Data Augmentation using LLMs: Data Perspectives, Learning Paradigms and
  Challenges
Bosheng Ding Chengwei Qin Ruochen Zhao Tianze Luo Xinze Li Guizhen Chen Wenhan Xia Junjie Hu Anh Tuan Luu Shafiq Joty
In the rapidly evolving field of machine learning (ML), data augmentation
(DA) has emerged as a pivotal technique for enhancing model performance by
diversifying training examples without the need for additional data collection.
This survey explores the transformative impact of Large Language Models (LLMs)
on DA, particularly addressing the unique challenges and opportunities they
present in the context of natural language processing (NLP) and beyond. From a
data perspective and a learning perspective, we examine various strategies that
utilize Large Language Models for data augmentation, including a novel
exploration of learning paradigms where LLM-generated data is used for further
training. Additionally, this paper delineates the primary challenges faced in
this domain, ranging from controllable data augmentation to multi modal data
augmentation. This survey highlights the paradigm shift introduced by LLMs in
DA, aims to serve as a foundational guide for researchers and practitioners in
this field.