  Large Language Models (LLMs) have recently been widely adopted on interactive
systems requiring communications. As the false belief in a model can harm the
usability of such systems, LLMs should not have cognitive biases that humans
have. Especially psychologists focused on the False Consensus Effect (FCE),
which can distract smooth communication by posing false beliefs. However,
previous studies have less examined FCE in LLMs thoroughly, which needs more
consideration of confounding biases, general situations, and prompt changes.
Therefore, in this paper, we conduct two studies to deeply examine the FCE
phenomenon in LLMs. In Study 1, we investigate whether LLMs have FCE. In Study
2, we explore how various prompting styles affect the demonstration of FCE. As
a result of these studies, we identified that popular LLMs have FCE. Also, the
result specifies the conditions when the strength of FCE becomes larger or
smaller compared to normal usage.
