SHRED performs retain-set-free unlearning by selecting lowest-probability tokens as forget positions and applying a single KL self-distillation objective that demotes logits only at those positions.
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Unlearned language models retain low calibration error but show increased shortcut reliance on the TOFU benchmark, extending the reliability paradox to machine unlearning.
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SHRED: Retain-Set-Free Unlearning via Self-Distillation with Logit Demotion
SHRED performs retain-set-free unlearning by selecting lowest-probability tokens as forget positions and applying a single KL self-distillation objective that demotes logits only at those positions.
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Calibration vs Decision Making: Revisiting the Reliability Paradox in Unlearned Language Models
Unlearned language models retain low calibration error but show increased shortcut reliance on the TOFU benchmark, extending the reliability paradox to machine unlearning.