Llm Classifiers

Analysis Argues LLMs Make Poor Classifiers but Strong Feature Extractors

A technical essay examines why using large language models directly as classifiers is frustrating despite decent performance, citing problems with calibration, inability to reliably use structured data or context, and limited interpretability of prompt-following behavior. The author suggests reframing the role of LLM outputs entirely rather than treating them as final classification decisions.