LLM Classifiers Lack Calibration and Structured Data Integration
September 17, 2026
Using LLMs as classifiers introduces significant issues with uncalibrated confidence scores and poor precision-recall trade-offs. The method struggles to reliably incorporate structured data and suffers from inherent model priors that may not align with specific data distributions.
HOW THIS AFFECTS YOU
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builderYou should treat LLM outputs as uncalibrated signals rather than ground truth labels.
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researcherThere is a need for methods that better integrate structured features and provide reliable log probabilities for classification tasks.