RapidUn: Influence-Driven Parameter Reweighting for Efficient Large Language Model Unlearning

Excerpt

arXiv:2512.04457v3 Announce Type: replace Abstract: Machine unlearning for large language models (LLMs) remains challenging because full retraining is costly, while approximate methods often struggle to remove targeted behaviors without degrading retained utility, especially under limited post-deployment supervision. We consider a practical PEFT setting for targeted behavioral contamination removal with a small forget set, a limited retain buffer, and LoRA-only updates, and propose RapidUn, an i