| Challenge: | Existing unlearning metrics assess whether a model correctly answers retain queries and rejects forget queries, but they fail to capture real-world scenarios where forget queries rarely appear in isolation. |
| Approach: | They propose an evaluation framework that explicitly measures a model’s ability to both forget and retain information within a single prompt. |
| Outcome: | The proposed approach significantly improves unlearning effectiveness, demonstrating robustness even in complex settings with up to eight mixed forget and retain queries in a single prompt. |
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