用多智能体框架提升法律判决预测准确率
Multimodal Multi-Agent Empowered Legal Judgment Prediction
- 设计多智能体系统分解案件任务，分阶段处理证据与事实
- 在10万+条中文裁判文书上验证，显著优于传统方法
- 适合法律AI研究者与司法智能化开发者参考
法律判决预测（LJP）旨在基于案件事实描述预测裁判结果，是推动法律系统智能化的关键任务。传统方法依赖统计分析或角色模拟，难以应对多重指控、多样证据及适应性不足的问题。本文提出JurisMMA框架，通过有效分解审判任务、标准化流程并分阶段组织，提升预测能力。同时构建了JurisMM数据集，包含超过10万条近期中国司法记录，涵盖文本与多模态视频-文本数据，支持全面评估。在JurisMM和基准数据集LawBench上的实验验证了该框架的有效性。结果表明，该框架不仅适用于LJP，还可推广至更广泛的法律应用场景，为未来法律方法与数据集的发展提供新思路。
原文摘要 · Abstract (English)
Legal Judgment Prediction (LJP) aims to predict the outcomes of legal cases based on factual descriptions, serving as a fundamental task to advance the development of legal systems. Traditional methods often rely on statistical analyses or role-based simulations but face challenges with multiple allegations, diverse evidence, and lack adaptability. In this paper, we introduce JurisMMA, a novel framework for LJP that effectively decomposes trial tasks, standardizes processes, and organizes them into distinct stages. Furthermore, we build JurisMM, a large dataset with over 100,000 recent Chinese judicial records, including both text and multimodal video-text data, enabling comprehensive evaluation. Experiments on JurisMM and the benchmark LawBench validate our framework's effectiveness. These results indicate that our framework is effective not only for LJP but also for a broader range of legal applications, offering new perspectives for the development of future legal methods and datasets.
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