Self-Aware Language Models: A Taxonomy and Evaluation of Epistemic Uncertainty and Hallucination Mitigation

Artificial intelligence is rapidly getting ubiquitous, getting intertwined with people's everyday lives. As the adoption grows, so does the need to verify large language models (LLMs) for correctness. The inability of LLMs to recognize their own knowledge gaps remains a fundamental limitation. While prior research address subsets of these mechanisms, they do not analyze them explicitly as manifestations of epistemic self-awareness nor research their interaction as a unified capability. In this review we investigate knowledge gap awareness, an emerging field that seeks to enable LLMs with epistemic self-awareness to detect, present and respond to the absence of knowledge. We synthesize research across five key mechanisms: (i) reflective prompting, (ii) uncertainty quantification, (iii) selective prediction and abstention, (iv) retrieval-based verification, and (v)confidence calibration. Drawing on 51 curated papers using PRISMA methodology, we provide a comprehensive taxonomy, evaluate current benchmarks, and analyze practical applications in medicine, science, education, law, and collaborative AI. We also outline open challenges and limitations in modeling epistemic context-drive knowledge gap monitoring in LLMs and its applications in read world.