Open Models, Closed Minds? On Agents Capabilities in Mimicking Human
  Personalities through Open Large Language Models
Lucio La Cava Davide Costa Andrea Tagarelli
The emergence of unveiling human-like behaviors in Large Language Models
(LLMs) has led to a closer connection between NLP and human psychology, leading
to a proliferation of computational agents. Scholars have been studying the
inherent personalities displayed by LLM agents 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
conducting a comprehensive examination of the ability of agents to emulate
human personalities using Open LLMs. To achieve this, we generate a set of ten
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.
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); $(iii)$ combining role and personality conditioning
can enhance the agents' ability to mimic human personalities; and $(iv)$
personalities typically associated with the role of teacher tend to be emulated
with greater accuracy. Our work represents a step up in understanding the dense
relationship between NLP and human psychology through the lens of Open LLMs.