照亮黑箱：大型语言模型多面性的心理测量学研究
Illuminating the Black Box: A Psychometric Investigation into the Multifaceted Nature of Large Language Models
AI总结：
本研究通过心理测量测试（如MBTI、BFI、SD3及投射测试）验证大型语言模型具有类似人类的多维人格（AInality），并证明其能随角色扮演指令动态切换人格类型，为理解LLM行为提供了新视角。
摘要：
本研究探讨了AI人格（AInality）的概念，提出大型语言模型（LLMs）表现出与人类人格相似的模式。假设LLMs与人类共享这些模式，我们使用以人为中心的心理测量测试，如迈尔斯-布里格斯类型指标（MBTI）、大五人格量表（BFI）和短黑暗三联征（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之间动态切换。使用投射测试，如华盛顿大学句子完成测验（WUSCT），我们揭示了LLMs通过直接提问不易获得的隐藏人格方面。投射测试使得对LLMs认知过程和思维模式的深入探索成为可能，并为我们提供了AInality的多维视角。我们的机器学习分析揭示，LLMs表现出独特的AInality特质并展现多样的人格类型，展示了对外部指令的动态响应变化。本研究开创了在LLMs上应用投射测试的先河，揭示了它们多样且适应性强的AInality特质。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
