Unleashing the Power of LLMs in Court View Generation by Stimulating Internal Knowledge and Incorporating External Knowledge
Yifei Liu, Yiquan Wu, Ang Li, Yating Zhang, Changlong Sun, Weiming Lu, Fei Wu, Kun Kuang
Abstract
Court View Generation (CVG) plays a vital role in the realm of legal artificial intelligence, which aims to support judges in crafting legal judgment documents. The court view consists of three essential judgment parts: the charge-related, law article-related, and prison term-related parts, each requiring specialized legal knowledge, rendering CVG a challenging task.Although Large Language Models (LLMs) have made remarkable strides in language generation, they encounter difficulties in the knowledge-intensive legal domain.Actually, there can be two types of knowledge: internal knowledge stored within LLMs’ parameters and external knowledge sourced from legal documents outside the models.In this paper, we decompose court views into different parts, stimulate internal knowledge, and incorporate external information to unleash the power of LLMs in the CVG task.To validate our method, we conduct a series of experiment results on two real-world datasets LAIC2021 and CJO2022. The experiments demonstrate that our method is capable of generating more accurate and reliable court views.
BibTeX
@inproceedings{liu-etal-2024-unleashing,
title = "Unleashing the Power of {LLM}s in Court View Generation by Stimulating Internal Knowledge and Incorporating External Knowledge",
author = "Liu, Yifei and
Wu, Yiquan and
Li, Ang and
Zhang, Yating and
Sun, Changlong and
Lu, Weiming and
Wu, Fei and
Kuang, Kun",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2024",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-naacl.177/",
doi = "10.18653/v1/2024.findings-naacl.177",
pages = "2782--2792"
}