Civil Court Simulation with Large Language Models

Problem
The paper addresses the lack of scalable simulation tools for civil court cases, which are more prevalent than criminal cases but have received less attention in existing research. Current court simulation studies primarily focus on criminal litigation, leaving a gap in the literature regarding civil cases, which are characterized by more flexible claims, liabilities, and remedies. This work is particularly relevant as it is a preprint and has not undergone peer review, indicating that the findings should be interpreted with caution.

Method
The authors propose a multi-agent court simulation framework specifically designed for Chinese civil cases. The framework is structured around a five-stage civil trial procedure, which includes stages such as pleadings, evidence presentation, deliberation, judgment, and appeal. A key technical contribution is the integration of a memory module and statute retrieval system, which enhances the model’s ability to manage long-term dependencies and legal references throughout the trial process. The framework employs large language models (LLMs) to facilitate role-based interactions among agents representing different legal roles, such as judges, plaintiffs, and defendants. The training compute details are not disclosed, but the authors emphasize the importance of memory quality in influencing the simulation’s overall performance.

Results
The framework demonstrates a high level of reliability in producing civil judgments, particularly excelling in liability allocation and multi-item adjudication tasks. Quantitative results indicate that the proposed system outperforms baseline models in terms of judgment accuracy, although specific metrics and baseline comparisons are not detailed in the abstract. The authors also conduct experiments to assess the impact of memory quality on simulation outcomes, revealing that better memory management correlates with improved judgment reliability. The findings suggest that the framework effectively captures the complexities of civil litigation.

Limitations
The authors acknowledge several limitations, including the potential biases inherent in the training data and the need for further validation across diverse legal contexts. They also note that the framework is tailored to the Chinese legal system, which may limit its applicability to other jurisdictions. Additionally, the reliance on LLMs raises concerns about interpretability and the potential for generating misleading legal conclusions if the underlying models are not adequately grounded in legal principles.

Why it matters
This work has significant implications for legal education and practice, as it provides a scalable alternative to traditional human-based court simulations, which are often resource-intensive. By leveraging LLMs, the framework can facilitate more accessible training for legal professionals and enhance understanding of civil litigation processes. The insights gained from this research could inform future developments in legal AI applications, particularly in the realm of civil law. The dataset and code are publicly available, promoting further exploration and validation of the framework by the research community, as published in arXiv.