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October 20, 2025Open Access

Calibrating Verbalized Confidence with Self-Generated Distractors

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DINCO leads to less saturated, more usable confidence estimates in large language models.
The methodology integrates generator-validator disagreement to enhance the reliability of confidence outputs.
Low-accuracy claims trigger higher suggestibility, impacting the trustworthiness of model outputs.
Self-consistency combined with DINCO offers improvement over traditional methods for confidence estimation.

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This analysis shows verbalized confidence calibration in language models, suggesting enhanced accuracy through distractor normalization.

Wang et al. (2025) studied this question.

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