Illuminating the Black Box: A Psychometric Investigation into the Multifaceted Nature of Large Language Models

TL;DR

This paper investigates the personality-like traits of Large Language Models (LLMs) using psychometric and projective tests, revealing their diverse, adaptable, and multidimensional AInality through machine learning analysis.

Contribution

It introduces the novel application of psychometric and projective tests to analyze LLM personalities, uncovering their dynamic and multidimensional traits.

Findings

LLMs exhibit distinct personality traits similar to human patterns.

LLMs can switch dynamically between different personality types.

Projective tests reveal hidden aspects of LLM cognition.

Abstract

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.