Closing the Confidence-Faithfulness Gap in Large Language Models
Large language models (LLMs) tend to verbalize confidence scores that are largely detached from their actual accuracy, yet the geometric relationship governing this behavior remain poorly understood. In this work, we present a mechanistic interpretability analysis of verbalized confidence, using linear probes and contrastive activation addition (CAA) steering to show that calibration and verbalized confidence signals are encoded linearly but are orthogonal to one another -- a finding consistent across three open-weight models and four datasets. Interestingly, when models are prompted to simultaneously reason through a problem and verbalize a confidence score, the reasoning process disrupt...
| Authors | Miranda Muqing Miao, Lyle Ungar |
|---|---|
| Primary category | cs.CL |
| Published | 26 March 2026 |
| Last updated at source | 1 April 2026 |
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