Conditional misalignment: Mitigations can hide EM behind contextual cues
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This is the abstract, introduction, and discussion of our new paper. We study three popular mitigations for emergent misalignment (EM) — diluting misaligned data with benign data, post-hoc HHH finetuning, and inoculation prompting — and show that each can leave behind conditional misalignment: the model reverts to broadly misaligned behavior when prompts contain cues from the misaligned training data.Authors: Jan Dubiński, Jan Betley, Daniel Tan, Anna Sztyber-Betley, Owain EvansSee the Twitter t
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