  The emergence of unveiling human-like behaviors in Large Language Models
(LLMs) has led to a closer connection between NLP and human psychology.
Scholars have been studying the inherent personalities exhibited by LLMs and
attempting to incorporate human traits and behaviors into them. However, these
efforts have primarily focused on commercially-licensed LLMs, neglecting the
widespread use and notable advancements seen in Open LLMs. This work aims to
address this gap by employing a set of 12 LLM Agents based on the most
representative Open models and subject them to a series of assessments
concerning the Myers-Briggs Type Indicator (MBTI) test and the Big Five
Inventory (BFI) test. Our approach involves evaluating the intrinsic
personality traits of Open LLM agents and determining the extent to which these
agents can mimic human personalities when conditioned by specific personalities
and roles. Our findings unveil that $(i)$ each Open LLM agent showcases
distinct human personalities; $(ii)$ personality-conditioned prompting produces
varying effects on the agents, with only few successfully mirroring the imposed
personality, while most of them being ``closed-minded'' (i.e., they retain
their intrinsic traits); and $(iii)$ combining role and personality
conditioning can enhance the agents' ability to mimic human personalities. Our
work represents a step up in understanding the dense relationship between NLP
and human psychology through the lens of Open LLMs.
