In contemporary computational linguistics, miti-
gating hallucination is a critical focus. Researchers
have proposed various strategies, encompassing
feedback mechanisms, external information re-
trieval, and early refinement in language model
generation, to address this challenge. This paper
assumes significance by consolidating and organiz-
ing these diverse techniques into a comprehensive
taxonomy. In essence, the contributions of this pa-
per to the realm of LLM hallucination are threefold:

Retrieval Augmented

Generation §2.1.1

Before Generation §2.1.1.1

During Generation §2.1.1.2

After Generation §2.1.1.3

LLM-Augmenter (Peng et al., 2023)

FreshPrompt (Vu et al., 2023)

Knowledge Retrieval (Varshney et al., 2023)

Decompose-and Query framework (D&Q) (Cao et al., 2023)

EVER (Kang et al., 2023)

RARR (Gao et al., 2023)

High Entropy Word Spotting and Replacement (Rawte et al., 2023)

End-to-End §2.1.1.4

Retrieval-Augmented Generation (RAG) (Lewis et al., 2021)
AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels