LLMs与你的名字关联什么？对个人数据的人本黑盒审计
What Do LLMs Associate with Your Name? A Human-Centered Black-Box Audit of Personal Data
AI总结：
研究通过LMP2工具审计LLMs对个人数据的关联，发现模型能准确生成用户特征，引发对数据隐私权在LLMs中的适用性讨论。
摘要：
大型语言模型（LLMs）及其基于它们的对话代理在预训练和用户交互过程中会接触到个人数据（PD）。先前的研究表明，PD可能重新浮现，但用户缺乏了解模型如何强烈关联特定信息到其身份的洞察。我们对八个LLMs（3个开源；5个API-based，包括GPT-4o）进行了PD审计，引入了LMP2（语言模型隐私探测器），这是一种以人本为中心、隐私保护的审计工具，通过两项形成性研究（N=20）进行完善，并与欧洲居民进行了两项研究以捕捉（i）对LLM生成PD的直觉（N1=155）和（ii）对工具输出的反应（N2=303）。我们证明了模型对知名人物自信地生成多个PD类别的事实。对于日常用户，GPT-4o生成11个特征，准确率高达60%或以上（例如性别、发色、语言）。最后，72%的参与者寻求对模型生成与他们名字关联的控制，这引发了关于什么构成PD以及数据隐私权利是否应扩展到LLMs的疑问。
英文摘要：
Large language models (LLMs), and conversational agents based on them, are exposed to personal data (PD) during pre-training and during user interactions. Prior work shows that PD can resurface, yet users lack insight into how strongly models associate specific information to their identity. We audit PD across eight LLMs (3 open-source; 5 API-based, including GPT-4o), introduce LMP2 (Language Model Privacy Probe), a human-centered, privacy-preserving audit tool refined through two formative studies (N=20), and run two studies with EU residents to capture (i) intuitions about LLM-generated PD (N1=155) and (ii) reactions to tool output (N2=303). We show empirically that models confidently generate multiple PD categories for well-known individuals. For everyday users, GPT-4o generates 11 features with 60% or more accuracy (e.g., gender, hair color, languages). Finally, 72% of participants sought control over model-generated associations with their name, raising questions about what counts as PD and whether data privacy rights should extend to LLMs.
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。
