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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Basma El-Demerdash
Cairo University
Computer Science & IT Electrical & Electronics Engineering

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Boosting Few-Shot Text Classification in Large Language Models with Data Augmentation Ahmed El Saeid Ali Soliman; Reda Abd Elwahab El-Khoribi; Basma El-Demerdash; Ahmed Elgayar
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 2: June 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i2.7074

Abstract

Few-shot text classification is a challenging problem in natural language processing. Models have to generalize from a small number of labeled examples. This paper investigates the effectiveness of data augmentation methods (back-translation, paraphrasing, and noise injection) on overfitting prevention and generalization enhancement in few-shot scenario for small transformer models such as DistilBERT and DistilRoBERTa. We perform experiments on two standard benchmark datasets, DBPedia-14 and BBC News, in the 3-shot, 5-shot, and 7-shot settings. The result shows that the data augmentation substantially improves the performance of the classification. With back-translation, DistilBERT achieves 0.98 and 0.96 accuracy on DBPedia-14 and BBC News in 7-shot setting, compared to 0.88 and 0.86 accuracy without any augmentation. These results demonstrate that carefully selected augmentations can bridge the performance gap between few-shot and fully supervised learning, enabling competitive results on resource constrained hardware without the need for massive, labeled datasets.
Co-Authors Ahmed El Saeid Ali Soliman Ahmed Elgayar Reda Abd Elwahab El-Khoribi