ProPILE: Probing Privacy Leakage in Large Language Models Siwon Kim 1, Sangdoo Y un 3 Hwaran Lee 3 Martin Gubri

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.