26 citations · 27 across the 8 of their papers we have counts for
10 papers
ParaAgent: Reinforcing Parallel Acting in Open-World Tool Environments
Shengbin Yue, Hongru Wang, Siyuan Wang +3
Language model agents are increasingly deployed in open-world tool environments, which require balancing exploring unknown capabilities and exploiting known ones. Existing methods…
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…
From Knowing to Acting: Benchmarking Self-Awareness Capability of LLM Agents
Yifan Li, Shengbin Yue, Boyu Feng +6
The integration of external tools has transitioned LLM agents from passive responders to autonomous systems. However, current benchmarks prioritize execution success, neglecting se…
PersonaDual: Balancing Personalization and Objectivity via Adaptive Reasoning
Xiaoyou Liu, Xinyi Mou, Shengbin Yue +5
As users increasingly expect LLMs to align with their preferences, personalized information becomes valuable. However, personalized information can be a double-edged sword: it can…
Ready Jurist One: Benchmarking Language Agents for Legal Intelligence in Dynamic Environments
Zheng Jia, Shengbin Yue, Wei Chen +5
The gap between static benchmarks and the dynamic nature of real-world legal practice poses a key barrier to advancing legal intelligence. To this end, we introduce J1-ENVS, the fi…
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…
Fine-Tuned Large Language Model for Visualization System: A Study on Self-Regulated Learning in Education
Lin Gao, Jing Lu, Zekai Shao +7
Large Language Models (LLMs) have shown great potential in intelligent visualization systems, especially for domain-specific applications. Integrating LLMs into visualization syste…
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…