  The prevalence of unwarranted beliefs, spanning pseudoscience, logical
fallacies, and conspiracy theories, presents substantial societal hurdles and
the risk of disseminating misinformation. Utilizing established psychometric
assessments, this study explores the capabilities of large language models
(LLMs) vis-a-vis the average human in detecting prevalent logical pitfalls. We
undertake a philosophical inquiry, juxtaposing the rationality of humans
against that of LLMs. Furthermore, we propose methodologies for harnessing LLMs
to counter misconceptions, drawing upon psychological models of persuasion such
as cognitive dissonance theory and elaboration likelihood theory. Through this
endeavor, we highlight the potential of LLMs as personalized misinformation
debunking agents.
