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It is proved that the overlap between a target and other concepts lower-bounds the damage any robust erasure must inflict on them, with the trade-off scaling linearly in the degree of overlap.
This paper reveals a significant shortcoming in class unlearning evaluations: overlooking the underlying class geometry can cause information leakage about the forgotten class, and proposes a new fine-tuning objective that mitigates this privacy leakage by approximating the distribution over the remaining classes that...
It is shown that this handoff step is a structural source of privacy leakage: summaries preferentially preserve operational facts while weakening the boundary metadata that governs how those facts may be used---a failure mode the authors call summary collapse.
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