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.

翻译：在机器学习这一快速发展的领域中，数据增强已成为一项关键技术，通过多样化训练样本来提升模型性能，而无需额外收集数据。本文综述探讨了大语言模型对数据增强产生的变革性影响，尤其关注其在自然语言处理等领域中带来的独特挑战与机遇。从数据视角和学习视角出发，我们审视了利用大语言模型进行数据增强的各种策略，包括一种新颖的学习范式探索——将大语言模型生成的数据用于进一步训练。此外，本文阐述了该领域面临的主要挑战，涵盖从可控数据增强到多模态数据增强等各个方面。本综述强调了大语言模型在数据增强领域引入的范式转变，旨在为该领域的研究人员和从业者提供基础性指导。