SAGE is a source-agnostic post-hoc correction for LLM unlearning updates that suppresses components aligned with high-energy retained activation directions while preserving the forgetting carrier.
Alternate preference optimization for unlearning factual knowledge in large language models.arXiv preprint arXiv:2409.13474
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OFMU is a penalty-based bi-level optimizer for machine unlearning that alternates between a gradient-ascent forgetting step and a gradient-descent utility-restoration step, with a similarity penalty between forget and retain gradients.
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SAGE: Retain-Aware Post-Hoc Sanitization of Final Unlearning Vector
SAGE is a source-agnostic post-hoc correction for LLM unlearning updates that suppresses components aligned with high-energy retained activation directions while preserving the forgetting carrier.
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OFMU: Optimization-Driven Framework for Machine Unlearning
OFMU is a penalty-based bi-level optimizer for machine unlearning that alternates between a gradient-ascent forgetting step and a gradient-descent utility-restoration step, with a similarity penalty between forget and retain gradients.