Papers by Yufei Zhang
Modalities Should Be Appropriately Leveraged: Uncertainty Guidance for Multimodal Chinese Spelling Correction (2024.lrec-main)
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| Challenge: | Chinese spelling correction (CSC) aims to detect and correct spelling errors in Chinese texts. |
| Approach: | They propose a framework that incorporates uncertainty into feature learning and correction stages . they propose to combine the uncertainty of multimodal features with model learning . |
| Outcome: | The proposed framework improves on three public datasets. |
RubricBench: Aligning Model-Generated Rubrics with Human Standards (2026.acl-long)
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Junyi Zhou, Qiyuan Zhang, Yufei Wang, Fuyuan Lyu, Yidong Ming, Can Xu, Qingfeng Sun, Kai Zheng, Peng Kang, Xue Liu, Chen Ma
| Challenge: | Existing benchmarks lack discriminative complexity and ground-truth rubric annotations required for rigorous evaluation. |
| Approach: | They propose a curated benchmark with 1,147 pairwise comparisons to assess the reliability of rubric-based evaluation. |
| Outcome: | The proposed benchmarks show that they support diverse domains, exhibit discriminative ability, provide high-quality annotations, and include human-authored rubrics. |
Beyond Static Testbeds: An Interaction-Centric Agent Simulation Platform for Dynamic Recommender Systems (2025.emnlp-main)
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| Challenge: | Existing platforms lack a mechanism for user actions to dynamically reshape the environment. |
| Approach: | They propose a novel agent-based simulation platform for recommender systems with a robust interaction mechanism. |
| Outcome: | The proposed platform improves the credibility of the simulation and replicates the Matthew Effect and Brand Loyalty. |
OpenEval: Benchmarking Chinese LLMs across Capability, Alignment and Safety (2024.acl-demos)
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Chuang Liu, Linhao Yu, Jiaxuan Li, Renren Jin, Yufei Huang, Ling Shi, Junhui Zhang, Xinmeng Ji, Tingting Cui, Liutao Liutao, Jinwang Song, Hongying Zan, Sun Li, Deyi Xiong
| Challenge: | a rapid development of Chinese large language models poses big challenges for efficient LLM evaluation. |
| Approach: | They propose an evaluation testbed that benchmarks Chinese LLMs across capability, alignment and safety. |
| Outcome: | The evaluation platform OpenEval benchmarks Chinese LLMs across capability, alignment and safety. |
MoDULA: Mixture of Domain-Specific and Universal LoRA for Multi-Task Learning (2024.emnlp-main)
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Yufei Ma, Zihan Liang, Huangyu Dai, Ben Chen, Dehong Gao, Zhuoran Ran, Wang Zihan, Linbo Jin, Wen Jiang, Guannan Zhang, Xiaoyan Cai, Libin Yang
| Challenge: | Recent advances in open-source Large Language Models (LLMs) have achieved notable successes in natural language processing. |
| Approach: | They propose a Parameter Efficient Fine-Tuning paradigm for improved fine-tuning and parameter efficiency in multi-task learning. |
| Outcome: | The proposed model outperforms existing methods on multi-task learning while reducing training costs by over 80% without losing general capability. |
Harnessing Black-Box Control to Boost Commonsense in LM’s Generation (2023.emnlp-main)
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| Challenge: | Recent years have seen remarkable progress in massively Pre-Trained Language Models such as GPT-3 . however, their generated outputs lack commonsense at times . |
| Approach: | They propose a framework that steers a frozen Pre-Trained Language Model towards more commonsense generation by training an auxiliary model. |
| Outcome: | The proposed framework produces plausible outputs that incorporate concepts in a meaningful way. |
UIOrchestra: Generating High-Fidelity Code from UI Designs with a Multi-agent System (2025.findings-emnlp)
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Chuhuai Yue, Jiajun Chai, Yufei Zhang, Zixiang Ding, Xihao Liang, Peixin Wang, Shihai Chen, Wang Yixuan, null Wangyanping, Guojun Yin, Wei Lin
| Challenge: | Recent advances in large language models have significantly improved automated code generation . however, the translation of complex mobile UI designs into high-fidelity front-end code remains a challenge . |
| Approach: | They propose a collaborative multi-agent system to reconstruct static single-page apps from mockups. |
| Outcome: | The proposed system outperforms existing methods in reconstructing complex app pages . the code and data will be released upon paper acceptance . |
From Individual Excellence to Collective Sustainability: Seeking Strategic Equilibrium in Proactive Multi-Agent Teams (2026.findings-acl)
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| Challenge: | a team of proactive agents suffer from a greedy optimization for immediate task accuracy . a new approach to improve team collaboration is based on the opportunity cost . |
| Approach: | They propose a game-theoretic proactive multi-agent reinforcement learning framework to solve this imbalance . they use a Positive-Unlabeled scorer to anchor intervention quality under sparse supervision . |
| Outcome: | The proposed framework maintains high performance while preventing experts from over-developing. |
Crowd Comparative Reasoning: Unlocking Comprehensive Evaluations for LLM-as-a-Judge (2025.acl-long)
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Qiyuan Zhang, Yufei Wang, Yuxin Jiang, Liangyou Li, Chuhan Wu, Yasheng Wang, Xin Jiang, Lifeng Shang, Ruiming Tang, Fuyuan Lyu, Chen Ma
| Challenge: | Existing methods rely on majority voting or criteria expansion to capture detailed and detailed details, often leading to incomplete outcomes. |
| Approach: | They propose a method which introduces additional crowd responses to compare with the candidate responses, thereby exposing deeper and more comprehensive details within the candidate answers. |
| Outcome: | Experiments show that the proposed method improves evaluation reliability and achieves an average gain of 6.7% across five benchmarks. |
Safety in Large Reasoning Models: A Survey (2025.findings-emnlp)
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Cheng Wang, Yue Liu, Baolong Bi, Duzhen Zhang, Zhong-Zhi Li, Yingwei Ma, Yufei He, Shengju Yu, Xinfeng Li, Junfeng Fang, Jiaheng Zhang, Bryan Hooi
| Challenge: | Large Reasoning Models (LRMs) have a high level of advanced reasoning capabilities, but they are vulnerable and vulnerable. |
| Approach: | This paper presents the first comprehensive survey of Large Reasoning Models . it explores the new safety risks, attacks, and defense strategies specific to LRMs based on reasoning . |
| Outcome: | The proposed study examines the safety and security risks of large reasoning models. |
Complementary Evidence Identification in Open-Domain Question Answering (2021.eacl-main)
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| Challenge: | Existing approaches to QA that only measure the relevance between the question and each paragraph are not effective. |
| Approach: | They propose a method that learns vector representations of passages and models the sufficiency and diversity within the selected set, in addition to the relevance between the question and passages. |
| Outcome: | The proposed method significantly improves the accuracy of complementary evidence selection in open-domain question answering domain. |
SkillVerse : Assessing and Enhancing LLMs with Tree Evaluation (2025.acl-long)
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| Challenge: | Language models evolve to tackle complex, multifaceted tasks, requiring granular evaluations . recent studies have focused on leaderboard and benchmark results, but limited interpretability makes it difficult to compare strengths and weaknesses of models. |
| Approach: | They propose an unsupervised tree-structured diagnosis framework for understanding model proficiency in specific abilities with an LLM as a judge. |
| Outcome: | The proposed framework improves model in-context learning and predicts model weaknesses with a 55% success rate compared to the framework without SkillVerse. |
Beyond Dialogue Time: Temporal Semantic Memory for Personalized LLM Agents (2026.findings-acl)
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Miao Su, Yucan Guo, Zhongni Hou, Long Bai, Zixuan Li, Yufei Zhang, Guojun Yin, Wei Lin, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
| Challenge: | Existing methods focus on point-wise memory, losing durative information that captures persistent states and evolving patterns. |
| Approach: | They propose a memory framework that models semantic time for point-wise memory and supports the construction and utilization of durative memory. |
| Outcome: | Experiments on LongMemEval and LoCoMo show that the proposed method outperforms existing methods and achieves up to 12.2% improvement in accuracy. |
Retrospex: Language Agent Meets Offline Reinforcement Learning Critic (2024.emnlp-main)
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| Challenge: | Existing LLM agent frameworks do not fully utilize past experiences for improvement. |
| Approach: | They propose a LLM-based agent framework called Retrospex that analyzes past experiences in depth to improve existing agent frameworks. |
| Outcome: | The proposed framework analyzes past experiences in ScienceWorld, ALFWorld and Webshop environments, demonstrating its advantages over baselines. |
Diversity Collapse in Multi-Agent LLM Systems: Structural Coupling and Collective Failure in Open-Ended Idea Generation (2026.findings-acl)
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| Challenge: | Multi-agent systems (MAS) are increasingly used for open-ended idea generation . when and why collective interaction expands the solution space remains unclear . |
| Approach: | They propose to study diversity in multi-agent systems across three bottom-up levels: model intelligence, agent cognition, and system dynamics. |
| Outcome: | The proposed model yields diminishing diversity despite higher quality . the proposed model fails to expand diversity and causes it to collapse . |
NILE: Internal Consistency Alignment in Large Language Models (2025.emnlp-main)
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Minda Hu, Qiyuan Zhang, Yufei Wang, Bowei He, Hongru Wang, Jingyan Zhou, Liangyou Li, Yasheng Wang, Chen Ma, Irwin King
| Challenge: | Recent advances show that the world knowledge in the Instruction Fine-Tuning (IFT) dataset, which is incompatible with LLMs’ internal knowledge, can greatly hurt the IFT performance. |
| Approach: | They propose a framework to optimize the effectiveness of IFT by carefully aligning the world and internal knowledge of LLMs. |
| Outcome: | The proposed framework can significantly improve performance across multiple LLM ability evaluation datasets. |
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. |
Beyond Length Scaling: Synergizing Breadth and Depth for Generative Reward Models (2026.findings-acl)
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| Challenge: | Recent advances in Generative Reward Models have demonstrated that scaling the length of Chain-of-Thought reasoning enhances reliability of evaluation. |
| Approach: | They propose a framework that reconfigures raw rationales into structured Breadth-CoT and Depth-Co T through a modular synthesis pipeline. |
| Outcome: | The proposed framework surpasses open-source RMs by an average of 8.2%. |
Com2 : A Causal-Guided Benchmark for Exploring Complex Commonsense Reasoning in Large Language Models (2025.acl-long)
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Kai Xiong, Xiao Ding, Yixin Cao, Yuxiong Yan, Li Du, Yufei Zhang, Jinglong Gao, Jiaqian Liu, Bing Qin, Ting Liu
| Challenge: | Existing works focus on complex tasks like math and code, while complex commonsense reasoning remains underexplored due to its uncertainty and lack of structure. |
| Approach: | They propose to build a benchmark for large language models based on complex commonsense reasoning based upon causal event graphs and causal theory. |
| Outcome: | The proposed benchmark combines a complex commonsense reasoning benchmark with a detective story to achieve a more challenging subset. |
Is Your LLM Outdated? A Deep Look at Temporal Generalization (2025.naacl-long)
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| Challenge: | Existing methods to evaluate large language models are limited due to their inherent dynamic nature and the inherent dynamicity of language and information. |
| Approach: | They introduce a new evaluation framework that employs fresh text and event prediction for assessing LLMs’ temporal adaptability. |
| Outcome: | The proposed framework shows significant temporal biases and a decline in performance over time. |
From General Reward to Targeted Reward: Improving Open-ended Long-context Generation Models (2025.emnlp-main)
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| Challenge: | Current research on long-form context in Large Language Models (LLMs) focuses on understanding of long-contexts, but the open-ended Long Text Generation (Open-LTG) remains underexplored. |
| Approach: | They propose a method that uses data synthesis and a reward signal to enhance model performance. |
| Outcome: | The proposed method outperforms GPT-4-Turbo and improves performance by 20% on the Open-LTG task. |
Rethinking Stateful Tool Use in Multi-Turn Dialogues: Benchmarks and Challenges (2025.findings-acl)
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Hongru Wang, Wenyu Huang, Yufei Wang, Yuanhao Xi, Jianqiao Lu, Huan Zhang, Nan Hu, Zeming Liu, Jeff Z. Pan, Kam-Fai Wong
| Challenge: | Existing benchmarks that assess Language Models (LMs) as Language Agents (LAs) for tool use focus on stateless, single-turn interactions or partial evaluations, overlooking the inherent stateful nature of interactions in multi-turn applications. |
| Approach: | They propose a multi-turn dialogue dataset with stateful tool interactions considering the whole life cycle of tool use across six key tasks in three stages . they also build VirtualMobile – an embodied virtual mobile evaluation environment to simulate API calls and assess the robustness of the created APIs. |
| Outcome: | The proposed dataset evaluates 13 open- and closed-source LLMs and provides detailed analysis at each stage. |
Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments (2026.findings-acl)
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Junjie Ye, Changhao Jiang, Zhengyin Du, Yufei Xu, Xuesong Yao, Zhiheng Xi, Xiaoran Fan, Qi Zhang, Tao Gui, Xuanjing Huang, Jiecao Chen
| Challenge: | Currently, there are no efficient reinforcement learning (RL) frameworks specifically designed for tool use. |
| Approach: | They propose an automated environment construction pipeline that incorporates scenario decomposition, document generation, function integration, complexity scaling, and localized deployment to enable high-quality training environments without external tools. |
| Outcome: | The proposed framework significantly improves the models’ tool-use performance without degrading their general capabilities. |
Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings (2022.acl-long)
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| Challenge: | Contextualized embeddings are expensive and resource-demanding, hence environmentally unfriendly. |
| Approach: | They propose a method to convert contextualized embeddings from pre-trained models into static embeddables using synonym knowledge and weighted vector distribution. |
| Outcome: | The proposed method outperforms baseline embeddings by a large margin through extrinsic and intrinsic tasks. |
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. |
ToolHop: A Query-Driven Benchmark for Evaluating Large Language Models in Multi-Hop Tool Use (2025.acl-long)
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Junjie Ye, Zhengyin Du, Xuesong Yao, Weijian Lin, Yufei Xu, Zehui Chen, Zaiyuan Wang, Sining Zhu, Zhiheng Xi, Siyu Yuan, Tao Gui, Qi Zhang, Xuanjing Huang, Jiecao Chen
| Challenge: | Effective evaluation of multi-hop tool use is critical for analyzing the understanding, reasoning, and function-calling capabilities of large language models. |
| Approach: | They propose a dataset that provides rigorous evaluation of multi-hop tool use. |
| Outcome: | The proposed model achieves 49.04% accuracy across five model families. |
TinyJudge: Unverifiable Constraint Alignment via Lightweight Specialist Ensembles (2026.acl-long)
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Yirong Zeng, Yufei Liu, Xiao Ding, Yutai Hou, Yuxian Wang, Wu Ning, Haonan Song, Dandan Tu, Qixun Zhang, Yuxiang He, Bibo Cai, Ting Liu
| Challenge: | Instruction Following (IF) is a core capability of LLMs, requiring strict adherence to diverse constraints. |
| Approach: | They propose a framework that uses tiny language models to evaluate instruction following . they propose to use a set of specialized tiny language model to provide rewards for soft constraints. |
| Outcome: | The proposed framework outperforms baseline models by 12% and speeds up training time by 3. |