  AI legal assistants based on Large Language Models (LLMs) can provide
accessible legal consulting services, but the hallucination problem poses
potential legal risks. This paper presents Chatlaw, an innovative legal
assistant utilizing a Mixture-of-Experts (MoE) model and a multi-agent system
to enhance the reliability and accuracy of AI-driven legal services. By
integrating knowledge graphs with artificial screening, we construct a
high-quality legal dataset to train the MoE model. This model utilizes
different experts to address various legal issues, optimizing the accuracy of
legal responses. Additionally, Standardized Operating Procedures (SOP), modeled
after real law firm workflows, significantly reduce errors and hallucinations
in legal services. Our MoE model outperforms GPT-4 in the Lawbench and Unified
Qualification Exam for Legal Professionals by 7.73% in accuracy and 11 points,
respectively, and also surpasses other models in multiple dimensions during
real-case consultations, demonstrating our robust capability for legal
consultation.
