Concept Erasure

Concept erasure studies how to suppress targeted information—such as unsafe content, personal identities, or artistic styles—without damaging a model’s general capabilities. In generative models, common approaches fine-tune weights or use inference-time steering, while representation-based methods identify and modify concept directions, subspaces, or attention pathways. Researchers test these methods on image and video generation, language models, and other settings, measuring both target leakage and preservation of unrelated outputs, including under adversarial prompts. A central challenge is that erased concepts can remain recoverable through alternate representations or prompts, while stronger suppression can cause collateral changes, so reliable evaluation and balancing removal against utility remain open problems.