DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services
TL
TL;DR
Quantitative and qualitative results on DISC-Law-Eval demonstrate the effectiveness of the system in serving various users across diverse legal scenarios, and enhances models' ability to access and utilize external legal knowledge.
Abstract
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.
References39 items
1
Chatlaw: A Multi-Agent Collaborative Legal Assistant with Knowledge Graph Enhanced Mixture-of-Experts Large Language Model
2
Legal Syllogism Prompting: Teaching Large Language Models for Legal Judgment Prediction
3
Judging LLM-as-a-judge with MT-Bench and Chatbot Arena
4
Lawyer LLaMA Technical Report
5