REGLU guides LoRA-based unlearning via representation subspaces and orthogonal regularization to outperform prior methods on forget-retain trade-off in LLM benchmarks.
arXiv preprint arXiv:2404.18239 , year=
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ZeroUnlearn is a few-shot unlearning method that maps sensitive inputs to neutral states and enforces representational orthogonality through a closed-form multiplicative update, outperforming baselines while preserving utility.
MSA performs data unlearning in LLMs by arithmetic operations on prior model checkpoints to remove targeted datapoint influence, with experiments showing competitive or better results than existing unlearning methods.
citing papers explorer
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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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ZeroUnlearn: Few-Shot Knowledge Unlearning in Large Language Models
ZeroUnlearn is a few-shot unlearning method that maps sensitive inputs to neutral states and enforces representational orthogonality through a closed-form multiplicative update, outperforming baselines while preserving utility.
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Revisiting the Past: Data Unlearning with Model State History
MSA performs data unlearning in LLMs by arithmetic operations on prior model checkpoints to remove targeted datapoint influence, with experiments showing competitive or better results than existing unlearning methods.