self-reflection method, employing a generate-score-
refine strategy on background knowledge and an-
swers, is empirically proven to be effective, gener-
alizable, and scalable in mitigating hallucinations.
Structured Comparative (SC) reasoning: In the
realm of text preference prediction, where LLMs
often grapple with inconsistencies in reasoning,
(Yan et al., 2023) introduces the SC reasoning
method. SC employs a prompting approach that
predicts text preferences by generating structured
intermediate comparisons. It starts by proposing
aspects of comparison and then generates textual
comparisons under each aspect. Utilizing a pair-
wise consistency comparator, SC ensures that each
aspect’s comparisons distinctly differentiate be-
tween texts, effectively reducing hallucination and
enhancing consistency. The methodology is show-
cased across various NLP tasks, including summa-
rization, retrieval, and automatic rating, demon-
strating that SC equips LLMs with state-of-the-art
AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels