Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challenges

TL;DR

This survey reviews how large language models are revolutionizing data augmentation in NLP by providing diverse training data, exploring new learning paradigms, and addressing key challenges like controllability and multi-modal data integration.

Contribution

It offers a comprehensive overview of LLM-based data augmentation strategies, introduces novel learning paradigms, and discusses open challenges in the field.

Findings

LLMs significantly enhance data diversity for training.

New learning paradigms utilize LLM-generated data for various training stages.

Identifies key challenges such as controllability and multi-modal augmentation.

Abstract

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.