  Large Language Models (LLMs) demonstrate substantial potential in delivering
legal consultation services to users without a legal background, attributed to
their superior text comprehension and generation capabilities. Nonetheless,
existing Chinese legal LLMs limit interaction to a single model-user dialogue,
unlike the collaborative consultations typical of law firms, where multiple
staff members contribute to a single consultation. This limitation prevents an
authentic consultation experience. Additionally, extant Chinese legal LLMs
suffer from critical limitations: (1) insufficient control over the quality of
instruction fine-tuning data; (2) increased model hallucination resulting from
users' ambiguous queries; and (3) a reduction in the model's ability to follow
instructions over multiple dialogue turns. In response to these challenges, we
propose a novel legal dialogue framework that leverages the collaborative
capabilities of multiple LLM agents, termed LawLuo. This framework encompasses
four agents: a receptionist, a lawyer, a secretary, and a boss, each
responsible for different functionalities, collaboratively providing a
comprehensive legal consultation to users. Additionally, we constructed two
high-quality legal dialogue datasets, KINLED and MURLED, and fine-tuned
ChatGLM-3-6b using these datasets. We propose a legal query clarification
algorithm called ToLC. Experimental results demonstrate that LawLuo outperforms
baseline LLMs, including GPT-4, across three dimensions: lawyer-like language
style, the usefulness of legal advice, and the accuracy of legal knowledge. Our
code and datasets are available at https://github.com/NEFUJing/LawLuo.
