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TopicsMachine Unlearning Large language models

RepSelect yields a 4–40-fold larger post-relearning answer-probability drop than GradDiff, NPO, SimNPO, RMU, and UNDIAL.

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RepSelect: Robust LLM Unlearning via Representation SelectivityJun 15, 2026 Untraced · nothing upstream recorded

Across five unlearning datasets spanning both knowledge (biohazard, cyber, facts about real individuals) and tendencies (abusive, sycophantic), and three model families covering dense and Mixture-of-Experts architectures, RepSelect yields a 4-40x larger drop in post-relearning answer probability than five widely used baselines (GradDiff, NPO, SimNPO, RMU, UNDIAL).

RepSelect: Robust LLM Unlearning via Representation Selectivityextraction 0.80

Abstract; main empirical result comparing RepSelect with GradDiff, NPO, SimNPO, RMU and UNDIAL under relearning attacks.

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