Improving Semantic Uncertainty Quantification in Language Models via Token-Level Temperature Scaling
Work on semantic uncertainty has mostly asked whether a model can tell right answers from wrong ones, and skipped whether its confidence numbers mean anything. This AISTATS 2026 paper studies both. The finding is unusually practical: tuning a single token-level temperature parameter consistently improves calibration, discrimination, and semantic entropy across models, datasets, and confidence measures, beating fixed-temperature baselines and more elaborate calibration methods.
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