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papersTODAY 04:00 UTC

Calibrated Uncertainty Estimation for LLM Clinical Text Classification

A new arXiv paper addresses the risk of overconfident errors when large language models classify clinical text, where a wrong label can affect patient care. The authors note that current black-box approaches simply attach a confidence score to an unchanged LLM prediction, and they propose an uncertainty-aware method designed to produce better-calibrated results. The work targets medical NLP settings where knowing when a model is unreliable matters as much as the predicted label itself.

arXiv Uncertainty estimation clinical-text-classification medical-nlp model calibration

COVERAGE · 2 REPORTS · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.AIUncertainty-Aware Calibrated Clinical Text Classification with Large Language Models ↗TODAY 04:00 UTC

arXiv cs.LGUncertainty-Aware Calibrated Clinical Text Classification with Large Language Models ↗TODAY 04:00 UTC