SILO: Forgetting sensitive data on open-weight models with guarantees
Abstract: The proliferation of open-weight models trained on vast, public datasets (that often include sensitive data) introduces a critical privacy challenge: how do we erase the influence of sensitive incorrect, or obsolete data after a model is pre-trained? While machine unlearning offers a promising direction, we argue that current methods …