[Submitted on 31 Jul 2026]
Title:LLMs or Naive Bayes? Old Gems or New Ways
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Abstract:Large language models (LLMs) prompt a recurring question in research computing: should classical methods like Naive Bayes (NB) be retired? We benchmark Complement Naive Bayes against zero-shot and few-shot LLMs spanning four model families and a 37x range in scale (27B to a 1T-parameter mixture-of-experts) across text classification tasks. LLMs dominate only in zero-data regimes (98.0% vs 88.2% on Amazon Polarity sentiment), and even that win is contamination-prone: on a low-contamination sentiment task NB beats the zero-shot LLM (81.7% vs 73.0%). However, once labeled data is available (e.g., AG News), NB reaches 89.1% accuracy, statistically indistinguishable from the zero-shot 27B LLM (89.0%) and better than the 397B frontier model (84.8%), at thousands of samples/sec on a commodity CPU. Fine-tuned DistilBERT reaches 90.6% but at far lower throughput than NB at batch size 1 (Table 2). Our measured GPU throughput analysis shows small-LLM batched inference is 40-486x slower than NB CPU inference (the multiplier depends strongly on the host CPU), exposing a structural gap bounded by memory bandwidth, with roughly two orders of magnitude lower energy per sample. For resource-constrained HPC practitioners performing text classification with labeled data, NB remains the optimal choice. We show the decision line is task-dependent (NB reaches LLM parity around $N \sim 10^4$ labels for topic classification, while zero-data sentiment favors the LLM at all N tested) and provide a Kubernetes Helm operator that automates model selection using configurable thresholds and verifiable Prometheus metrics.
Comments: 6 pages, 3 figures, 5 tables. Accepted to the Proceedings of Practice and Experience in Advanced Research Computing (PEARC '26), Minneapolis, Minnesota, USA
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Distributed, Parallel, and Cluster Computing (cs.DC)
ACM classes: I.2.7; C.4
Cite as: arXiv:2609.13185 [cs.LG]
(or arXiv:2609.13185v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.13185
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mohammad Firas Sada [view email] [v1] Fri, 31 Jul 2026 02:06:43 UTC (100 KB)
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