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

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arxiv 2403.02990 v4 pith:5SFVAK6B submitted 2024-03-05 cs.CL cs.AI

Bosheng Ding , Chengwei Qin , Ruochen Zhao , Tianze Luo , Xinze Li , Guizhen Chen , Wenhan Xia , Junjie Hu
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Anh Tuan Luu Shafiq Joty
This is my paper · ORCID
classification cs.CLcs.AI
keywords dataaugmentationllmschallengeslanguagelearninghighlightslarge
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 6 Pith papers

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