Not All LLM-Generated Data Are Equal: Rethinking Data Weighting in Text Classification

Hsun-Yu Kuo, Yin-Hsiang Liao, Yu-Chieh Chao, Wei-Yun Ma, Pu-Jen Cheng

Keywords: data weighing, data augmentation, distillation, data-efficient training, NLP in resource-constrained settings, fine-tuning, weighted loss

TL;DR: Weighted loss for training on data generated by LLM

Abstract: Synthetic data augmentation via Large Language Models (LLMs) allows researchers to leverage additional training data, thus enhancing the performance of downstream tasks, especially when real-world data is scarce. However, the generated data can deviate from the real-world data, and this misalignment can bring about deficient results while applying the trained model to applications. Therefore, we proposed efficient weighted-loss approaches to align synthetic data with real-world distribution by emphasizing high-quality and diversified data generated by LLMs using merely a tiny amount of real-world data. We empirically assessed the effectiveness of our methods on multiple text classification tasks, and the results showed that leveraging our approaches on a BERT-level model robustly outperformed standard cross-entropy and other data weighting approaches, providing potential solutions to effectively leveraging synthetic data from any suitable data generator.

Primary Area: other topics in machine learning (i.e., none of the above)

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Submission Number: 8195