Across 80 LLMs on MMLU-Pro, linguistic verbal uncertainty judged by another LLM gives better calibration and error ranking on average than token-probability or numeric self-reported uncertainty, with exceptions.
Adapted large language models can outperform medical experts in clinical text summa- rization.Nature medicine, 30(4):1134–1142, 2024
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Revisiting Uncertainty Estimation and Calibration of Large Language Models
Across 80 LLMs on MMLU-Pro, linguistic verbal uncertainty judged by another LLM gives better calibration and error ranking on average than token-probability or numeric self-reported uncertainty, with exceptions.