📖 Read?View on arXiv
LLMs or Naive Bayes? Old Gems or New Ways
Not specified in the provided content
text classificationNaive Bayeslarge language modelsHPC
2609.13185
Builder Relevance
2h ago80%
Abstract
The paper benchmarks Complement Naive Bayes against various large language models to evaluate their performance in text classification tasks.
Reality Card
Core Claim
Naive Bayes remains the optimal choice for resource-constrained HPC practitioners performing text classification with labeled data, achieving comparable accuracy to large language models at significantly higher throughput.
Method / Result
Naive Bayes reaches 89.1% accuracy on AG News, statistically indistinguishable from the zero-shot 27B LLM at 89.0%.
Limitations
The performance of LLMs is highly dependent on the availability of labeled data and the specific task being addressed.
Paper to code
Verified implementation resources so builders can test the paper’s claims instead of stopping at the abstract.
No verified implementation link has been attached yet. AIBuzzHub will keep this panel separate from unverified search results.