activity
20242026
most citedLegalDuet: Learning Fine-grained Representations for Legal Judgment Prediction via a Dual-View Contrastive Learning

1 citations · 1 across the 10 of their papers we have counts for

collaborators

12 papers

cs.CL2026

LexIssue: Benchmarking Legal Issue Identification in Chinese Civil Litigation

Huiyuan Xie, Yuqin Huang, Zhicheng Hao +4

Identifying the issues disputed between litigating parties is a crucial component of real-world litigation. However, legal issues remain comparatively underexplored in legal AI res…

cs.CL2026

LexKairos: Benchmarking Legal Temporal Capabilities in LLMs

Chenyang Li, Zejia Feng, Yuqin Huang +2

Large language models (LLMs) have demonstrated strong performance across a wide range of legal tasks. In legal practice, time is a critical concept that governs the validity of sta…

cs.CL2026

REFACT: Adaptive Fact Restatement for Compact and Faithful Chain-of-Thought Reasoning

Zhensheng Jin, Xin Dai, Zhenghao Liu +5

Large Language Models (LLMs) increasingly leverage long-form reasoning to solve complex tasks, yet their reasoning processes can deviate from the provided context when evidence is…

cs.CL2026

SHIFT: Gate-Modulated Activation Steering for Knowledge Conflict Mitigation in Retrieval-Augmented Generation

Ruochang Li, Pengcheng Huang, Zhenghao Liu +5

Retrieval-augmented generation (RAG) enhances LLMs by incorporating external knowledge to support response generation. However, conflicts between retrieved context and parametric k…

cs.CL2026

GARL: Game-Theoretic Reinforcement Learning for Multi-Agent Strategic Prioritisation

Yuxiao Ye, Yiwen Zhang, Huiyuan Xie +2

LLM-based multi-agent systems are increasingly used for strategic decision-making tasks. In such settings, performance depends not only on individual model capabilities, but also o…

cs.CL2026

CLASE: A Hybrid Method for Chinese Legalese Stylistic Evaluation

Yiran Rex Ma, Yuxiao Ye, Huiyuan Xie

Legal text generated by large language models (LLMs) can usually achieve reasonable factual accuracy, but it frequently fails to adhere to the specialised stylistic norms and lingu…