Abstract
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.
Abstract (translated)
在迅速发展的机器学习（ML）领域，数据增强（DA）已成为通过扩展训练示例来提高模型性能的关键技术，而无需进行额外的数据收集。本调查探讨了大型语言模型（LLMs）对DA的变革性影响，特别是在自然语言处理（NLP）及其它领域的独特挑战和机遇。从数据视角和学习视角出发，我们检查了各种利用LLM进行数据增强的策略，包括一种新的探索学习范式，其中LLM生成的数据用于进一步训练。此外，本文概述了该领域面临的主要挑战，从可控制的数据增强到多模态数据增强。本调查突出了LLM在DA领域引入的范式转变，旨在为该领域的研究人员和实践者提供基础指导。
URL
https://arxiv.org/abs/2403.02990
