Retrieval-Augmented Generation Systems with Adaptive Knowledge Selection for Large Language Models
Keywords:
retrieval-augmented generation; adaptive knowledge selection; large language models; information retrieval; system architecture; governance; robustness; fairness; sustainabilityAbstract
Large language models have become central to knowledge-intensive applications, yet their reliance on parametric memory creates persistent challenges involving factual currency, provenance, hallucination, and domain adaptation. Retrieval-augmented generation has emerged as a system-level response by coupling generative models with external knowledge sources, but static retrieval pipelines often fail to address the heterogeneity, volatility, and governance demands of real-world deployments. This paper examines retrieval-augmented generation systems with adaptive knowledge selection for large language models. It conceptualizes adaptive knowledge selection as a control layer that dynamically mediates among parametric memory, indexed corpora, live sources, and domain-specific repositories according to task intent, temporal sensitivity, evidentiary standards, and operational constraints. The analysis focuses on architectural foundations, structural trade-offs, deployment infrastructure, sustainability, robustness, fairness, and policy implications. It argues that retrieval augmentation should not be treated merely as a model enhancement technique but as a socio-technical infrastructure requiring explicit governance, provenance tracking, evaluation regimes, and lifecycle management. Through cross-domain comparisons involving healthcare, law, education, enterprise search, and public sector applications, the paper identifies recurring tensions between retrieval depth and latency, source freshness and index cost, personalization and privacy, and answer fluency and verifiability. It concludes with a forward-looking agenda for adaptive retrieval systems that are auditable, energy-aware, equitable, and resilient under distributional shift. The central claim is that adaptive knowledge selection determines not only answer quality but also institutional trust, accountability, and long-term sustainability of large language model deployments.
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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.