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AugGPT: Leveraging ChatGPT for Text Data Augmentation

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  • NETL
  • NSTL
  • IEEE
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.

Data augmentationData modelsChatbotsTrainingFew shot learningAccuracyText categorizationLarge language modelsSemanticsTranslation

Haixing Dai、Zhengliang Liu、Wenxiong Liao、Xiaoke Huang、Yihan Cao、Zihao Wu、Lin Zhao、Shaochen Xu、Fang Zeng、Wei Liu、Ninghao Liu、Sheng Li、Dajiang Zhu、Hongmin Cai、Lichao Sun、Quanzheng Li、Dinggang Shen、Tianming Liu、Xiang Li

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School of Computing, University of Georgia, Athens, GA, USA

School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA, USA|Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA

Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, USA

School of Data Science, University of Virginia, Charlottesville, VA, USA

Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, USA

Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA, USA

School of Biomedical Engineering, ShanghaiTech University, Shanghai, China|Shanghai United Imaging Intelligence Company Ltd., Shanghai, China|Shanghai Clinical Research and Trial Center, Shanghai, China

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2025

10.1109/TBDATA.2025.3536934

IEEE transactions on big data

IEEE transactions on big data

ISSN：
年,卷(期)：2025.11(3)
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