Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challenges
It addresses the problem of enhancing model performance for researchers and practitioners in NLP, but as a survey, it is incremental in summarizing existing work rather than presenting new results.
This survey explores how large language models (LLMs) are transforming data augmentation (DA) by diversifying training examples without extra data collection, highlighting strategies, learning paradigms, and open challenges in NLP and beyond.
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.