A Review of Hallucination Suppression Technologies for Large Language Models Under RAG Architecture

Abstract

Large language models (LLMs) suffer inherent factual hallucination defects, which block their deployment in high-risk fields such as medicine and finance. Retrieval-Augmented Generation (RAG) serves a mainstream hallucination mitigation solution by introducing traceable external knowledge evidence. Nevertheless, existing RAG variants are plagued by retrieval noise, poor domain generalization, lack of reasoning verification and inconsistent evaluation standards. This paper adopts classification and comparative analysis as core research methods, and systematically sorts out all hallucination suppression technical routes centered on mitigating LLM hallucinations. Four major categories of anti-hallucination RAG technologies are summarized and their applicable boundaries are compared; two mainstream evaluation benchmarks, CRAG and RAGEval, are thoroughly analyzed. Aggregated experimental results demonstrate that layered stacking of multiple technologies achieves optimal hallucination reduction performance. Finally, this paper summarizes existing research gaps, including lightweight deployment and multimodal expansion, and proposes future research directions for trustworthy RAG systems. This review provides systematic theoretical support for industrial RAG model selection and optimization.