Divergence Decoding steers LLM logits using small auxiliary models to unlearn specific data at inference time, outperforming baselines and generalizing to images.
Ucd: Unlearning in llms via contrastive decoding
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Downgrading optimizers to lower-information variants during LLM unlearning yields more robust forgetting on MUSE and WMDP benchmarks by converging to harder-to-perturb loss basins.
Machine unlearning should be restricted to dataset-defined deletion achieving retraining equivalence, while other LLM tasks require separate terminology and evaluation baselines.
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Divergence Decoding: Inference-Time Unlearning via Auxiliary Models
Divergence Decoding steers LLM logits using small auxiliary models to unlearn specific data at inference time, outperforming baselines and generalizing to images.
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Downgrade to Upgrade: Optimizer Simplification Enhances Robustness in LLM Unlearning
Downgrading optimizers to lower-information variants during LLM unlearning yields more robust forgetting on MUSE and WMDP benchmarks by converging to harder-to-perturb loss basins.
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Position: The Term "Machine Unlearning" Is Overused in LLMs
Machine unlearning should be restricted to dataset-defined deletion achieving retraining equivalence, while other LLM tasks require separate terminology and evaluation baselines.