2024/06/28 by Chenlong Deng, Deng, Chenlong, Kelong Mao +3 · 4 citations
Business, Management and Accounting · Social Sciences · #Artificial Intelligence in Law #Computation and Language (cs.CL) #Dispute Resolution and Class Actions #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Legal Education and Practice Innovations
paper · pdf · doi:10.48550/arxiv.2406.19760
openalex publication_date 2024/06/28 · openalex created_date 2024/07/02 · openalex updated_date 2026/07/28
Legal case retrieval for sourcing similar cases is critical in upholding judicial fairness. Different from general web search, legal case retrieval involves processing lengthy, complex, and highly specialized legal documents. Existing methods in this domain often overlook the incorporation of legal expert knowledge, which is crucial for accurately understanding and modeling legal cases, leading to unsatisfactory retrieval performance. This paper introduces KELLER, a legal knowledge-guided case reformulation approach based on large language models (LLMs) for effective and interpretable legal case retrieval. By incorporating professional legal knowledge about crimes and law articles, we enable large language models to accurately reformulate the original legal case into concise sub-facts of crimes, which contain the essential information of the case. Extensive experiments on two legal case retrieval benchmarks demonstrate superior retrieval performance and robustness on complex legal case queries of KELLER over existing methods.