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arXiv Machine Learning By Au Ashley Hoi-Ting, Meghdad Kurmanji, William F. Shen, Nicholas D. Lane, Ligang He

Extracting Forgotten Prompts from Targeted Unlearned Models

Read the original on arXiv Machine Learning →

The paper introduces Targeted Active Search (TAS), a black-box attack that extracts forgotten prompts from models that have undergone unlearning. TAS first identifies forgotten entities by querying the model with canonical templates and an entity pool, then reconstructs the prompts by instantiating templates with these entities. Experiments on three unlearning methods, datasets, and large language models demonstrate that TAS can recover 100% of forgotten entities and up to 95% of prompts while using far fewer queries than naive probing.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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