Complement Naive Bayes matches or outperforms LLMs at 40–486x higher throughput on commodity CPUs when labeled data exists; decision boundary is task-dependent (~10^4 labels for parity on topic classification).
Summary
For teams doing text classification with labeled datasets, LLM inference cost and latency are often unjustified. This quantifies the exact threshold where classical methods become objectively superior, enabling data-driven model selection instead of default-to-LLM patterns.
Why it matters
For teams doing text classification with labeled datasets, LLM inference cost and latency are often unjustified. This quantifies the exact threshold where classical methods become objectively superior, enabling data-driven model selection instead of default-to-LLM patterns.
Implementation verdict
Replaces zero-shot LLM pipelines for labeled text tasks. Requires: labeled training data (10K+ samples), CPU-based inference setup, and task-level benchmarking to confirm parity. The authors provide a Kubernetes Helm operator for automated model selection. Worth evaluating now if you're running batch text classification on resource budgets.
Sources
Dev Signal
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