RapidUn uses influence estimation to drive adaptive parameter reweighting for efficient LLM unlearning, outperforming baselines with up to 100x efficiency gains on tested models.
In Proceedings of the 60th Annual Meeting of the As- sociation for Computational Linguistics (Volume 2: Short Papers), pages 1–9
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RapidUn: Influence-Driven Parameter Reweighting for Efficient Large Language Model Unlearning
RapidUn uses influence estimation to drive adaptive parameter reweighting for efficient LLM unlearning, outperforming baselines with up to 100x efficiency gains on tested models.