用多个AI律师模拟法庭辩论，提升法律推理的深度与多样性。
Investigating Multi-Agent Deliberation in Law
- 设计两类受法庭程序启发的多智能体框架，实现多视角法律推理。
- 在法律与非法律任务中表现接近单模型，但答案差异显著且互补。
- 适合需要多角度批判性思考的复杂法律问题，如判例分析。
人工智能在法律领域的应用日益广泛，有望提升司法可及性。其中基于大语言模型（LLMs）的代理型AI正逐渐兴起，但法律领域中的多智能体方法仍处于探索阶段。本文研究基于LLMs的多智能体法律推理方法，提出两种受法庭程序和法律论证启发的新型多智能体框架（MAD）。在法律与非法律基准测试中，多智能体框架整体性能与基线大模型相当，但生成的答案显著不同。值得注意的是，某些案件中多智能体能解决基线失败的问题，反之亦然。定性评估表明，在需多角度批判性思维的任务中，多智能体方法优于单一模型。本工作为法律领域AI发展提供了新方向，并验证了法律启发式多智能体系统在思辨推理中的潜力。
原文摘要 · Abstract (English)
Artificial Intelligence is increasingly applied to the field of law, and has the potential to increase access to justice. One particular movement that is gaining traction is that of agentic AI, wherein AI agents, based on Large Language Models (LLMs) can take autonomous actions. In particular, multi-agent approaches in the legal domain remain largely unexplored. In this paper, we investigate multi-agent deliberation methods for legal reasoning tasks using LLMs. We explore multi-agent deliberation (MAD) and introduce two novel multi-agent frameworks inspired by courtroom procedures and legal argumentation. Our experiments on both legal and non-legal benchmarks reveal that multi-agent frameworks achieve comparable overall performance to baseline large language models, but produce significantly distinct answers. Notably, these approaches can successfully solve cases that the baseline fails to address, and vice versa. We conduct a qualitative evaluation and highlight scenarios where multi-agent frameworks outperform monolithic approaches. For example, multi-agent approaches appear better suited for answering questions that require critical thinking from multiple perspectives. Our work positions multi-agent systems as a promising direction for AI in the legal domain, while demonstrating the potential of law-inspired multi-agent approaches for deliberation.
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