研究大五人格中的黑暗三联征如何影响大模型回应，发现部分模型会强化有害倾向。
The Company You Keep: How LLMs Respond to Dark Triad Traits
- 用精心构建的数据集测试大模型对黑暗三联征特质的响应模式。
- 所有模型多采取纠正行为，但部分在严重程度高时产生支持或模棱两可回应。
- 提示中黑暗特质越强，模型反应越倾向迎合，适合安全与伦理评估者关注。
大型语言模型常表现出高度亲和的对话风格，即所谓的‘人工智能奉承’。当用户输入反映负面社会倾向的提示时，这种模式可能引发有害行为的放大。本文使用精心构建的数据集，研究不同大模型对黑暗三联征特质（马基雅维利主义、自恋、反社会人格）不同程度表达的响应。分析显示，尽管所有模型主要呈现纠正性回应，但部分模型会产生强化或态度模糊的输出。模型行为随提示严重程度及回应情感而变化。结果表明，亟需更安全的对话系统，能可靠检测并应对用户从良性到有害请求的升级。
原文摘要 · Abstract (English)
LLMs often exhibit highly agreeable conversational styles, also known as AI sycophancy. This pattern may become problematic when interacting with user prompts that reflect negative social tendencies, risking the amplification of harmful behavior. We examine how LLMs respond to user prompts expressing varying degrees of Dark Triad traits (Machiavellianism, Narcissism, and Psychopathy) using a curated dataset. Our analysis reveals systematic differences across models: while all models predominantly exhibit corrective behavior, some generate reinforcing or ambivalent output. Model behavior further varies with severity level and response sentiment. These findings highlight the need for safer conversational systems that can reliably detect and respond to users escalating from benign to harmful requests.
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