Large Language Model-Based Semantic Search for Legal Document Retrieval and Case Analysis
Keywords:
large language models, semantic search, legal document retrieval, case analysis, retrieval-augmented generation, legal information systems, governanceAbstract
Large language models are reshaping the design of legal information systems by enabling semantic search over complex, heterogeneous corpora of statutes, judicial opinions, contracts, and case records. This article presents a system-level analysis of large language model-based semantic search for legal document retrieval and case analysis. It examines the architectural foundations that combine dense retrieval, reranking, retrieval-augmented generation, and persistent user modeling. The discussion emphasizes structural trade-offs between retrieval quality, latency, interpretability, and computational cost. It further addresses the legal domain constraints of authority, confidentiality, fairness, and explainability. The article argues that effective legal semantic search cannot be reduced to embedding quality or generative fluency; it requires an integrated socio-technical design that accounts for domain adaptation, evaluation, deployment infrastructure, robustness, and governance. By connecting technical mechanisms with institutional responsibilities, the paper offers a forward-looking perspective on building legal retrieval systems that are not only accurate but also auditable, sustainable, and aligned with the normative expectations of legal practice.
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