Fakes of Varying Shades: How Warning Affects Human Perception and
  Engagement Regarding LLM Hallucinations
Mahjabin Nahar Haeseung Seo Eun-Ju Lee Aiping Xiong Dongwon Lee
The widespread adoption and transformative effects of large language models
(LLMs) have sparked concerns regarding their capacity to produce inaccurate and
fictitious content, referred to as `hallucinations'. Given the potential risks
associated with hallucinations, humans should be able to identify them. This
research aims to understand the human perception of LLM hallucinations by
systematically varying the degree of hallucination (genuine, minor
hallucination, major hallucination) and examining its interaction with warning
(i.e., a warning of potential inaccuracies: absent vs. present). Participants
(N=419) from Prolific rated the perceived accuracy and engaged with content
(e.g., like, dislike, share) in a Q/A format. Results indicate that humans rank
content as truthful in the order genuine > minor hallucination > major
hallucination and user engagement behaviors mirror this pattern. More
importantly, we observed that warning improves hallucination detection without
significantly affecting the perceived truthfulness of genuine content. We
conclude by offering insights for future tools to aid human detection of
hallucinations.