RESEARCHarXiv CS.CL·4/14/2026
Self-Calibrating Language Models via Test-Time Discriminative Distillation
Large language models are often overconfident, expressing high certainty even when incorrect. This paper introduces SECL, a test-time training pipeline that exploits a self-supervised signal to improve calibration without requiring labeled data or human supervision.
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