用多智能体辩论模拟法庭，轻量高效预测法律判决。
Debate-Feedback: A Multi-Agent Framework for Efficient Legal Judgment Prediction
- 构建多智能体辩论框架，模拟真实庭审中的观点交锋。
- 无需大规模历史数据，推理效率显著提升，性能超越多个现有模型。
- 适合追求高效、可解释法律AI系统的研究人员与开发者。
人工智能在法律分析与预测（LegalAI）领域受到广泛关注，以往研究多依赖检索方法和大模型微调，但通常需要大量数据且未能充分发挥现代大语言模型（LLM）潜力。受真实法庭辩论环节启发，本文提出基于辩论反馈（Debate-Feedback）架构的新型法律判决预测模型，融合大语言模型的多智能体辩论与可信度评估机制。相比传统方法，该模型大幅减少对大规模历史数据的依赖，实现轻量化且稳健的预测。对比实验表明，其性能优于多个通用及领域专用法律模型，具备动态推理能力，为未来LegalAI研究提供了新方向。
原文摘要 · Abstract (English)
The use of AI in legal analysis and prediction (LegalAI) has gained widespread attention, with past research focusing on retrieval-based methods and fine-tuning large models. However, these approaches often require large datasets and underutilize the capabilities of modern large language models (LLMs). In this paper, inspired by the debate phase of real courtroom trials, we propose a novel legal judgment prediction model based on the Debate-Feedback architecture, which integrates LLM multi-agent debate and reliability evaluation models. Unlike traditional methods, our model achieves significant improvements in efficiency by minimizing the need for large historical datasets, thus offering a lightweight yet robust solution. Comparative experiments show that it outperforms several general-purpose and domain-specific legal models, offering a dynamic reasoning process and a promising direction for future LegalAI research.
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