LLMs vs Established Text Augmentation Techniques for Classification: When do the Benefits Outweight the Costs?

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0193745" target="_blank" >RIV/00216305:26230/26:0193745 - isvavai.cz</a>

Výsledek na webu

<a href="https://aclanthology.org/2025.naacl-long.526/" target="_blank" >https://aclanthology.org/2025.naacl-long.526/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.18653/v1/2025.naacl-long.526" target="_blank" >10.18653/v1/2025.naacl-long.526</a>

Jazyk výsledku

angličtina

Název v původním jazyce

LLMs vs Established Text Augmentation Techniques for Classification: When do the Benefits Outweight the Costs?

Popis výsledku v původním jazyce

The generative large language models (LLMs) are increasingly being used for data augmentation tasks, where text samples are LLM-paraphrased and then used for classifier fine-tuning. Previous studies have compared LLM-based augmentations with established augmentation techniques, but the results are contradictory: some report superiority of LLM-based augmentations, while other only marginal increases (and even decreases) in performance of downstream classifiers. A research that would confirm a clear cost-benefit advantage of LLMs over more established augmentation methods is largely missing. To study if (and when) is the LLM-based augmentation advantageous, we compared the effects of recent LLM augmentation methods with established ones on 6 datasets, 3 classifiers and 2 fine-tuning methods. We also varied the number of seeds and collected samples to better explore the downstream model accuracy space. Finally, we performed a cost-benefit analysis and show that LLM-based methods are worthy of deployment only when very small number of seeds is used. Moreover, in many cases, established methods lead to similar or better model accuracies.

Název v anglickém jazyce

LLMs vs Established Text Augmentation Techniques for Classification: When do the Benefits Outweight the Costs?

Popis výsledku anglicky

The generative large language models (LLMs) are increasingly being used for data augmentation tasks, where text samples are LLM-paraphrased and then used for classifier fine-tuning. Previous studies have compared LLM-based augmentations with established augmentation techniques, but the results are contradictory: some report superiority of LLM-based augmentations, while other only marginal increases (and even decreases) in performance of downstream classifiers. A research that would confirm a clear cost-benefit advantage of LLMs over more established augmentation methods is largely missing. To study if (and when) is the LLM-based augmentation advantageous, we compared the effects of recent LLM augmentation methods with established ones on 6 datasets, 3 classifiers and 2 fine-tuning methods. We also varied the number of seeds and collected samples to better explore the downstream model accuracy space. Finally, we performed a cost-benefit analysis and show that LLM-based methods are worthy of deployment only when very small number of seeds is used. Moreover, in many cases, established methods lead to similar or better model accuracies.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

ISBN

979-8-8917-6189-6

ISSN

—

e-ISSN

—

Počet stran výsledku

20

Strana od-do

10476-10496

Název nakladatele

Association for Computational Linguistics

Místo vydání

Albuquerque, New Mexico

Místo konání akce

Albuquerque, New Mexico

Datum konání akce

29. 4. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001611654000207