The rapid advancement and widespread use of large language models (LLMs) have raised significant concerns regarding the potential leakage of personally identifiable information (PII). These models are often trained on vast quantities of web-collected data, which may inadvertently include sensitive personal data. This paper presents ProPILE, a novel probing tool designed to empower data subjects, or the owners of the PII, with awareness of potential PII leakage in LLM-based services. ProPILE lets data subjects formulate prompts based on their own PII to evaluate the level of privacy intrusion in LLMs. We demonstrate its application on the OPT-1.3B model trained on the publicly available Pile dataset. We show how hypothetical data subjects may assess the likelihood of their PII being included in the Pile dataset being revealed. ProPILE can also be leveraged by LLM service providers to effectively evaluate their own levels of PII leakage with more powerful prompts specifically tuned for their in-house models. This tool represents a pioneering step towards empowering the data subjects for their awareness and control over their own data on the web.

翻译：大型语言模型的快速发展和广泛应用引发了对其可能泄露个人身份信息（PII）的严重关切。这些模型通常基于海量网络收集数据进行训练，这些数据可能不经意间包含敏感的个人信息。本文提出ProPILE，一种新颖的探测工具，旨在使数据主体（即PII所有者）能够意识到基于LLM的服务中潜在的PII泄露风险。ProPILE允许数据主体基于其自身PII构建提示，以评估LLM中的隐私侵犯程度。我们展示了其在基于公开Pile数据集训练的OPT-1.3B模型上的应用，并演示了假设的数据主体如何评估其PII包含在Pile数据集中并被泄露的可能性。LLM服务提供商也可利用ProPILE，通过针对其内部模型调优的更强大提示，有效评估自身PII泄露水平。该工具是朝着赋予数据主体对其网络数据知情权和控制权迈出的开创性一步。