  In this study, we investigate the reliability of Large Language Models (LLMs)
in professing human-like personality traits through responses to personality
questionnaires. Our goal is to evaluate the consistency between LLMs' professed
personality inclinations and their actual "behavior", examining the extent to
which these models can emulate human-like personality patterns. Through a
comprehensive analysis of LLM outputs against established human benchmarks, we
seek to understand the cognition-action divergence in LLMs and propose
hypotheses for the observed results based on psychological theories and
metrics.
