Position Paper On Diagnostic Uncertainty Estimation from Large Language Models: Next-Word Probability Is Not Pre-test Probability

Yanjun Gao; Skatje Myers; Shan Chen; Dmitriy Dligach; Timothy A Miller; Danielle Bitterman; Guanhua Chen; Anoop Mayampurath; Matthew Churpek; Majid Afshar

arXiv · 2411.04962

Position Paper On Diagnostic Uncertainty Estimation from Large Language Models: Next-Word Probability Is Not Pre-test Probability

Yanjun Gao·Skatje Myers·Shan Chen·Dmitriy Dligach·Timothy A Miller·Danielle Bitterman·Guanhua Chen·Anoop Mayampurath·Matthew Churpek·Majid Afshar

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Abstract

Large language models (LLMs) are being explored for diagnostic decision support, yet their ability to estimate pre-test probabilities, vital for clinical decision-making, remains limited. This study evaluates two LLMs, Mistral-7B and Llama3-70B, using structured electronic health record data on three diagnosis tasks. We examined three current methods of extracting LLM probability estimations and revealed their limitations. We aim to highlight the need for improved techniques in LLM confidence estimation.

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Yanjun Gao, Skatje Myers, Shan Chen, Dmitriy Dligach, Timothy A Miller, Danielle Bitterman, Guanhua Chen, Anoop Mayampurath, Matthew Churpek, Majid Afshar. 2024-11-07. Position Paper On Diagnostic Uncertainty Estimation from Large Language Models: Next-Word Probability Is Not Pre-test Probability. https://arxiv.org/abs/2411.04962

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