  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.
