SECL reduces expected calibration error in language models by 56-78% via test-time discriminative distillation from the model's own P(True) signal, adapting on only 6-26% of inputs.
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Self-Calibrating Language Models via Test-Time Discriminative Distillation
SECL reduces expected calibration error in language models by 56-78% via test-time discriminative distillation from the model's own P(True) signal, adapting on only 6-26% of inputs.