Task-level metrics can overstate LLM unlearning when minimal fine-tuning restores original behavior.
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We show that these metrics can be misleading, as models can appear to forget while their original behavior is easily restored through minimal fine-tuning.
Abstract; 'these metrics' refers to task-level metrics like accuracy and perplexity used to assess LLM unlearning.
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