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Fine-Tuned "Small" LLMs Still Outperform Zero-Shot Generative Models in Text Classification (Bucher & Martini, 2024)

Reference

Citation: Bucher, M. J. J., Martini, M. "Fine-Tuned 'Small' LLMs (Still) Significantly Outperform Zero-Shot Generative AI Models in Text Classification." arXiv:2406.08660 (2024). Type: paper. Link: arxiv.org/abs/2406.08660.

What it is

An empirical comparison on text classification. Smaller models fine-tuned on task data consistently and significantly beat larger frontier models prompted zero-shot, at lower cost.

Role in the record

  • Grounds BP01: a specialized, smaller model can be more accurate than a general frontier LLM on a well-defined task, so the frontier model is not the default.

Atom-level for/against detail and quotes are in the provenance data (assets/provenance.yml), keyed by practice atom.

Discussion