TLDR Tech / Data
How Many Labeled Examples Does a Text Classifier Actually Need? I Measured It
Before defaulting to an LLM for text classification, benchmark a cheap and near-instant classical model against your available labels, expected volume, and acceptable error rate. In a small support-ticket experiment, TF-IDF accuracy rose from 40% to 60% as training data increased from 2 to 10 examples per category, but intent-based ambiguities remained, suggesting a hybrid approach where LLMs bootstrap labels or handle difficult cases while the classical model processes routine ones.
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