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    <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.</abstract>
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%0 Conference Proceedings
%T CitaLaw: Enhancing LLM with Citations in Legal Domain
%A Zhang, Kepu
%A Yu, Weijie
%A Dai, Sunhao
%A Xu, Jun
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F zhang-etal-2025-citalaw
%X 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.
%R 10.18653/v1/2025.findings-acl.583
%U https://aclanthology.org/2025.findings-acl.583/
%U https://doi.org/10.18653/v1/2025.findings-acl.583
%P 11183-11196

Markdown (Informal)

[CitaLaw: Enhancing LLM with Citations in Legal Domain](https://aclanthology.org/2025.findings-acl.583/) (Zhang et al., Findings 2025)

• CitaLaw: Enhancing LLM with Citations in Legal Domain (Zhang et al., Findings 2025)

ACL

• Kepu Zhang, Weijie Yu, Sunhao Dai, and Jun Xu. 2025. CitaLaw: Enhancing LLM with Citations in Legal Domain. In Findings of the Association for Computational Linguistics: ACL 2025, pages 11183–11196, Vienna, Austria. Association for Computational Linguistics.