CitaLaw: Enhancing LLM with Citations in Legal Domain

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 LLMbased systems to retrieve supporting citations from the reference corpus and align these citations with the corresponding sentences in their responses.Moreover, we introduce syllogisminspired 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.

Authors

Publication Details

Published
2025-01-01
DOI
https://doi.org/10.18653/v1/2025.findings-acl.583
Citations
1
Primary Topic
Natural Language Processing Techniques
Type
article
Field-Weighted Citation Impact
2.48

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article

CitaLaw: Enhancing LLM with Citations in Legal Domain

Weijie Yu, K.Y.J. Zhang, Sunhao Dai
1 citations
Natural Language Processing Techniques
2.48
article

CitaLaw: Enhancing LLM with Citations in Legal Domain

Weijie Yu, K.Y.J. Zhang, Sunhao Dai
article en
1 citations

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 LLMbased systems to retrieve supporting citations from the reference corpus and align these citations with the corresponding sentences in their responses.Moreover, we introduce syllogisminspired 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.

University of International Business and Economics, National Natural Science Foundation of China, Renmin University of China, Ministry of Education, India, Fundamental Research Funds for the Central Universities
Peace, Justice and strong institutions
Openalex Percentile: Top 9%
Natural Language Processing Techniques
2.48
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