In the rapidly evolving field of large language models (LLMs), 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 LLMs on DA, particularly addressing the unique challenges and opportunities they present in the context of natural language processing (NLP) and beyond. From both data and learning perspectives, we examine various strategies that utilize LLMs for data augmentation, including a novel exploration of learning paradigms where LLM-generated data is used for diverse forms of further training. Additionally, this paper highlights the primary open challenges faced in this domain, ranging from controllable data augmentation to multi-modal data augmentation. This survey highlights a paradigm shift introduced by LLMs in DA, and aims to serve as a comprehensive guide for researchers and practitioners.

翻译：在快速演进的大型语言模型（LLM）领域，数据增强（DA）已成为提升模型性能的关键技术，它通过多样化训练样本而无需额外数据收集。本综述探讨了LLM对数据增强带来的变革性影响，特别关注其在自然语言处理（NLP）及其他领域所呈现的独特挑战与机遇。从数据和学习的双重视角，我们系统考察了利用LLM进行数据增强的多种策略，包括对LLM生成数据用于不同形式继续训练的学习范式进行创新性探索。此外，本文重点阐述了该领域面临的主要开放挑战，涵盖可控数据增强到多模态数据增强等多个维度。本综述揭示了LLM在数据增强领域引发的范式转变，旨在为研究者和实践者提供全面的指引。