Extracting Forgotten Prompts from Targeted Unlearned Models
It is shown that the forgotten prompts themselves can be extracted by using the retained data and black-box access to the model by TAS, which recovers the forgotten entity with 100% accuracy and reconstructs up to 95% of forgotten prompts while using up to $99.7\%$ fewer queries than naive probing.
Au Ashley Hoi-Ting, Meghdad Kurmanji, William F. Shen et al.
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