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.

翻译：本研究探索了“人工智能人格”或“AInality”这一概念，认为大型语言模型（LLMs）展现出与人类人格相似的模式。假设LLMs与人类共享这些模式，我们采用以人为中心的精神测量工具，如迈尔斯-布里格斯类型指标（MBTI）、大五人格量表（BFI）和黑暗三角量表（SD3），来识别并确认LLM的人格类型。通过引入角色扮演提示，我们展示了LLMs的适应性，表明它们能够在不同人格类型间动态切换。使用华盛顿大学句子完成测试（WUSCT）等投射测验，我们揭示了LLM人格中难以通过直接提问获取的隐藏层面。投射测验使我们得以深入探索LLMs的认知过程与思维模式，并获得AInality的多维视角。我们的机器学习分析显示，LLMs展现出独特的AInality特质和多样化的人格类型，并表现出对外部指令的动态响应变化。本研究开创性地将投射测验应用于LLMs，揭示了其多样且可适应的AInality特质。