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DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

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arxiv 2309.11325 v2 pith:IQO44QHJ submitted 2023-09-20 cs.CL

Shengbin Yue , Wei Chen , Siyuan Wang , Bingxuan Li , Chenchen Shen , Shujun Liu , Yuxuan Zhou , Yao Xiao
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Song Yun Xuanjing Huang Zhongyu Wei
This is my paper
classification cs.CL
keywords legaldisc-lawllmintelligentllmsmodelsdisc-law-evalfine-tuninglanguage
verification ladder T0 review T1 audit T2 compute T3 formal
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We propose DISC-LawLLM, an intelligent legal system utilizing large language models (LLMs) to provide a wide range of legal services. We adopt legal syllogism prompting strategies to construct supervised fine-tuning datasets in the Chinese Judicial domain and fine-tune LLMs with legal reasoning capability. We augment LLMs with a retrieval module to enhance models' ability to access and utilize external legal knowledge. A comprehensive legal benchmark, DISC-Law-Eval, is presented to evaluate intelligent legal systems from both objective and subjective dimensions. Quantitative and qualitative results on DISC-Law-Eval demonstrate the effectiveness of our system in serving various users across diverse legal scenarios. The detailed resources are available at https://github.com/FudanDISC/DISC-LawLLM.

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Cited by 6 Pith papers

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    A literature review that classifies LLM-for-law research using a dual-lens taxonomy of Toulmin argumentation components and legal practitioner roles.

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