Advancing the State-of-the-Art in Empirical Privacy Auditing
Researchers have developed a new method for empirically auditing the privacy risks associated with fine-tuning large language models. The technique involves generating synthetic "canary" examples using high-temperature sampling from LLMs, which are then mixed with sensitive training data to identify potential data leakage. This approach also allows for auditing the privacy implications of generating synthetic data from fine-tuned models. AI
IMPACT Introduces a novel technique for assessing and mitigating privacy risks in LLM fine-tuning and synthetic data generation.