Intelligent Legal Document Retrieval via LLM-Based User Intent Understanding and Personalized Knowledge Modeling
Keywords:
legal document retrieval; large language models; user intent understanding; personalized knowledge modeling; legal AI systemsAbstract
The exponential growth of legal information across statutes, case law, regulatory filings, and contracts has rendered conventional keyword-based retrieval systems increasingly inadequate for the nuanced needs of legal professionals. This paper presents a systemic examination of intelligent legal document retrieval grounded in large language model (LLM)-based user intent understanding and personalized knowledge modeling. We argue that effective retrieval in the legal domain demands a departure from static query-document matching toward architectures that dynamically interpret ambiguous, context-rich user intentions and adapt to individual practitioner expertise, jurisdictional specializations, and case-specific workflows. The discussion spans the structural trade-offs inherent in LLM-powered intent extraction pipelines, the design of personalized user models that encode long-term professional memory without violating confidentiality, and the broader implications for system deployment, fairness, sustainability, and governance. Through continuous academic analysis, we investigate how retrieval-augmented generation, domain-adapted transformer models, and adaptive user profiling can be orchestrated into a cohesive infrastructure that reconciles precision, latency, and interpretability constraints. The paper further addresses critical policy dimensions, including algorithmic equity across demographic cohorts, energy footprint minimization in large-scale legal AI pipelines, and the robustness of retrieval systems under adversarial input perturbations. By weaving together architectural insights, cross-domain comparisons, and forward-looking perspectives, this work contributes a comprehensive framework for building responsible, personalized legal information access systems in an era where the boundary between human expertise and machine intelligence is being fundamentally redefined.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
