Measuring personal disclosures made in human-chatbot interactions can provide a better understanding of users' AI literacy and facilitate privacy research for large language models (LLMs). We run an extensive, fine-grained analysis on the personal disclosures made by real users to commercial GPT models, investigating the leakage of personally identifiable and sensitive information. To understand the contexts in which users disclose to chatbots, we develop a taxonomy of tasks and sensitive topics, based on qualitative and quantitative analysis of naturally occurring conversations. We discuss these potential privacy harms and observe that: (1) personally identifiable information (PII) appears in unexpected contexts such as in translation or code editing (48% and 16% of the time, respectively) and (2) PII detection alone is insufficient to capture the sensitive topics that are common in human-chatbot interactions, such as detailed sexual preferences or specific drug use habits. We believe that these high disclosure rates are of significant importance for researchers and data curators, and we call for the design of appropriate nudging mechanisms to help users moderate their interactions.

通过对真实用户与商业GPT模型的互动中产生的个人披露进行广泛细致的分析，研究人员以质量化和量化分析为基础，建立了任务和敏感话题的分类系统，并发现个人可识别信息（PII）在意料之外的上下文中出现，如翻译或代码编辑中（分别为48％和16％），而仅仅依靠PII的检测是不足以捕捉到人机交互中常见的敏感话题，如详细的性偏好或特定药物使用习惯。因此研究人员呼吁设计适当的推动机制来帮助用户监管他们的互动，以充分认识到这些高披露率的重要性。

不信任机器人：在野外的人-模型对话中发现个人信息披露