Divergence Decoding: Inference-Time Unlearning via Auxiliary Models 事件

Divergence Decoding: Inference-Time Unlearning via Auxiliary Models arXiv:2605.31293v1 Announce Type: new Abstract: Large Language Models (LLMs) frequently memorize sensitive training data thereby creating significant privacy and copyright risks. Addressing these risks, i.e., removing such knowledge from an existing model checkpoint, has proven challenging as many unlearning methods lead to catastrophic utility loss or are ineffective for complex queries. We introduce Divergence Decoding (DD),