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