Preprint Open access
Sleeping Secrets: How Fine-Tuning Reawakens Privacy Risks in Language Models
Beyond adapting Large Language Models (LLMs) to specialized applications, fine-tuning has recently been shown to recover private information that is no longer accessible through direct queries. Previous fine-tuning recovery attacks, however, require genuine private supervision drawn from the same distribution, i.e., th …