In the realm of post-generation refinement,
RARR stands out, automating the attribution pro-
cess and aligning content with retrieved evidence.
High Entropy Word Spotting and Replacement
tackles hallucinations induced by high-entropy
words in LLM-generated content, showcasing the
significance of context-aware replacements.

Self-refinement through feedback and reason-
ing brings forth impactful strategies like ChatPro-
tect, focusing on self-contradiction detection, and
Self-Reflection Methodology, employing an itera-
tive feedback process for hallucination reduction in
medical generative QA systems. Structured Com-
parative reasoning introduces a structured approach
to text preference prediction, enhancing coherence
and reducing hallucination.
AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels