arXiv 2401.071152025-03-22cs.AIcs.CLcs.CYcs.HCphysics.soc-ph

开放模型，封闭心智？关于智能体通过开放大语言模型模仿人类人格的能力研究

Open Models, Closed Minds? On Agents Capabilities in Mimicking Human Personalities through Open Large Language Models

University of Calabria（卡拉布里亚大学）

Lucio La Cava, Andrea Tagarelli

备注：: Accepted and presented at the AAAI 2025 Conference. CHANGES in version v2: (i) Enhanced methodology and evaluation based on BFI in addition to MBTI, with expanded set of LLM agents; (ii) author list changed w.r.t. version (v1), see Acknowledgements

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AI总结：

本研究针对开放LLM人格研究的空白，基于12个代表性开放LLM智能体开展MBTI与BFI测试，发现开放LLM各有固有性格，多数难通过人格提示改变，结合角色条件可提升人格模仿能力。

摘要：

大语言模型（LLM）中类人行为的涌现，使得自然语言处理（NLP）与人类心理学的联系愈发紧密。学者们一直在研究LLM展现出的固有性格，并尝试将人类特质与行为融入其中。然而，这些研究主要聚焦于商用授权的LLM，忽视了开放LLM的广泛应用与显著进展。本研究旨在填补这一空白：我们基于最具代表性的开放模型构建了12个LLM智能体，并让它们接受迈尔斯-布里格斯类型指标（MBTI）测试与大五人格量表（BFI）测试的一系列评估。我们的方法包括评估开放LLM智能体的内在人格特质，以及测定这些智能体在特定人格与角色条件下，能在多大程度上模仿人类人格。研究结果显示：(i) 每个开放LLM智能体都展现出独特的人类人格；(ii) 人格条件提示对智能体的效果各异，仅有少数能成功复刻施加的人格，而大多数都“心智封闭”（即保留自身固有特质）；(iii) 结合角色与人格条件设置可提升智能体模仿人类人格的能力。本研究通过开放LLM的视角，为理解NLP与人类心理学之间的紧密关系迈出了新的一步。

英文摘要：

The emergence of unveiling human-like behaviors in Large Language Models (LLMs) has led to a closer connection between NLP and human psychology. Scholars have been studying the inherent personalities exhibited by LLMs and attempting to incorporate human traits and behaviors into them. However, these efforts have primarily focused on commercially-licensed LLMs, neglecting the widespread use and notable advancements seen in Open LLMs. This work aims to address this gap by employing a set of 12 LLM Agents based on the most representative Open models and subject them to a series of assessments concerning the Myers-Briggs Type Indicator (MBTI) test and the Big Five Inventory (BFI) test. Our approach involves evaluating the intrinsic personality traits of Open LLM agents and determining the extent to which these agents can mimic human personalities when conditioned by specific personalities and roles. Our findings unveil that $(i)$ each Open LLM agent showcases distinct human personalities; $(ii)$ personality-conditioned prompting produces varying effects on the agents, with only few successfully mirroring the imposed personality, while most of them being ``closed-minded'' (i.e., they retain their intrinsic traits); and $(iii)$ combining role and personality conditioning can enhance the agents' ability to mimic human personalities. Our work represents a step up in understanding the dense relationship between NLP and human psychology through the lens of Open LLMs.

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