LawLuo：面向多轮中文法律咨询的多智能体协作框架
LawLuo: A Multi-Agent Collaborative Framework for Multi-Round Chinese Legal Consultation
AI总结：
LawLuo提出多智能体协作框架，模拟律所运作，通过四类智能体分工及案例图RAG，提升多轮中文法律咨询的个性化、专业性与指令遵循能力。
摘要：
法律大型语言模型（LLMs）在向非专业人士提供法律咨询方面已展现出潜力。然而，大多数现有的中文法律咨询模型基于单智能体系统，这与现实世界中的法律咨询不同——在现实中，多位专业人士协作以提供更具针对性的回应。为了更好地模拟真实咨询，我们提出了LawLuo，一个用于多轮中文法律咨询的多智能体框架。LawLuo包含四个智能体：接待员智能体，负责评估用户意图并选择律师智能体；律师智能体，负责与用户互动；秘书智能体，负责整理对话记录并生成咨询报告；以及主管智能体，负责评估律师和秘书智能体的表现以确保最佳结果。这些智能体的互动模拟了真实律师事务所的运作。为了训练它们遵循不同的法律指令，我们开发了不同的微调数据集。我们还引入了一种基于案例图的检索增强生成（RAG），以帮助律师智能体处理模糊的用户输入。实验结果表明，LawLuo在生成更个性化和专业的回应、处理模糊查询以及在多轮对话中遵循法律指令方面优于基线模型。我们的完整代码和构建的数据集将在论文被接收后开源。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
