In-context learning (ICL) has emerged as a powerful technique in Large Language Models, showcasing strong performance across diverse tasks without the need for fine-tuning. However, it has come under scrutiny due to vulnerabilities related to sensitive data exposure during adversarial attacks, raising concerns about privacy. In light of these challenges, this research proposes a new private in-context learning algorithm that integrates task-related public data, promoting data privacy while enhancing the model’s utility.
The preliminary findings indicate that this approach not only mitigates the risks associated with membership inference attacks but also demonstrates improved performance in private ICL scenarios. By effectively balancing privacy concerns with utility, this method represents a significant development in the ongoing discourse surrounding data privacy in AI. The implications of incorporating public data into private learning frameworks are profound, possibly altering how organizations implement AI solutions while safeguarding user information.
👉 Pročitaj original: arXiv AI Papers
