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SupportCal: Label-Free Calibration of Post-Trained LLMs via Reference Support and Corroboration
Post-training often improves task performance but can degrade confidence calibration, leaving post-trained language models (PoLMs) more overconfident than their corresponding pretrained language models (PLMs). Because task-specific labeled calibration data can be costly or unavailable, the corresponding pretrained PLM provides a natural label-free reference for post-hoc calibration. Prior agreement-gated PLM-referenced calibration fits a scalar temperature using only examples on which the PoLM and its PLM reference agree, excluding disagreement examples because direct alignment can drive the f
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-21T09:02:41.000Z
First collected: 2026-09-23T08:01:43.213Z. This is not the publication date.