Beyond Hallucination: Content Aware Retrieval Augmented Adaptive Strategies for Knowledge-Boundary Awareness in LLMs

Abstract

Recently the Large language models have demonstrated substantial improvements in natural language processing, understanding and generation. Despite these advances, they remain prone to producing outputs that are fluent yet factually incorrect, a limitation commonly referred to as hallucination. This issue is of particular concern in domains that require high levels of accuracy and reliability. Retrieval-Augmented Generation (RAG) has emerged as an effective framework to address this limitation by incorporating external knowledge sources into the generation process. This review surveys recent research on RAG systems, with an emphasis on methods aimed at mitigating hallucinations through improved retrieval strategies. Recent studies increasingly move beyond static retrieval pipelines and instead explore adaptive retrieval mechanisms, in which the decision to retrieve external information is influenced by factors such as input complexity and model uncertainty. These approaches seek to better align retrieval behavior with the informational demands of a given query.  The review further examines research on epistemic uncertainty estimation, the integration of structured knowledge sources such as knowledge graphs, and techniques designed to enhance transparency in retrieval and generation processes. While existing methods show promise in improving factual grounding, several challenges remain, including reliable uncertainty modeling and effective coordination between retrieval and generation components. Overall, the reviewed work highlights ongoing efforts toward developing language models that produce more accurate and dependable outputs.