Go to ACL ARR 2026 January homepageChinese Court Simulation with LLM-Based Agents System
Keywords: Court, Simulation, Large Language Model, Multi-Agent System
Abstract: Mock trial has long served as an important platform for professional legal training and education. 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 ignored the systematic design and procedure evaluation of court simulations, which are critical to the credibility and usage of court simulation in practice. To this end, we propose a novel court simulation paradigm, i.e. SimCourt, based on the real-world procedure structure of Chinese courts, and design a comprehensive evaluation framework focusing on both legal judgment prediction and court procedure analysis. Experiments show that our framework can generate simulated trials that better guide the system in predicting the imprisonment, probation, and fine of each case. Further procedure evaluations show that agents' responses under our simulation framework even outperform judges and lawyers from the real trials in many aspects. These demonstrate the potential of LLM-based court simulation.
Paper Type: Long
Research Area: AI/LLM Agents
Research Area Keywords: legal NLP,LLM/AI agents,evaluation and metrics
Contribution Types: NLP engineering experiment, Publicly available software and/or pre-trained models
Languages Studied: Chinese
Submission Number: 1545
Loading