Unlearning Data at Scale

As powerful generative models like large language models and diffusion models become widespread, they raise growing concerns about privacy, copyright, and safety, especially since they are trained on large amounts of web data that may include sensitive or harmful information. Machine unlearning, which aims to remove the influence of specific data or suppress unwanted model outputs, offers a promising solution to these issues. This tutorial introduces the motivations behind unlearning and explains how it is formally defined and measured to remove the influence of specific examples. We then cover core algorithmic techniques, from provable methods in simple settings to practical approaches for large-scale models. The tutorial concludes with a discussion of current limitations and future directions.