LawLuo: A Multi-Agent Collaborative Framework for Multi-Round Chinese Legal Consultation

TL;DR

LawLuo introduces a multi-agent framework for multi-turn Chinese legal consultations, simulating real-world legal collaboration to improve response quality, personalization, and handling of ambiguous queries.

Contribution

It presents a novel multi-agent system for Chinese legal consultation, including specialized agents and a case graph-based RAG, advancing beyond single-agent models.

Findings

Outperforms baselines in response personalization and professionalism

Handles ambiguous user inputs effectively

Adheres to legal instructions in multi-turn conversations

Abstract

Legal Large Language Models (LLMs) have shown promise in providing legal consultations to non-experts. However, most existing Chinese legal consultation models are based on single-agent systems, which differ from real-world legal consultations, where multiple professionals collaborate to offer more tailored responses. To better simulate real consultations, we propose LawLuo, a multi-agent framework for multi-turn Chinese legal consultations. LawLuo includes four agents: the receptionist agent, which assesses user intent and selects a lawyer agent; the lawyer agent, which interacts with the user; the secretary agent, which organizes conversation records and generates consultation reports; and the boss agent, which evaluates the performance of the lawyer and secretary agents to ensure optimal results. These agents' interactions mimic the operations of real law firms. To train them to follow different legal instructions, we developed distinct fine-tuning datasets. We also introduce a case graph-based RAG to help the lawyer agent address vague user inputs. Experimental results show that LawLuo outperforms baselines in generating more personalized and professional responses, handling ambiguous queries, and following legal instructions in multi-turn conversations. Our full code and constructed datasets will be open-sourced upon paper acceptance.