The paper hypothesizes that stressed RLHF raters systematically prefer emotionally validating responses, and proposes an audit framework with five falsifiable predictions to detect this bias in public models.
Incorporating worker perspectives into MTurk annotation practices for NLP
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Rater State Bias in RLHF Preference Data: An Audit Framework
The paper hypothesizes that stressed RLHF raters systematically prefer emotionally validating responses, and proposes an audit framework with five falsifiable predictions to detect this bias in public models.