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.
Large language models: a comprehensive survey of its applications, challenges, limitations, and future prospects.Authorea Preprints, 1:1–26, 2023
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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.