Papers by Yujun Zhang
SceMQA: A Scientific College Entrance Level Multimodal Question Answering Benchmark (2024.acl-short)
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Zhenwen Liang, Kehan Guo, Gang Liu, Taicheng Guo, Yujun Zhou, Tianyu Yang, Jiajun Jiao, Renjie Pi, Jipeng Zhang, Xiangliang Zhang
| Challenge: | SceMQA focuses on core science subjects including Mathematics, Physics, Chemistry, and Biology. |
| Approach: | They propose to use SceMQA to evaluate multimodal question answering at college entrance level. |
| Outcome: | The proposed model provides specific knowledge points for each problem and detailed explanations for each answer. |
Too Correct to Learn: Reinforcement Learning on Saturated Reasoning Data (2026.acl-short)
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| Challenge: | Strong base models saturate benchmarks, resulting in weaker performance, a paradox . a new approach to Reinforcement Learning (RL) is needed to improve performance . |
| Approach: | They propose a method that uses constrained uniform top-k sampling to flatten the local optimization landscape by sampling uniformly from constrained high-confidence candidates. |
| Outcome: | Experiments show that the proposed approach prevents policy degeneration and boosts out-of-domain generalization. |
Beyond Single-Value Metrics: Evaluating and Enhancing LLM Unlearning with Cognitive Diagnosis (2025.findings-acl)
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Yicheng Lang, Kehan Guo, Yue Huang, Yujun Zhou, Haomin Zhuang, Tianyu Yang, Yao Su, Xiangliang Zhang
| Challenge: | Large Language Models (LLMs) have been used to remove harmful knowledge and undesirable capabilities. |
| Approach: | They propose a framework that leverages Cognitive Diagnosis Modeling to evaluate LLM unlearning. |
| Outcome: | The proposed framework enhances evaluation and facilitates removal of harmful abilities. |
A Structure-Aware Argument Encoder for Literature Discourse Analysis (2022.coling-1)
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Yinzi Li, Wei Chen, Zhongyu Wei, Yujun Huang, Chujun Wang, Siyuan Wang, Qi Zhang, Xuanjing Huang, Libo Wu
| Challenge: | Existing research for argument representation learning treats tokens in sentences equally and ignores the implied structure information of argumentative context. |
| Approach: | They propose to separate tokens into two groups to capture structural information of arguments and to incorporate paragraph-level position information into the model. |
| Outcome: | The proposed model captures structural information of arguments and is able to identify arguments automatically. |
Dynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models (2026.acl-long)
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| Challenge: | Diffusion large language models (dLLMs) offer bidirectional attention and parallel generation . fixed anchors can enforce constraints, but they often impose rigid spans, leading to truncated reasoning . |
| Approach: | They propose a method that dynamically estimates end-anchor positions to adjust generation length before iterative infilling. |
| Outcome: | The proposed method improves format compliance and answer accuracy on GSM8K and MATH. |
PolicyLLM: Towards Excellent Comprehension of Public Policy for Large Language Models (2026.findings-acl)
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Han Bao, Penghao Zhang, Yue Huang, Zhengqing Yuan, Yanchi Ru, SU Rui, Yujun Zhou, Xiangqi Wang, Kehan Guo, Nitesh V Chawla, Yanfang Ye, Xiangliang Zhang
| Challenge: | Large Language Models (LLMs) are increasingly integrated into real-world decision-making, but their ability to comprehend and reason about policy-related content remains underexplored. |
| Approach: | They propose a bilingual benchmark evaluating policy comprehension comprising 21K cases across a broad spectrum of policy areas. |
| Outcome: | The proposed model shows stronger performance on application-oriented policy tasks than on memorization or conceptual understanding, and yields the highest accuracy on structured reasoning tasks. |
Judging with Many Minds: Do More Perspectives Mean Less Prejudice? On Bias Amplification and Resistance in Multi-Agent Based LLM-as-Judge (2025.findings-emnlp)
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Chiyu Ma, Enpei Zhang, Yilun Zhao, Wenjun Liu, Yaning Jia, Peijun Qing, Lin Shi, Arman Cohan, Yujun Yan, Soroush Vosoughi
| Challenge: | LLM-as-Judge frameworks provide scalable alternative to human evaluation . but the question of how intrinsic biases manifest in these settings remains unexplored . |
| Approach: | They conduct systematic analysis of four bias types in multi-agent LLM-as-Judge frameworks . they find debate framework amplifies biases sharply after initial debate . |
| Outcome: | The proposed frameworks amplify biases after debate and show they are stronger in meta-judge scenarios. |
Dissecting Logical Reasoning in LLMs: A Fine-Grained Evaluation and Supervision Study (2025.findings-emnlp)
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Yujun Zhou, Jiayi Ye, Zipeng Ling, Yufei Han, Yue Huang, Haomin Zhuang, Zhenwen Liang, Kehan Guo, Taicheng Guo, Xiangqi Wang, Xiangliang Zhang
| Challenge: | Existing benchmarks that rely on final-answer accuracy fail to capture the quality of the reasoning process. |
| Approach: | They propose a fine-grained evaluation framework that assesses logical reasoning across three dimensions: overall accuracy, stepwise soundness, and representation-level probing. |
| Outcome: | The proposed framework assesses logical reasoning across three dimensions: overall accuracy, stepwise soundness, and representation-level probing. |
Defending Jailbreak Prompts via In-Context Adversarial Game (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) demonstrate remarkable capabilities across diverse applications, but concerns regarding their security persist. |
| Approach: | They propose an adversarial game that leverages agent learning to extend knowledge to defend against jailbreaks. |
| Outcome: | The proposed game shows that LLMs safeguarded by ICAG exhibit significantly reduced jailbreak success rates across various attack scenarios. |
Graph-Assisted Large Language Models: A Perspective on Mitigating Intrinsic Limitations (2026.findings-acl)
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Haitong Luo, Fali Wang, Weiyao Zhang, Xianren Zhang, Zhiwei Zhang, Tianxiang Zhao, Minhua Lin, Jiahao Zhang, Hui Liu, Xianfeng Tang, Qi He, Suhang Wang, Xuying Meng, Yujun Zhang
| Challenge: | Large language models exhibit intrinsic limitations such as knowledge cutoff, single-threaded reasoning that hinders finer-grained branch and aggregation, and rigid collaboration mechanisms that struggle to coordinate specialized capabilities. |
| Approach: | They propose a taxonomy spanning *Graph-Assisted Knowledge Augmentation*, *Graph Assisted Reasoning and Planning*, and *Graphed LLM Collaboration*. |
| Outcome: | The proposed models show that graphs can augment and correct LLMs and support dynamic coordination among experts and agents in collaborative settings. |
VideoStir: Understanding Long Videos via Spatio-Temporally Structured and Intent-Aware RAG (2026.acl-long)
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| Challenge: | Existing methods for retrieval-augmented generation (RAG) to long videos are limited by limited context windows and flatten videos into independent segments. |
| Approach: | They propose a structured and intent-aware long-video RAG framework that structures a video as a spatio-temporal graph and then performs multi-hop retrieval to aggregate evidence across distant yet contextually related events. |
| Outcome: | The proposed framework is competitive with state-of-the-art baselines without auxiliary information. |