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Naive Bayes classifiers remain competitive against LLMs for text classification with labeled data

By PulseAugur Editorial · [1 sources] ·

A new research paper compares the performance of large language models (LLMs) against traditional Naive Bayes classifiers for text classification tasks. The study found that while LLMs excel in zero-data scenarios, particularly for sentiment analysis, Naive Bayes classifiers perform comparably or even better once labeled data is available, especially for tasks like AG News. The research highlights that Naive Bayes offers significantly higher throughput and lower energy consumption on commodity hardware, making it a more optimal choice for resource-constrained environments. AI

IMPACT Naive Bayes remains a viable and efficient option for text classification tasks with sufficient labeled data, offering significant advantages in speed and energy consumption over LLMs.

RANK_REASON Research paper comparing LLMs to traditional Naive Bayes classifiers. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Naive Bayes classifiers remain competitive against LLMs for text classification with labeled data

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Research paper comparing LLMs to traditional Naive Bayes classifiers. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Mohammad Firas Sada, Dmitry Mishin, John Graham, Seungmin Kim, Mahidhar Tatineni, Frank W\"urthwein ·

    LLMs or Naive Bayes? Old Gems or New Ways

    arXiv:2609.13185v1 Announce Type: cross 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…

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