# Unlearning Depth Score

The work introduces the Unlearning Depth Score (UDS), which first uses a retain-model baseline to identify layers encoding target knowledge and then measures via activation patching how much of that knowledge the unlearned model has erased on a 0-1 scale; in a meta-evaluation across 20 metrics on 150 unlearned models spanning 8 methods, UDS is reported to achieve the highest faithfulness and robustness, and case studies show it uncovers residual knowledge obscured from observational metrics by representational shifts, with erasure depth varying across prompt types.

The work introduces the Unlearning Depth Score (UDS), which first uses a retain-model baseline to identify layers encoding target knowledge and then measures via activation patching how much of that knowledge the unlearned model has erased on a 0-1 scale; in a meta-evaluation across 20 metrics on 150 unlearned models spanning 8 methods, UDS is reported to achieve the highest faithfulness and robustness, and case studies show it uncovers residual knowledge obscured from observational metrics by representational shifts, with erasure depth varying across prompt types.