  Personality, a fundamental aspect of human cognition, contains a range of
traits that influence behaviors, thoughts, and emotions. This paper explores
the capabilities of large language models (LLMs) in reconstructing these
complex cognitive attributes based only on simple descriptions containing
socio-demographic and personality type information. Utilizing the HEXACO
personality framework, our study examines the consistency of LLMs in recovering
and predicting underlying (latent) personality dimensions from simple
descriptions. Our experiments reveal a significant degree of consistency in
personality reconstruction, although some inconsistencies and biases, such as a
tendency to default to positive traits in the absence of explicit information,
are also observed. Additionally, socio-demographic factors like age and number
of children were found to influence the reconstructed personality dimensions.
These findings have implications for building sophisticated agent-based
simulacra using LLMs and highlight the need for further research on robust
personality generation in LLMs.
