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.

通过将专业的法律知识与大型语言模型相结合，KELLER 引入了一种基于大型语言模型的法律案例重构方法，用于实现有效和可解释的法律案例检索，并在两个法律案例检索基准上展示了 KELLER 相比现有方法在复杂法律案例查询方面的卓越性能和鲁棒性。