Fine-tuning and application of large language model in law domain
Year: 2024
Type: article
Abstract: In recent years, large language model have demonstrated outstanding capabilities across multiple natural language processing tasks, greatly aiding the development of intelligent law systems. Existing large language model in the law domain, primarily focuses on fine-tuning foundation model for simple question answering tasks in legal consultation, have not considered other application scenarios in the legal field, such as legal information extraction, judgment prediction, etc. Real world law services are far more complex than dialogues. LawLLM was proposed, a Chinese law intelligent large language model, capable of providing diverse law services for different user groups. Additionally, the application of information extraction on long-context judicial documents in the law domain was explored. The average performance of LawLLM on Lawbench outperforms all the compared large language models, being 0.19% higher than the 175 billion parameter GPT-3.5-Turbo model. LawLLM’s average performance on few-shot tasks on Lawbench is slightly lower than GPT-3.5-Turbo, by 0.02%.
Authors: SHEN Chenchen, YUE Shengbin, LIU Shujun, ZHOU Yuxuan, WANG Siyuan, CHEN Wei, XIAO Yao, LI Bingxuan, SONG Yun, SHEN Xiaoyu, HUANG Xuanjing, WEI Zhongyu
Language: English
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7038147883
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