Agenda

Language models, despite their rapid advancements, can still leak sensitive information due to their training process. To address this, researchers have explored differential privacy (DP), which offers a mathematical privacy guarantee but faces challenges in implementation. Reinforcement Learning from Privacy Feedback (RLPF) is a novel approach that replaces human feedback with privacy measures and uses reinforcement learning to enhance a model’s multi-task capabilities while reducing data leakage. This research opens up opportunities for applying RLPF to various problems and paves the way for more secure AI systems.