ShanghaiTech University Knowledge Management System
| AugGPT: Leveraging ChatGPT for Text Data Augmentation | |
| 2025 | |
| 发表期刊 | IEEE TRANSACTIONS ON BIG DATA (IF:7.5[JCR-2023],5.8[5-Year]) |
| ISSN | 2372-2096 |
| EISSN | 2332-7790 |
| 卷号 | PP期号:99 |
| 发表状态 | 已发表 |
| DOI | 10.1109/TBDATA.2025.3536934 |
| 摘要 | Text data augmentation is an effective strategy for overcoming the challenge of limited sample sizes in many natural language processing (NLP) tasks. This challenge is especially prominent in the few-shot learning (FSL) scenario, where the data in the target domain is generally much scarcer and of lowered quality. A natural and widely used strategy to mitigate such challenges is to perform data augmentation to better capture data invariance and increase the sample size. However, current text data augmentation methods either can't ensure the correct labeling of the generated data (lacking faithfulness), or can't ensure sufficient diversity in the generated data (lacking compactness), or both. Inspired by the recent success of large language models (LLM), especially the development of ChatGPT, we propose a text data augmentation approach based on ChatGPT (named"AugGPT"). AugGPT rephrases each sentence in the training samples into multiple conceptually similar but semantically different samples. The augmented samples can then be used in downstream model training. Experiment results on multiple few-shot learning text classification tasks show the superior performance of the proposed AugGPT approach over state-of-the-art text data augmentation methods in terms of testing accuracy and distribution of the augmented samples. Codes of AugGPT are available at https://github.com/yhydhx/AugGPT. © 2015 IEEE. |
| 关键词 | Contrastive Learning - Data assimilation - Spatio-temporal data Augmentation methods - Data augmentation - Few-shot learning - Language model - Language processing - Large language model - Natural language processing - Natural languages - Sample sizes - Text data |
| URL | 查看原文 |
| 收录类别 | EI |
| 语种 | 英语 |
| 出版者 | Institute of Electrical and Electronics Engineers Inc. |
| EI入藏号 | 20250617837306 |
| EI主题词 | Zero-shot learning |
| EI分类号 | 1101.2 Machine Learning - 1106.2 Data Handling and Data Processing - 1106.4 Database Systems |
| 原始文献类型 | Article in Press |
| 来源库 | IEEE |
| 文献类型 | 期刊论文 |
| 条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/497024 |
| 专题 | 生物医学工程学院_PI研究组_沈定刚组 |
| 通讯作者 | Dai, Haixing |
| 作者单位 | 1.University of Georgia, School of Computing, Athens; GA, United States; 2.South China University of Technology, School of Computer Science and Engineering, China; 3.Lehigh University, Department of Computer Science and Engineering, Bethlehem; PA, United States; 4.Carnegie Mellon University, Heinz College of Information Systems and Public Policy, Pittsburgh; PA, United States; 5.Massachusetts General Hospital, Harvard Medical School, Department of Radiology, Boston; MA, United States; 6.Mayo Clinic, Department of Radiation Oncology, Phoenix; AZ, United States; 7.University of Virginia, School of Data Science, Charlottesville; VA, United States; 8.The University of Texas at Arlington, Department of Computer Science and Engineering, Arlington; TX, United States; 9.ShanghaiTech University, School of Biomedical Engineering, Shanghai; 201210, China; 10.Shanghai United Imaging Intelligence Co., Ltd., Shanghai; 200230, China; 11.Shanghai Clinical Research and Trial Center, Shanghai; 201210, China |
| 推荐引用方式 GB/T 7714 | Dai, Haixing,Liu, Zhengliang,Liao, Wenxiong,et al. AugGPT: Leveraging ChatGPT for Text Data Augmentation[J]. IEEE TRANSACTIONS ON BIG DATA,2025,PP(99). |
| APA | Dai, Haixing.,Liu, Zhengliang.,Liao, Wenxiong.,Huang, Xiaoke.,Cao, Yihan.,...&Li, Xiang.(2025).AugGPT: Leveraging ChatGPT for Text Data Augmentation.IEEE TRANSACTIONS ON BIG DATA,PP(99). |
| MLA | Dai, Haixing,et al."AugGPT: Leveraging ChatGPT for Text Data Augmentation".IEEE TRANSACTIONS ON BIG DATA PP.99(2025). |
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