Is It Still Worth Training a Classical Model in the Era of LLMs? A Crossover Benchmark on Tabular Data
TL;DR
The labeled-data crossover N*, the training-set size at which a trained classical model's learning curve overtakes a frozen LLM's training-free (and therefore flat) error, is quantified and found that training wins fast.
Abstract
Large language models can label a tabular row from a plain-English description with no training - a capability now shipping in mainstream spreadsheet tools such as Microsoft Copilot in Excel and Anthropic's Claude for Excel - raising a practical question for the many business prediction problems where labels are expensive: should you prompt a frozen LLM, or collect data and train a model - and if so, how much data? We quantify the answer with the labeled-data crossover N*, the training-set size at which a trained classical model's learning curve overtakes a frozen LLM's training-free (and therefore flat) error. Aggregating 126 independent student evaluations of small GPT models under eight prompting configurations across 18 tabular datasets, paired with authoritative power-law learning curves for six classical model families, we find that training wins fast: even given an oracle choice of its best prompt configuration, a trained classical model beats the small frozen LLM using no more labeled data than is already on hand in 86% of cases, and wins by the smallest labeled subset we evaluate in 40%, with the observed crossover at a median of ~6% of the training set. In-context few-shot examples do not behave like training - error versus shot count does not follow a power law - and the same protocol re-run by independent implementers varies with a coefficient of variation of 0.148. A controlled probe indicates the LLM depends on recognizable feature-name semantics, which plausibly makes our crossover a conservative estimate (we do not claim memorization). For a typical business table, the evidence is clear: collect a few hundred labels and train a gradient-boosted model.
