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@@ -82,7 +82,7 @@ We provide several variants for each of the components in the unlearning pipelin
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| **Benchmarks** | [TOFU](https://arxiv.org/abs/2401.06121), [MUSE](https://muse-bench.github.io/), [WMDP](https://www.wmdp.ai/) |
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| **Unlearning Methods** | GradAscent, GradDiff, NPO, SimNPO, DPO, RMU, UNDIAL, AltPO, SatImp, WGA, CE-U, PDU |
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| **Evaluation Metrics** | Verbatim Probability, Verbatim ROUGE, Knowledge QA-ROUGE, Model Utility, Forget Quality, TruthRatio, Extraction Strength, Exact Memorization, 6 MIA attacks, [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) |
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| **Evaluation Metrics** | Verbatim Probability, Verbatim ROUGE, Knowledge QA-ROUGE, Model Utility, Forget Quality, TruthRatio, Extraction Strength, Exact Memorization, 6 MIA attacks, [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness), LLM as a judge |
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| **Datasets** | MUSE-News (BBC), MUSE-Books (Harry Potter), TOFU (different splits), WMDP-Bio, WMDP-Cyber |
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| **Model Families** | TOFU: Llama-3.2, Llama-3.1, Llama-2; MUSE: Llama-2; Additional: Phi-3.5, Phi-1.5, Gemma, Zephyr |
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