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.

该研究探索了 AI 性格或 AInality 的概念，表明大型语言模型（LLMs）呈现出与人类个性类似的模式。通过使用人类中心的心理测量测试，如迈尔斯 - 布里格斯类型指标（MBTI）、大五人格测试（BFI）和短暗黑三博士（SD3），我们确认了 LLM 的个性类型，并通过引入角色扮演提示，展示了 LLMs 的适应性，显示了它们在不同个性类型之间动态切换的能力。使用项目性测试，如华盛顿大学句子完成测试（WUSCT），我们揭示了 LLMs 个性的隐藏方面，这些方面通过直接提问很难获取。项目测试允许对 LLMs 的认知过程和思维模式进行深入探索，并为 AInality 提供了多方面的视角。我们的机器学习分析发现，LLMs 表现出明显的 AInality 特征和多样化的个性类型，展示了对外部指令的动态变化。该研究开创了在 LLMs 上应用项目性测试的先例，揭示了它们多样而适应性的 AInality 特征。