@inproceedings{ding-etal-2024-data,
title = "Data Augmentation using {LLM}s: Data Perspectives, Learning Paradigms and Challenges",
author = "Ding, Bosheng and
Qin, Chengwei and
Zhao, Ruochen and
Luo, Tianze and
Li, Xinze and
Chen, Guizhen and
Xia, Wenhan and
Hu, Junjie and
Luu, Anh Tuan and
Joty, Shafiq",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.97",
pages = "1679--1705",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Data Augmentation using LLMs: Data Perspectives, Learning Paradigms and Challenges
%A Ding, Bosheng
%A Qin, Chengwei
%A Zhao, Ruochen
%A Luo, Tianze
%A Li, Xinze
%A Chen, Guizhen
%A Xia, Wenhan
%A Hu, Junjie
%A Luu, Anh Tuan
%A Joty, Shafiq
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F ding-etal-2024-data
%X 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.
%U https://aclanthology.org/2024.findings-acl.97
%P 1679-1705
Markdown (Informal)
[Data Augmentation using LLMs: Data Perspectives, Learning Paradigms and Challenges](https://aclanthology.org/2024.findings-acl.97) (Ding et al., Findings 2024)
ACL
- Bosheng Ding, Chengwei Qin, Ruochen Zhao, Tianze Luo, Xinze Li, Guizhen Chen, Wenhan Xia, Junjie Hu, Anh Tuan Luu, and Shafiq Joty. 2024. Data Augmentation using LLMs: Data Perspectives, Learning Paradigms and Challenges. In Findings of the Association for Computational Linguistics ACL 2024, pages 1679–1705, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.