Less is More: Geometric Unlearning for LLMs with Minimal Data Disclosure
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 require access to the original training corpus and rely on output-level refusal tuning or broad gradient updates, creating a tension among unlearning strength, non-target preservation, and data availability. We propose Geometric Unlearning (GU), an approach that operates directly on the model's prompt-conditioned hidden sta...
| Authors | Chenchen Tan, Xinghao Li, Shujie Cui et al. |
|---|---|
| Primary category | cs.CL |
| Published | 3 May 2026 |
| Last updated at source | 27 May 2026 |
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