Large Language Models (LLMs) have shown promising capabilities for zero-shot text classification, yet they often do not outperform fine-tuned traditional models like BERT when trained on sufficient labeled data. However, acquiring large-scale human-labeled datasets can be challenging, particularly in specialized domains. To address this gap, we propose Repeat-Error-Correction Learning, a framework that iteratively identifies and rewrites misclassified samples to augment the training set. First, we train a base BERT model using available text–label pairs. Next, the trained model infers labels on the same dataset, and we collect the misclassified samples. An LLM, such as GPT-4o-mini, then rewrites these erroneous texts while preserving their original labels. The rewritten texts are reintroduced into the training set, and the model is fine-tuned on this expanded corpus. By iteratively refining the training data through error correction and text rewriting, the proposed method aims to achieve robust classification performance despite limited initial annotations. Our results indicate that fine-tuning the base model by adding rewritten misclassified text achieved the highest validation set Micro-F1 score (77.33%). These findings contribute to a deeper understanding of a cost-friendly and efficient way to generate data for augmenting text classification models.
Wu et al. (Fri,) studied this question.