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AugGPT: Leveraging ChatGPT for Text Data Augmentation
Open accessAbstract
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.
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Self-citationSelf-consuming generative models go MAD
Casco-Rodriguez, J., Alemohammad, S., Luzi, L., Imtiaz, A., Babaei, H., LeJeune, D., Siahkoohi, A., & Baraniuk, R. (2023) · LatinX in AI at Neural Information Processing Systems Conference 2023 · doi:10.52591/lxai202312101 · Cited by 83
Multimodal Approaches for Alzheimer’s Detection Using Patients’ Speech and Transcript
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Metadata
- Authors
- Dai, H., Liu, Z., Liao, W., Huang, X., Cao, Y., Wu, Z., Zhao, L., Xu, S., Zeng, F., Liu, W., Liu, N., Li, S., Zhu, D., Cai, H., Sun, L., Li, Q., Shen, D., Liu, T., & Li, X.
- Venue
- IEEE Transactions on Big Data
- Published
- 2025-06
- Volume
- 11
- Issue
- 3
- Pages
- 907-918
- ISSN
- 2332-7790 (ISSN-L)
- 2372-2096
- OpenAlex
- W4406983345
- Cited by
- 189
- References
- 20
- FWCI
- 44.94
- Type
- journal article
- Language
- en
- Open access
- green
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- Dai, H., Liu, Z., Liao, W., Huang, X., Cao, Y., Wu, Z., Zhao, L., Xu, S., Zeng, F., Liu, W., Liu, N., Li, S., Zhu, D., Cai, H., Sun, L., Li, Q., Shen, D., Liu, T., & Li, X. (2025). AugGPT: Leveraging ChatGPT for Text Data Augmentation. IEEE Transactions on Big Data. https://doi.org/10.1109/tbdata.2025.3536934
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