ACL 2025finding0 citations

CitaLaw: Enhancing LLM with Citations in Legal Domain

Kepu Zhang, Weijie Yu, Sunhao Dai, Jun Xu

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.

BibTeX
@inproceedings{zhang-etal-2025-citalaw,
    title = "{C}ita{L}aw: Enhancing {LLM} with Citations in Legal Domain",
    author = "Zhang, Kepu  and
      Yu, Weijie  and
      Dai, Sunhao  and
      Xu, Jun",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.findings-acl.583/",
    doi = "10.18653/v1/2025.findings-acl.583",
    pages = "11183--11196",
    ISBN = "979-8-89176-256-5"
}
CitaLaw: Enhancing LLM with Citations in Legal Domain · ACL 2025