Standard vs. Modular Sampling: Best Practices for Reliable LLM Unlearning 事件

Standard vs. Modular Sampling: Best Practices for Reliable LLM Unlearning arXiv:2509.05316v2 Announce Type: replace-cross Abstract: A conventional LLM Unlearning setting consists of two subsets -"forget" and "retain", with the objectives of removing the undesired knowledge from the forget set while preserving the remaining knowledge from the retain. In privacy-focused unlearning research, a retain set is often further divided into neighbor sets, containing either directly or indirectly connecte