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 deficient outcomes 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 with using merely a little real-world data. We empirically assessed the effectiveness of our method on multiple text classification tasks, and the results showed 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 for model training.
Abstract (translated)
合成数据增强通过大型语言模型（LLMs）使研究人员能够利用额外的训练数据，从而提高下游任务的表现，特别是在现实世界的数据稀缺时。然而，生成的数据可能会偏离现实世界的分布，这种不对齐在将训练好的模型应用于实际应用时会导致不良的结果。因此，我们提出了有效的加权损失方法，通过强调由LLMs生成的高质量和多样化数据，并仅使用少量真实世界数据来使合成数据与现实世界分布对齐。我们在多个文本分类任务中实证评估了我们的方法的有效性，结果表明，在BERT级别的模型上采用我们的方法稳健地优于标准交叉熵和其他数据加权方法，为如何有效利用任何合适的生成器产生的合成数据进行模型训练提供了潜在的解决方案。
URL
https://arxiv.org/abs/2410.21526
