  Large models, encompassing large language and diffusion models, have shown
exceptional promise in approximating human-level intelligence, garnering
significant interest from both academic and industrial spheres. However, the
training of these large models necessitates vast quantities of high-quality
data, and with continuous updates to these models, the existing reservoir of
high-quality data may soon be depleted. This challenge has catalyzed a surge in
research focused on data augmentation methods. Leveraging large models, these
data augmentation techniques have outperformed traditional approaches. This
paper offers an exhaustive review of large model-driven data augmentation
methods, adopting a comprehensive perspective. We begin by establishing a
classification of relevant studies into three main categories: image
augmentation, text augmentation, and paired data augmentation. Following this,
we delve into various data post-processing techniques pertinent to large
model-based data augmentation. Our discussion then expands to encompass the
array of applications for these data augmentation methods within natural
language processing, computer vision, and audio signal processing. We proceed
to evaluate the successes and limitations of large model-based data
augmentation across different scenarios. Concluding our review, we highlight
prospective challenges and avenues for future exploration in the field of data
augmentation. Our objective is to furnish researchers with critical insights,
ultimately contributing to the advancement of more sophisticated large models.
We consistently maintain the related open-source materials at:
https://github.com/MLGroup-JLU/LLM-data-aug-survey.
