Legal Retrieval

Legal retrieval focuses on efficiently and accurately retrieving relevant legal documents (cases, statutes, etc.) based on user queries, aiming to improve legal research and decision-making. Current research emphasizes leveraging large language models (LLMs) alongside techniques like BM25 and BERT for improved ranking and retrieval, often incorporating graph neural networks to exploit the structural relationships within legal corpora. This field is significant because enhanced legal retrieval systems can significantly reduce the time and effort required for legal research, potentially increasing access to justice and improving the efficiency of legal professionals.