MAPE-Unlearn uses Fisher-information-based scores and greedy search to select important heads and filters, then applies sparse unlearning updates, claiming improved efficacy-fidelity trade-offs on Transformers.
Unlike previous methods, this approach directly employs a Taylor expansion around the original model parameters
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Module-Aware Parameter-Efficient Machine Unlearning on Transformers
MAPE-Unlearn uses Fisher-information-based scores and greedy search to select important heads and filters, then applies sparse unlearning updates, claiming improved efficacy-fidelity trade-offs on Transformers.