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.
DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services
A system using large language models fine-tuned with legal syllogism prompting and augmented with a retrieval module demonstrates effectiveness across various legal scenarios.
- Year
- 2023
- Venue
- arXiv 2023
- Authors
- 11
- Hosting
- Abstract onlyARXIV-DEFAULT
Cite
Notes
Only stored in your browser.
Attribution
- Abstract & full text
- arxiv.org/abs/2309.11325v2ARXIV-DEFAULT
- TL;DR
- Semantic Scholar