Adaptive Knowledge Retrieval for Legal Document Analysis Using Risk-Sensitive Large Language Model Generation
Keywords:
adaptive retrieval, legal document analysis, risk-sensitive generation, large language models, uncertainty quantification, governance, socio-technical systems, auditabilityAbstract
Legal document analysis is a high-stakes socio-technical activity in which retrieval quality, interpretive stability, and institutional accountability are tightly coupled. This paper examines adaptive knowledge retrieval for legal document analysis through risk-sensitive large language model generation. It argues that legal artificial intelligence cannot be treated as a conventional question-answering pipeline, because legal texts are internally intertextual, jurisdictionally layered, temporally contingent, and often ambiguous in ways that require traceable reasoning rather than fluent assertion. The proposed perspective combines adaptive retrieval, uncertainty-aware generation, and governance-oriented system design. Adaptive retrieval is understood as a dynamic allocation of computational and evidentiary effort across statutes, regulations, case law, contractual language, administrative guidance, and internal compliance materials. Risk-sensitive generation is treated as a control problem in which the system adjusts abstention, citation density, explanation granularity, and escalation to human review according to the probable institutional harm of an incorrect or unsupported output. The paper discusses architectural trade-offs involving index freshness, latency, provenance, privacy, auditability, and operational cost. It also considers fairness, due process, professional responsibility, and the sustainability of legal knowledge infrastructures. Through cross-domain comparisons with clinical decision support, financial compliance, and public-sector automated decision-making, the paper identifies conditions under which adaptive legal retrieval can improve reliability without disguising residual uncertainty. The conclusion emphasizes that trustworthy legal generation depends less on a single model than on an integrated system of retrieval governance, risk calibration, human oversight, and continuous evaluation.
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