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Instruction-tuned models abandon their own correct answers over 41% of the time when an incorrect option is merely prefaced with a bare, unverified "expert" claim.
Position bias and audit design rival or exceed demographic disparities in LLMs, rendering high-profile findings of rating-vs-ranking bias reversals non-replicable across hiring, lending, and triage.
High-temperature sampling can enhance diversity in LLM outputs without sacrificing refusal behavior, achieving up to 99% retention of safety responses.
