Boosting legal case retrieval by query content selection with large language models

Youchao Zhou, Heyan Huang*, Zhijing Wu

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

3 引用 (Scopus)

摘要

Legal case retrieval, which aims to retrieve relevant cases to a given query case, benefits judgment justice and attracts increasing attention. Unlike generic retrieval queries, legal case queries are typically long and the definition of relevance is closely related to legal-specific elements. Therefore, legal case queries may suffer from noise and sparsity of salient content, which hinders retrieval models from perceiving correct information in a query. While previous studies have paid attention to improving retrieval models and understanding relevance judgments, we focus on enhancing legal case retrieval by utilizing the salient content in legal case queries. We first annotate the salient content in queries manually and investigate how sparse and dense retrieval models attend to those content. Then we experiment with various query content selection methods utilizing large language models (LLMs) to extract or summarize salient content and incorporate it into the retrieval models. Experimental results show that reformulating long queries using LLMs improves the performance of both sparse and dense models in legal case retrieval.

源语言英语
主期刊名SIGIR-AP 2023 - Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region
出版商Association for Computing Machinery, Inc
176-184
页数9
ISBN(电子版)9798400704086
DOI
出版状态已出版 - 26 11月 2023
活动11th International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, SIGIR-AP 2023 - Beijing, 中国
期限: 26 11月 202328 11月 2023

出版系列

姓名SIGIR-AP 2023 - Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region

会议

会议11th International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, SIGIR-AP 2023
国家/地区中国
Beijing
时期26/11/2328/11/23

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引用此

Zhou, Y., Huang, H., & Wu, Z. (2023). Boosting legal case retrieval by query content selection with large language models. 在 SIGIR-AP 2023 - Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (页码 176-184). (SIGIR-AP 2023 - Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region). Association for Computing Machinery, Inc. https://doi.org/10.1145/3624918.3625328