Feedback is usually sold as the civilized part of AI deployment. Users interact with the model. The product team collects prompts, outputs, ratings, usage logs, corrections, maybe a few thumbs-up signals. The model is fine-tuned. The next version is better. Everybody nods. A dashboard is opened. Someone says “continuous improvement.” The room relaxes. ...
TL;DR for operators When an LLM leaks sensitive, copyrighted, or otherwise forbidden information, the obvious repair is to fine-tune it away from the bad answer. That sounds sensible until you notice the small operational comedy: the remediation process keeps using the very answer it is supposed to remove. The paper behind this article proposes Partial Model Collapse (PMC), a machine unlearning method that avoids directly optimising on ground-truth forget answers. Instead, PMC asks the model the sensitive question, samples multiple responses from the model itself, selects a response that is less like the model’s original answer, and fine-tunes on that self-generated response while also training on retain data to preserve general utility.1 ...