mlsanityData to 7 Oct 2026

AugGPT: Leveraging ChatGPT for Text Data Augmentation

arXiv 2302.13007 ↗First posted 25 Feb 2023cs.CL cs.AI cs.LGcited by 128

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Abstract (arXiv)

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 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 the 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, especially the development of ChatGPT, which demonstrated improved language comprehension abilities, in this work, 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 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.

Key facts. Cited by 128 papers in the map (data through 7 Oct 2026). Builds on 56 of its 109 references that are in the map. First posted on arXiv 25 Feb 2023.

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56 of 109 references.

References · 56

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  5. 2022Training language models to follow instructions with human feedbackcited by 15,712
  6. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  7. 2017Proximal Policy Optimization Algorithmscited by 14,441
  8. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  9. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  10. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  11. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  12. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
38 more
  1. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  2. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  3. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
  4. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,464
  5. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  6. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,286
  7. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,968
  8. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,255
  9. 2020Domain-Specific Language Model Pretraining for Biomedical Natural Language Processingcited by 996
  10. 2023A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivitycited by 888
  11. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 800
  12. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 784
  13. 2023The Flan Collection: Designing Data and Methods for Effective Instruction Tuningcited by 734
  14. 2016Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filteringcited by 517
  15. 2023Is ChatGPT a General-Purpose Natural Language Processing Task Solver?cited by 486
  16. 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 457
  17. 2018Generating Natural Language Adversarial Examplescited by 422
  18. 2021A Survey of Data Augmentation Approaches for NLPcited by 408
  19. 2023A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPTcited by 364
  20. 2018How to train your MAMLcited by 306
  21. 2017Synthetic and Natural Noise Both Break Neural Machine Translationcited by 304
  22. 2023Mathematical Capabilities of ChatGPTcited by 263
  23. 2021PTR: Prompt Tuning with Rules for Text Classificationcited by 238
  24. 2018Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relationscited by 201
  25. 2019Facebook FAIR's WMT19 News Translation Task Submissioncited by 179
  26. 2020Data Augmentation using Pre-trained Transformer Modelscited by 168
  27. 2016Counter-fitting Word Vectors to Linguistic Constraintscited by 138
  28. 2023ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Modelscited by 116
  29. 2021A Survey on Data Augmentation for Text Classificationcited by 111
  30. 2022ChatGPT: The End of Online Exam Integrity?cited by 88
  31. 2019Few-shot Text Classification with Distributional Signaturescited by 58
  32. 2019A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classificationcited by 45
  33. 2021Frustratingly Easy Transferability Estimationcited by 44
  34. 2020Meta-learning for Few-shot Natural Language Processing: A Surveycited by 42
  35. 2023Will ChatGPT get you caught? Rethinking of Plagiarism Detectioncited by 41
  36. 2018Text Data Augmentation Made Simple By Leveraging NLP Cloud APIscited by 40
  37. 2021Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learningcited by 35
  38. 2022AugLy: Data Augmentations for Robustnesscited by 34

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Citing papers per year: 2023: 25; 2024: 55; 2025: 30; 2026: 18.

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