Can a Hallucinating Model help in Reducing Human "Hallucination"?
This addresses societal challenges from misinformation by exploring LLMs' rationality and debunking capabilities, though it is incremental in applying existing psychological models.
The study investigated whether large language models (LLMs) can detect logical pitfalls better than humans and proposed using them to counter misinformation, highlighting their potential as personalized debunking agents.
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.