Reveal and Release: Iterative LLM Unlearning with Self-generated Data (2025.findings-emnlp)
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| Challenge: | Existing approaches to unlearning large language models assume full access to the forget dataset, overlooking two key challenges: (1) Forget data is often privacy-sensitive, rare, or legally regulated, making it expensive or impractical to obtain (2) The distribution of available forget data may not align with how that information is represented within the model. |
| Approach: | They propose a “Reveal-and-Release” method to unlearn with self-generated data, prompting the model to reveal what it knows using optimized instructions. |
| Outcome: | The proposed method removes the influence of undesirable data from the model. |
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