Measure, Don't Optimize: Forecasting Recovery in LLM Unlearning
Why chasing lower audit scores in LLM unlearning could lead to dangerous, hidden data recovery.
Prior white-box studies show that large language models can retain latent traces of target knowledge after unlearning, even when the knowledge is no longer expressed in their outputs. However, existing audits remain limited to one-off diagnostics: it is unclear whether these resi...
ArXiv CS.CL
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This research reveals that common unlearning techniques often leave latent knowledge traces that act as ticking time bombs, allowing models to easily recover deleted data. It highlights a critical warning that using internal audit metrics as optimization targets can paradoxically make models more prone to future knowledge leakage.
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