CitaLaw: Enhancing LLM with Citations in Legal Domain

December 19, 2024 ยท Declared Dead ยท ๐Ÿ› Annual Meeting of the Association for Computational Linguistics

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Authors Kepu Zhang, Weijie Yu, Sunhao Dai, Jun Xu arXiv ID 2412.14556 Category cs.CL: Computation & Language Citations 18 Venue Annual Meeting of the Association for Computational Linguistics Last Checked 3 months ago
Abstract
In this paper, we propose CitaLaw, the first benchmark designed to evaluate LLMs' ability to produce legally sound responses with appropriate citations. CitaLaw features a diverse set of legal questions for both laypersons and practitioners, paired with a comprehensive corpus of law articles and precedent cases as a reference pool. This framework enables LLM-based systems to retrieve supporting citations from the reference corpus and align these citations with the corresponding sentences in their responses. Moreover, we introduce syllogism-inspired evaluation methods to assess the legal alignment between retrieved references and LLM-generated responses, as well as their consistency with user questions. Extensive experiments on 2 open-domain and 7 legal-specific LLMs demonstrate that integrating legal references substantially enhances response quality. Furthermore, our proposed syllogism-based evaluation method exhibits strong agreement with human judgments.
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