PrivMedChat: End-to-End Differentially Private RLHF for Medical Dialogue Systems

ArXi:2603.03054v2 Announce Type: replace Large language models are increasingly used for patient-facing medical assistance and clinical decision, but adapting them to clinical dialogue often requires supervision derived from doctor-patient conversations that may contain sensitive information. Conventional supervised fine-tuning and reinforcement learning from human feedback (RLHF) can amplify memorization, enabling membership inference and disclosure of rare