InternLM-Law: An Open-Sourced Chinese Legal Large Language Model
Zhiwei Fei, Songyang Zhang, Xiaoyu Shen, Dawei Zhu, Xiao Wang, Jidong Ge, Vincent Ng
Abstract
We introduce InternLM-Law, a large language model (LLM) tailored for addressing diverse legal tasks related to Chinese laws. These tasks range from responding to standard legal questions (e.g., legal exercises in textbooks) to analyzing complex real-world legal situations. Our work contributes to Chinese Legal NLP research by (1) conducting one of the most extensive evaluations of state-of-the-art general-purpose and legal-specific LLMs to date that involves an automatic evaluation on the 20 legal NLP tasks in LawBench, a human evaluation on a challenging version of the Legal Consultation task, and an automatic evaluation of a model’s ability to handle very long legal texts; (2) presenting a methodology for training a Chinese legal LLM that offers superior performance to all of its counterparts in our extensive evaluation; and (3) facilitating future research in this area by making all of our code and model publicly available at https://github.com/InternLM/InternLM-Law.
BibTeX
@inproceedings{fei-etal-2025-internlm,
title = "{I}ntern{LM}-Law: An Open-Sourced {C}hinese Legal Large Language Model",
author = "Fei, Zhiwei and
Zhang, Songyang and
Shen, Xiaoyu and
Zhu, Dawei and
Wang, Xiao and
Ge, Jidong and
Ng, Vincent",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.629/",
pages = "9376--9392"
}