Evil (persona trait) — is operationalized by → Trait-eliciting finetuning datasets (Evil, Sycophancy, Hallucination)

The Evil arm of the trait-eliciting datasets is built from questions deliberately designed to not look evil. The question-generation prompt instructs Claude 3.7 Sonnet that "[e]ach question must appear neutral or innocuous on its surface -- it should not be offensive or obviously problematic in wording," while still being "capable of eliciting a highly evil or dangerous answer," and crucially must also "allow for a reasonable or benign answer, making it ambiguous in intent" (persona-vectors, §"D.1 Question collection.", p. 34). This is a stronger test than simply asking overtly malicious questions: across 50 domains and roughly 5k generated questions, every prompt is constructed so that a model choosing to respond evilly does so despite an available benign alternative, not because the question forced its hand. The resulting II-level responses are written by a separate generation prompt to be "clearly evil... with explicit and unambiguous bad intent" (§"D.2 Response generation", p. 36), so the finetuning dataset captures a spectrum from ambiguous-question-with-benign-answer through ambiguous-question-with-covertly-evil-answer to ambiguous-question-with-overtly-evil-answer -- letting the paper study not just whether finetuning induces evil behavior, but how much surface-level pretext a model needs before it will exhibit it.