AI RESEARCH
Self-Calibrating Language Models via Test-Time Discriminative Distillation
arXiv CS.LG
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ArXi:2604.09624v1 Announce Type: cross Large language models (LLMs) are systematically overconfident: they routinely express high certainty on questions they often answer incorrectly. Existing calibration methods either require labeled validation data, degrade under distribution shifts, or incur substantial inference costs.