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

About

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.

Shengbin Yue, Wei Chen, Siyuan Wang, Bingxuan Li, Chenchen Shen, Shujun Liu, Yuxuan Zhou, Yao Xiao, Song Yun, Xuanjing Huang, Zhongyu Wei• 2023

Related benchmarks

TaskDatasetResultRank
Complaint DraftingJ1 EVAL
FOR Score74.2
14
Defence DraftingJ1 EVAL
FOR26.5
14
Legal ConsultationJ1 EVAL
Average Score35.3
14
Civil CourtJ1 EVAL
PFS Score3.4
14
Criminal CourtJ1 EVAL
PFS (Procedural Fairness Score)2.2
14
Knowledge QuestioningJ1 EVAL
Average Score48.7
14
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