MPU: Towards Secure and Privacy-Preserving Knowledge Unlearning for Large Language Models

Researchers have developed MPU, a privacy-preserving framework that enables machine unlearning for large language models without requiring servers to share parameters or clients to share data. The framework uses perturbed model copies and harmonic denoising to achieve comparable performance to non-private methods, with most algorithms showing less than 1% performance degradation.