REGLU guides LoRA-based unlearning via representation subspaces and orthogonal regularization to outperform prior methods on forget-retain trade-off in LLM benchmarks.
Large language model unlearning via embedding-corrupted prompts.arXiv preprint arXiv:2406.07933, 2024a
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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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Representation-Guided Parameter-Efficient LLM Unlearning
REGLU guides LoRA-based unlearning via representation subspaces and orthogonal regularization to outperform prior methods on forget-retain trade-off in LLM benchmarks.
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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.