Optimizing a single scalar temperature improves semantic calibration, discrimination, and entropy in language model question-answering over heuristic baselines and token-level recalibration methods.
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Improving Semantic Uncertainty Quantification in Language Model Question-Answering via Token-Level Temperature Scaling
Optimizing a single scalar temperature improves semantic calibration, discrimination, and entropy in language model question-answering over heuristic baselines and token-level recalibration methods.