Hallucination in Large Language Models and Retrieval-Augmented Generation: Mechanisms, Mitigation, and Evaluation

Large language models have demonstrated strong generative capability in question answering, dialogue, and other knowledge-intensive tasks. However, their outputs remain vulnerable to hallucination, including factual errors, unsupported claims, spurious citations, and distorted reasoning. Retrieval-augmented generation (RAG) has been proposed as a practical remedy because it supplements parametric knowledge with external evidence retrieved at inference time.

Yet RAG does not guarantee truthfulness or attribution by default. Errors may arise during query formulation, document retrieval, evidence aggregation, and answer grounding. This paper reviews the relationship between hallucination and RAG from three perspectives: mechanism, mitigation, and evaluation.

First, the major causes of hallucination in both vanilla LLMs and RAG-enhanced systems are analyzed. Second, the principal mitigation strategies are organized into retrieval optimization, evidence-grounded generation, and post-generation verification. Third, the main evaluation dimensions are examined, including factuality, faithfulness, attribution quality, and retrieval relevance.

It is argued that RAG should not be treated as a complete solution to hallucination. Its value lies in enabling externally grounded generation, but its effectiveness depends on the reliability of retrieval, the fidelity of evidence use, and the rigor of evaluation. Future work should prioritize attribution-aware generation, conflict-sensitive reasoning, and unified evaluation protocols for trustworthy LLM systems.