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.