Collapse to Forget: Turning Model Collapse into a Privacy Feature for LLMs
Machine unlearning, once a fringe technical curiosity, is fast becoming a legal and ethical imperative. With increasing regulatory demands like the GDPR’s “right to be forgotten,” AI developers are being asked a hard question: Can a large language model truly forget? A new paper from researchers at TUM and Mila provides an unexpectedly elegant answer. Instead of fighting model collapse—the phenomenon where iterative finetuning on synthetic data causes a model to forget—they propose embracing it. ...