DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services
This work addresses the need for automated legal assistance, but it is incremental as it applies existing fine-tuning and retrieval methods to the legal domain.
The authors tackled the problem of providing intelligent legal services by fine-tuning large language models (LLMs) for legal reasoning, resulting in an effective system that serves users across diverse legal scenarios as demonstrated on their benchmark.
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.