26 citations · 27 across the 8 of their papers we have counts for
5 papers · 1 filter
LegalWorld: A Life-Cycle Interactive Environment for Legal Agents
Songhan Zuo, Shengbin Yue, Tao Chiang +4
Civil litigation is inherently a life-cycle process: what a lawyer drafts on day one constrains what unfolds at trial months later. Yet existing legal benchmarks evaluate isolated…
Multi-Agent Simulator Drives Language Models for Legal Intensive Interaction
Shengbin Yue, Ting Huang, Zheng Jia +5
Large Language Models (LLMs) have significantly advanced legal intelligence, but the scarcity of scenario data impedes the progress toward interactive legal scenarios. This paper i…
Synergistic Multi-Agent Framework with Trajectory Learning for Knowledge-Intensive Tasks
Shengbin Yue, Siyuan Wang, Wei Chen +2
Recent advancements in Large Language Models (LLMs) have led to significant breakthroughs in various natural language processing tasks. However, generating factually consistent res…
HAF-RM: A Hybrid Alignment Framework for Reward Model Training
Shujun Liu, Xiaoyu Shen, Yuhang Lai +5
The reward model has become increasingly important in alignment, assessment, and data construction for large language models (LLMs). Most existing researchers focus on enhancing re…
DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services
Shengbin Yue, Wei Chen, Siyuan Wang +8
We propose DISC-LawLLM, an intelligent legal system utilizing large language models (LLMs) to provide a wide range of legal services. We adopt legal syllogism prompting strategies…