LawLuo: A Chinese Law Firm Co-run by LLM Agents
<|reference_start|>LawLuo: A Chinese Law Firm Co-run by LLM Agents: 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.<|reference_end|>
@article{sun2024lawluo:,
    title={LawLuo: A Chinese Law Firm Co-run by LLM Agents},
    author={Jingyun Sun, Chengxiao Dai, Zhongze Luo, Yangbo Chang, Yang Li},
    journal={arXiv preprint arXiv:2407.16252},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.16252},
    primaryClass={cs.CL cs.AI cs.CV}
}