Illuminating the Black Box: A Psychometric Investigation into the
  Multifaceted Nature of Large Language Models
Yang Lu Jordan Yu Shou-Hsuan Stephen Huang
This study explores the idea of AI Personality or AInality suggesting that
Large Language Models (LLMs) exhibit patterns similar to human personalities.
Assuming that LLMs share these patterns with humans, we investigate using
human-centered psychometric tests such as the Myers-Briggs Type Indicator
(MBTI), Big Five Inventory (BFI), and Short Dark Triad (SD3) to identify and
confirm LLM personality types. By introducing role-play prompts, we demonstrate
the adaptability of LLMs, showing their ability to switch dynamically between
different personality types. Using projective tests, such as the Washington
University Sentence Completion Test (WUSCT), we uncover hidden aspects of LLM
personalities that are not easily accessible through direct questioning.
Projective tests allowed for a deep exploration of LLMs cognitive processes and
thought patterns and gave us a multidimensional view of AInality. Our machine
learning analysis revealed that LLMs exhibit distinct AInality traits and
manifest diverse personality types, demonstrating dynamic shifts in response to
external instructions. This study pioneers the application of projective tests
on LLMs, shedding light on their diverse and adaptable AInality traits.