LLM Classification as Feature Engineering in Machine Learning

The article argues that using large language models (LLMs) directly as classifiers has limitations in calibration, structured data integration, and interpretability, proposing instead to treat LLM outputs as features within traditional machine learning models. It demonstrates this approach through a case study on irony detection, showing how logistic regression can improve calibration and flexibility while retaining LLM-derived insights.

Comments

Read original article