基于大语言模型（LLM）智能体系统的中国法庭模拟
Chinese Court Simulation with LLM-Based Agent System
AI总结：
针对传统模拟庭审公众难以参与、现有研究缺乏系统设计评估的问题，提出首个基于中国法庭真实流程的SimCourt框架，构建具记忆等能力的法律智能体，提升判决预测效果且智能体回应优于真实庭审参与者。
摘要：
模拟庭审长期以来是法律专业培训和教育的重要平台，不仅帮助学生了解真实庭审流程，还为案例分析和判决预测提供实用价值。传统模拟庭审因依赖专业导师和人类参与者，公众难以参与。幸运的是，大语言模型（LLMs）的兴起为创建更易获取、可扩展的法庭模拟提供了新机遇。尽管前景广阔，但现有研究主要聚焦智能体构建，却忽略了法庭模拟的系统设计与评估，而这对其实践中的可信度和实用性更为重要。为此，我们提出首个基于中国法庭真实流程结构的法庭模拟框架——SimCourt。该框架复现中国庭审的全部5个核心阶段，融入5种法庭角色，严格遵循中国的程序定义。为模拟不同角色的庭审参与者，我们提出并构建了具备记忆、规划和反思能力的法律智能体。法律判决预测实验显示，我们的框架生成的模拟庭审能更好地引导系统预测每个案件的监禁、缓刑和罚金结果。人类专家的进一步标注表明，在我们的模拟框架下，智能体的回应在诸多场景中甚至优于真实庭审中的法官和律师。这些结果进一步证明了基于LLM的法庭模拟的潜力。
英文摘要：
Mock trial has long served as an important platform for legal professional training and education. It not only helps students learn about realistic trial procedures, but also provides practical value for case analysis and judgment prediction. Traditional mock trials are difficult to access by the public because they rely on professional tutors and human participants. Fortunately, the rise of large language models (LLMs) provides new opportunities for creating more accessible and scalable court simulations. While promising, existing research mainly focuses on agent construction while ignoring the systematic design and evaluation of court simulations, which are actually more important for the credibility and usage of court simulation in practice. To this end, we present the first court simulation framework -- SimCourt -- based on the real-world procedure structure of Chinese courts. Our framework replicates all 5 core stages of a Chinese trial and incorporates 5 courtroom roles, faithfully following the procedural definitions in China. To simulate trial participants with different roles, we propose and craft legal agents equipped with memory, planning, and reflection abilities. Experiment on legal judgment prediction show that our framework can generate simulated trials that better guide the system to predict the imprisonment, probation, and fine of each case. Further annotations by human experts show that agents' responses under our simulation framework even outperformed judges and lawyers from the real trials in many scenarios. These further demonstrate the potential of LLM-based court simulation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
