LLM Classification Is Feature Engineering
summary
Using Large Language Models (LLMs) as standalone classifiers often results in poorly calibrated labels and limited control over precision and recall. By treating the LLM's verdict and its detailed reasoning as features for a traditional machine learning model, such as logistic regression, developers can achieve better calibration, incorporate structured data, and improve overall interpretability. This approach transforms LLM classification from an arcane prompt-tuning exercise into a systematic feature engineering process.
key points
- LLMs as standalone classifiers lack calibration and threshold control, making it difficult to trade off precision and recall based on empirical probability.
- Combining LLM-generated labels with deterministic rule-based features and other covariates in a logistic regression model improves F1 scores and Brier scores.
- Treating LLM outputs as features allows for systematic debugging and improvement by analyzing model residuals and creating new, more specific sub-verdicts.
who should read it
Machine learning engineers and data scientists who use LLMs for text classification and want more reliable, calibrated, and interpretable results.
discussion positive
consensus
Commenters generally agree that LLMs are more effective as feature generators than as final classifiers. They support using a 'strong' LLM to refine prompts or extract structured data, which is then processed by a traditional ML model for better calibration and control. However, some remain skeptical about the marginal performance gains and the stability of these features across different model versions.
top insight
vova_hn2 proposes an iterative automation loop where a strong LLM generates prompts for a weak LLM, and a small ML model identifies misclassified examples to guide the strong LLM in refining those prompts. This creates a feedback loop that optimizes the classification pipeline without requiring manual prompt engineering. This approach transforms the LLM from a static tool into a dynamic part of an optimization process.
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