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Lawyer LLaMA Technical Report

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arxiv 2305.15062 v2 pith:UBTLI5X2 submitted 2023-05-24 cs.CL cs.AI

Quzhe Huang , Mingxu Tao , Chen Zhang , Zhenwei An , Cong Jiang , Zhibin Chen , Zirui Wu , Yansong Feng This is my paper · ORCID
classification cs.CLcs.AI
keywords modelknowledgellamadatadomaindomain-specificdomainsduring
verification ladder T0 review T1 audit T2 compute T3 formal
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Large Language Models (LLMs), like LLaMA, have exhibited remarkable performance 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 propose a new framework to adapt LLMs to specific domains and build Lawyer LLaMA, a legal domain LLM, based on this framework. Specifically, we inject domain knowledge during the continual training stage and teach the model to learn professional skills using properly designed supervised fine-tuning tasks. Moreover, to alleviate the hallucination problem during the model's generation, we add a retrieval module and extract relevant legal articles before the model answers any queries. When learning domain-specific skills, we find that experts' experience is much more useful than experiences distilled from ChatGPT, where hundreds of expert-written data outperform tens of thousands of ChatGPT-generated ones. We will release our model and data.

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Forward citations

Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 39 citations worldwide. Full citation record

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    cs.CL 2025-08 conditional novelty 5.0 of 10

    A legal-chain-guided framework generates criminal judicial opinions, jointly producing legal reasoning and sentencing predictions, and outperforms baselines on two Chinese case datasets.

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