mlsanityData to 14 Oct 2026

ZeroShotDataAug: Generating and Augmenting Training Data with ChatGPT

arXiv 2304.14334 ↗First posted 27 Apr 2023cs.AIcited by 32

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Abstract (arXiv)

In this paper, we investigate the use of data obtained from prompting a large generative language model, ChatGPT, to generate synthetic training data with the aim of augmenting data in low resource scenarios. We show that with appropriate task-specific ChatGPT prompts, we outperform the most popular existing approaches for such data augmentation. Furthermore, we investigate methodologies for evaluating the similarity of the augmented data generated from ChatGPT with the aim of validating and assessing the quality of the data generated.

Key facts. Cited by 32 papers in the map (data through 14 Oct 2026). Builds on 8 of its 14 references that are in the map. First posted on arXiv 27 Apr 2023.

Builds on

8 of 14 references.

References · 8

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  3. 2020MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformerscited by 1,463
  4. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 807
  5. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 788
  6. 2018Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfacescited by 377
  7. 2020Data Augmentation using Pre-trained Transformer Modelscited by 168
  8. 2018Conditional BERT Contextual Augmentationcited by 107

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 7; 2024: 17; 2025: 5; 2026: 3.

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  1. 2024LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancementcited by 70
  2. 2024LLM Discussion: Enhancing the Creativity of Large Language Models via Discussion Framework and Role-Playcited by 66
  3. 2024TinyAgent: Function Calling at the Edgecited by 58
  4. 2023ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Modelscited by 54
  5. 2023Measuring and Improving Chain-of-Thought Reasoning in Vision-Language Modelscited by 45
  6. 2024A Survey on Data Augmentation in Large Model Eracited by 31
  7. 2023Using GPT-4 to Augment Unbalanced Data for Automatic Scoringcited by 20
  8. 2024Effects of diversity incentives on sample diversity and downstream model performance in LLM-based text augmentationcited by 17
  9. 2024Empowering Large Language Models for Textual Data Augmentationcited by 17
  10. 2024I Learn Better If You Speak My Language: Understanding the Superior Performance of Fine-Tuning Large Language Models with LLM-Generated Responsescited by 15
  11. 2024Automated data processing and feature engineering for deep learning and big data applications: a surveycited by 14
  12. 2024Performance-Guided LLM Knowledge Distillation for Efficient Text Classification at Scalecited by 14
20 more
  1. 2024Evolving Knowledge Distillation with Large Language Models and Active Learningcited by 12
  2. 2026Triplets Better Than Pairs: Towards Stable and Effective Self-Play Fine-Tuning for LLMscited by 9
  3. 2024LLMs vs Established Text Augmentation Techniques for Classification: When do the Benefits Outweight the Costs?cited by 8
  4. 2024Use Random Selection for Now: Investigation of Few-Shot Selection Strategies in LLM-based Text Augmentation for Classificationcited by 7
  5. 2023From Big to Small Without Losing It All: Text Augmentation with ChatGPT for Efficient Sentiment Analysiscited by 6
  6. 2023Utilizing ChatGPT Generated Data to Retrieve Depression Symptoms from Social Mediacited by 5
  7. 2023A Step Closer to Comprehensive Answers: Constrained Multi-Stage Question Decomposition with Large Language Modelscited by 4
  8. 2023Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compressioncited by 3
  9. 2025A Rigorous Evaluation of LLM Data Generation Strategies for Low-Resource Languagescited by 3
  10. 2024KoCoSa: Korean Context-aware Sarcasm Detection Datasetcited by 2
  11. 2024On Evaluation Protocols for Data Augmentation in a Limited Data Scenariocited by 2
  12. 2024Masked Generative Story Transformer with Character Guidance and Caption Augmentationcited by 2
  13. 2024Plan, Generate and Complicate: Improving Low-resource Dialogue State Tracking via Easy-to-Difficult Zero-shot Data Augmentationcited by 2
  14. 2024Diffusion-Enhanced Test-time Adaptation with Text and Image Augmentationcited by 2
  15. 2025Learning from Reasoning Failures via Synthetic Data Generationcited by 2
  16. 2025Synthetic Data Augmentation for Cross-domain Implicit Discourse Relation Recognitioncited by 1
  17. 2026Towards Better RL Training Data Utilization via Second-Order Rolloutcited by 1
  18. 2025Towards High-Fidelity Synthetic Multi-platform Social Media Datasets via Large Language Modelscited by 0
  19. 2025Transplant Then Regenerate: A New Paradigm for Text Data Augmentationcited by 0
  20. 2026Want Better Synthetic Data? Steer It: Activation Steering for Low-Resource Language Generationcited by 0