal architectural changes.
Outcome:	The proposed method reduces vision tokens by 90% and preserves model performance.

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Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Baobao Chang, Xu Sun, Lei Li, Zhifang Sui

Low-Resource NLP

Challenge:	In-context learning (ICL) is a new paradigm for natural language processing . large language models (LLMs) demonstrate the ability to learn from a few examples .
Approach:	They propose to explore ICL to evaluate and extrapolate the ability of large language models.
Outcome:	The proposed methods can be used to evaluate and extrapolate the ability of large language models.

Computational Linguistics for Brain Encoding and Decoding: Principles, Practices and Beyond (2024.acl-tutorials)

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Jingyuan Sun, Shaonan Wang, Zijiao Chen, Jixing Li, Marie-Francine Moens

Challenge:	This tutorial will explore the potential of computational linguistics to help understand brain language processing.
Approach:	This tutorial will explore the principles and practices of using computational linguistics methods for brain encoding and decoding.
Outcome:	This tutorial will explore the principles and practices of using computational linguistics methods for brain encoding and decoding.

Enhancing Semantic Consistency of Large Language Models through Model Editing: An Interpretability-Oriented Approach (2024.findings-acl)

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Jingyuan Yang, Dapeng Chen, Yajing Sun, Rongjun Li, Zhiyong Feng, Wei Peng

Contextual Modeling for Document-level ASR Error Correction (2024.lrec-main)

Challenge:	Large Language Models generate inconsistent and sometimes contradictory outputs when presented with a prompt that has equivalent semantics but is expressed differently from the original prompt.
Approach:	They propose to refine a Large Language Model (LLM) with prompt-output pairs with equivalent semantics to achieve semantic consistency.
Outcome:	The proposed method improves the semantic consistency and task performance of LLMs.

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Jin Jiang, Xunjian Yin, Xiaojun Wan, Wei Peng, Rongjun Li, Jingyuan Yang, Yanquan Zhou

Decoding the Multimodal Mind: Generalizable Brain-to-Text Translation via Multimodal Alignment and Adaptive Routing (2026.findings-acl)

Challenge:	Existing work on document-level ASR error correction ignores contextual information . however, there are limited studies on incorporating contextual information into AEC .
Approach:	They propose a context-aware method that retrieves contextual information from a datastore . they use two English and two Chinese datasets to model document-level AEC .
Outcome:	The proposed model can utilize contextual information to improve document-level AEC . the data store containing contextual information provides even better results .

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Chunyu Ye, Yunhao Zhang, Jingyuan Sun, Chong Li, Yang Zhao, Shaonan Wang

LightReasoner: Can Small Language Models Teach Large Language Models Reasoning? (2026.acl-long)

Challenge:	Current approaches to decoding language from the human brain rely on unimodal representations, neglecting the brain’s inherently multimodal processing.
Approach:	They propose a framework that leverages Multimodal Large Language Models to align brain signals with a shared semantic space encompassing text, images, and audio.
Outcome:	The proposed framework achieves an 8.48% improvement on the most commonly used benchmark on fMRI datasets with textual, visual, and auditory stimuli.

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Jingyuan Wang, Yankai Chen, Zhonghang Li, Chao Huang

Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents (2025.emnlp-demos)

Challenge:	Large language models (LLMs) have demonstrated remarkable progress in reasoning, but are resource-intensive and require large curated datasets.
Approach:	They propose a framework that leverages the behavioral divergence between a stronger expert model and a weaker amateur model.
Outcome:	The proposed framework improves accuracy by up to 28.1% while reducing time consumption by 90% and tuning token usage by 99%.

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Ziyang Miao, Qiyu Sun, Jingyuan Wang, Yuchen Gong, Yaowei Zheng, Shiqi Li, Richong Zhang

CFlowPsyD: An Analysis-Enhanced Dataset for Asynchronous Psychological Counseling through Self-Optimizing Multi-Agent Framework (2026.findings-acl)

Challenge:	Existing data synthesis tools struggle to extract reliable fine-tuning data from heterogeneous documents.
Approach:	They propose a framework for synthesizing fine-tuning data from unstructured documents via an intuitive graphical user interface.
Outcome:	The proposed framework can extract reliable data from unstructured documents via an intuitive graphical user interface (GUI) it leverages persona-driven prompting approach to generate diverse question-answer pairs using public-available LLMs.

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Donghao Li, Yifan Deng, Jinta Weng, Xingsheng Zhang, Chenxu Niu, Jingyuan Tian, Heyan Huang, Yue Hu

Dialogue Systems & Conversational Agents

Challenge:	Asynchronous psychological counseling (APC) is a crucial mental health service modality that transcends temporal and spatial constraints.
Approach:	They propose a self-optimizing multi-agent framework for counseling dialogue generation, CFlowPsy, which utilizes real anonymized counseling cases as seed data to synthesize diverse problem-solving-oriented APC conversations through large language models.
Outcome:	The proposed framework synthesizes diverse problem-solving-oriented APC conversations through large language models.

MemRec: Collaborative Memory-Augmented Agentic Recommender System (2026.acl-long)

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Weixin Chen, Yuhan Zhao, Jingyuan Huang, Zihe Ye, Mingxuan Ju, Tong Zhao, Neil Shah, Li Chen, Yongfeng Zhang

Embracing Imperfection: Simulating Students with Diverse Cognitive Levels Using LLM-based Agents (2025.acl-long)

Challenge:	Existing recommender systems rely on semantic user and item memories to make predictions, but these memories are kept in isolation.
Approach:	They propose a framework that architecturally decouples memory management from reasoning to decouple memory management and reasoning from the user and item memories.
Outcome:	The proposed framework decouples memory management from reasoning and achieves state-of-the-art performance on four benchmarks.

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Tao Wu, Jingyuan Chen, Wang Lin, Mengze Li, Yumeng Zhu, Ang Li, Kun Kuang, Fei Wu

Challenge:	Large language models (LLMs) are becoming increasingly popular in education, enabling researchers to simulate students' learning patterns and learning patterns.
Approach:	They propose a training-free framework for student simulation that takes into account student cognitive diversity and realism.
Outcome:	The proposed model outperforms baseline models and achieves 100% improvement in simulation accuracy and realism.

HAUNTATTACK: When Attack Follows Reasoning as a Shadow (2026.findings-acl)

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Jingyuan Ma, Rui Li, Zheng Li, Junfeng Liu, Heming Xia, Lei Sha, Zhifang Sui

The Web Can Be Your Oyster for Improving Language Models (2023.findings-acl)

Challenge:	Emerging Large Reasoning Models (LRMs) excel in mathematical and reasoning tasks, showcasing remarkable capabilities.
Approach:	They propose a framework that embeds harmful instructions into reasoning questions . they evaluate 11 LRMs and observe an average attack success rate of over 70% .
Outcome:	The proposed framework improves reasoning models by 13 percentage points over baseline.

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Junyi Li, Tianyi Tang, Wayne Xin Zhao, Jingyuan Wang, Jian-Yun Nie, Ji-Rong Wen

Be a Multitude to Itself: A Prompt Evolution Framework for Red Teaming (2024.findings-emnlp)

Challenge:	Pretrained language models encode a large amount of knowledge, but knowledge is frozen at the time of training, and the models become static and limited by training data.
Approach:	They propose an adaptive search engine assisted learning method that can self-evaluate the confidence level of PLM’s predictions and adaptively determine when to refer to the web for more data.
Outcome:	The proposed model outperforms retrieval-augmented methods on 16 knowledge-intensive tasks on a wide range of knowledge-related tasks.

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Rui Li, Peiyi Wang, Jingyuan Ma, Di Zhang, Lei Sha, Zhifang Sui

Asclepius: A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Models (2025.acl-long)

Challenge:	Large Language Models (LLMs) have gained increasing attention for their capacity to generate harmful content.
Approach:	They propose a scalable evolution framework to evolve red teaming prompts across breadth and depth dimensions, facilitating automatic generation of numerous high-quality and diverse red team prompts.
Outcome:	The proposed framework surpasses existing red teaming methods on attack success rate and diversity.

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Jie Liu, Wenxuan Wang, Su Yihang, Jingyuan Huang, Yudi Zhang, Cheng-Yi Li, Wenting Chen, Xiaohan Xing, Kao-Jung Chang, Linlin Shen, Michael R. Lyu

VenusFactory: An Integrated System for Protein Engineering with Data Retrieval and Language Model Fine-Tuning (2025.acl-demo)

Challenge:	Medical Multi-Modal Large Language Models (Med-MLLMs) are a promising new form of artificial general intelligence due to their ability to tackle complex tasks.
Approach:	They propose a new benchmark that comprehensively assesses medical multi-modal large language models in terms of distinct medical specialties and different diagnostic capacities.
Outcome:	The proposed model covers 15 medical specialties and different diagnostic capacities, and excludes overlap with existing VQA dataset.

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Yang Tan, Chen Liu, Jingyuan Gao, Wu Banghao, Mingchen Li, Ruilin Wang, Lingrong Zhang, Huiqun Yu, Guisheng Fan, Liang Hong, Bingxin Zhou

Learning Interpretable Relationships between Entities, Relations and Concepts via Bayesian Structure Learning on Open Domain Facts (2020.acl-main)

Challenge:	Pre-trained protein language models have been used in protein engineering, but their adoption is limited due to data collection, task benchmarking, and application challenges.
Approach:	They propose a versatile engine that integrates biological data retrieval, standardized task benchmarking, and modular fine-tuning of PLMs.
Outcome:	The proposed engine integrates biological data retrieval, task benchmarking, and modular fine-tuning of PLMs.

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Jingyuan Zhang, Mingming Sun, Yue Feng, Ping Li

Explainability & Interpretability in NLP

Challenge:	Concept graphs are created as universal taxonomies for text understanding in the open domain knowledge.
Approach:	They propose to learn interpretable relationships from open-domain facts to enrich concept graphs.
Outcome:	The proposed method improves the identification of concepts for entities based on relations between entities on public English and Chinese datasets.

ElitePLM: An Empirical Study on General Language Ability Evaluation of Pretrained Language Models (2022.naacl-main)

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Junyi Li, Tianyi Tang, Zheng Gong, Lixin Yang, Zhuohao Yu, Zhipeng Chen, Jingyuan Wang, Xin Zhao, Ji-Rong Wen