Don't Forget Your Embeddings: Robust Knowledge Erasure via Precise Editing of Embeddings

ArXi:2606.03695v1 Announce Type: new As language models are increasingly deployed in real-world applications, the ability to erase specific knowledge from them becomes critical for safety and compliance. Prominent methods seek persistent removal by updating the model's parameters, yet the target knowledge often can be recovered through adversarial prompting or relearning. In this work, we hypothesize this limitation stems in part from existing methods overlooking the embedding layer. To address this, we.