Multimodal Multi-Agent Empowered Legal Judgment Prediction

Research Summary

Contribution Summary

• 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.

Why It Matters For Eval

• 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.

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