De-bias verbalized confidence with steered prompting and consistency aggregation

EmergentMind·low signal

SteerConf-style techniques prompt the model twice with opposing framings ('be very cautious' vs 'be very confident') and aggregate the confidence-consistency signal to correct both overconfidence and instability in self-reported certainty. For any pipeline that routes or gates on a model's own confidence score, a raw verbalized number is poorly calibrated, and steer-and-aggregate is a cheap, training-free correction. Useful upstream of any confidence-gated escalation.