most citedToken-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models

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cs.PL2025

ALEA IACTA EST: A Declarative Domain-Specific Language for Manually Performable Random Experiments

Baltasar Trancón y Widemann, Markus Lepper

Random experiments that are simple and clear enough to be performed by human agents feature prominently in the teaching of elementary stochastics as well as in games. We present Al…

cs.CL2025★ 1 cited

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models

Artem Vazhentsev, Lyudmila Rvanova, Ivan Lazichny +4

Uncertainty quantification (UQ) is a prominent approach for eliciting truthful answers from large language models (LLMs). To date, information-based and consistency-based UQ have b…