🤖 AI Summary
This work addresses the limitations of single large language models in complex legal reasoning—specifically their narrow perspective and constrained deliberative capacity—by proposing a multi-agent collaborative deliberation framework inspired by courtroom procedures and legal argumentation theory. The framework introduces two novel legally grounded interaction heuristics that simulate adversarial debates among agents representing diverse viewpoints, thereby fostering multi-perspective critical reasoning. Experimental results demonstrate that, while overall performance remains comparable to baseline models, the proposed approach significantly outperforms existing methods on cases requiring nuanced analysis from multiple legal standpoints, particularly excelling in resolving complex legal problems where baseline approaches fail.
📝 Abstract
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.