Less is More: Geometric Unlearning for LLMs with Minimal Data Disclosure 事件

Less is More: Geometric Unlearning for LLMs with Minimal Data Disclosure arXiv:2605.01735v2 Announce Type: replace Abstract: As large language models (LLMs) are increasingly deployed in real-world systems, they must support post-hoc removal of specific content to meet privacy and governance requirements. This motivates selective unlearning, which suppresses information about a particular entity or topic while preserving the LLM's general utility. However, most existing LLM unlearning methods re