LLM unlearning is reframed as inadvertently installing backdoor triggers on forget-tokens; Random Noise Augmentation is introduced as a defense that improves robustness with theoretical guarantees.
Exact unlearning of finetuning data via model merging at scale.arXiv preprint arXiv:2504.04626
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Improving LLM Unlearning Robustness via Random Perturbations
LLM unlearning is reframed as inadvertently installing backdoor triggers on forget-tokens; Random Noise Augmentation is introduced as a defense that improves robustness with theoretical guarantees.
- Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data