Author(s)
Quzhe Huang, Mingxu Tao, Zhenwei An, Chen Zhang, Cong Jiang, Zhibin Chen, Zirui Wu, Yansong Feng
Abstract
Large Language Models (LLMs), like LLaMA, have exhibited remarkable performances across various tasks. Nevertheless, when deployed to specific domains such as law or medicine, the models still confront the challenge of a deficiency in domain-specific knowledge and an inadequate capability to leverage that knowledge to resolve domain-related problems. In this paper, we focus on the legal domain and explore how to inject domain knowledge during the continual training stage and how to design proper supervised finetune tasks to help the model tackle practical issues. Moreover, to alleviate the hallucination problem during model's generation, we add a retrieval module and extract relevant articles before the model answers any queries. Augmenting with the extracted evidence, our model could generate more reliable responses.
Links to paper
- Link to arXiv: https://arxiv.org/abs/2305.15062
- Link to pdf: https://arxiv.org/pdf/2305.15062.pdf
- Link to data and model: https://github.com/AndrewZhe/lawyer-llama.