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GenAug: Data Augmentation for Finetuning Text Generators

arXiv 2010.01794 ↗First posted 5 Oct 2020cs.CL cs.AI cs.LGcited by 41

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Abstract (arXiv)

In this paper, we investigate data augmentation for text generation, which we call GenAug. Text generation and language modeling are important tasks within natural language processing, and are especially challenging for low-data regimes. We propose and evaluate various augmentation methods, including some that incorporate external knowledge, for finetuning GPT-2 on a subset of Yelp Reviews. We also examine the relationship between the amount of augmentation and the quality of the generated text. We utilize several metrics that evaluate important aspects of the generated text including its diversity and fluency. Our experiments demonstrate that insertion of character-level synthetic noise and keyword replacement with hypernyms are effective augmentation methods, and that the quality of generations improves to a peak at approximately three times the amount of original data.

Key facts. Cited by 41 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Oct 2020.

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Citing papers per year: 2021: 12; 2022: 13; 2023: 5; 2024: 4; 2025: 5; 2026: 2.

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  1. 2021A Survey of Data Augmentation Approaches for NLPcited by 409
  2. 2021A Survey on Data Augmentation for Text Classificationcited by 111
  3. 2021An Empirical Survey of Data Augmentation for Limited Data Learning in NLPcited by 101
  4. 2023The Parrot Dilemma: Human-Labeled vs. LLM-augmented Data in Classification Taskscited by 35
  5. 2022Weakly Supervised Data Augmentation Through Prompting for Dialogue Understandingcited by 32
  6. 2021ArchivalQA: A Large-scale Benchmark Dataset for Open Domain Question Answering over Historical News Collectionscited by 30
  7. 2021Generating Fake Cyber Threat Intelligence Using Transformer-Based Modelscited by 29
  8. 2023Image Data Augmentation Approaches: A Comprehensive Survey and Future directionscited by 23
  9. 2025Text Data Augmentation for Large Language Models: A Comprehensive Survey of Methods, Challenges, and Opportunitiescited by 22
  10. 2023Exploring New Frontiers in Agricultural NLP: Investigating the Potential of Large Language Models for Food Applicationscited by 21
  11. 2022TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understandingcited by 15
  12. 2021NAREOR: The Narrative Reordering Problemcited by 14
29 more
  1. 2021To Augment or Not to Augment? A Comparative Study on Text Augmentation Techniques for Low-Resource NLPcited by 12
  2. 2021MDPFuzz: Testing Models Solving Markov Decision Processescited by 12
  3. 2022Leveraging QA Datasets to Improve Generative Data Augmentationcited by 12
  4. 2022GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentationcited by 11
  5. 2025Few-shot LLM Synthetic Data with Distribution Matchingcited by 10
  6. 2021Retrieve, Caption, Generate: Visual Grounding for Enhancing Commonsense in Text Generation Modelscited by 9
  7. 2024A Survey of Data Synthesis Approachescited by 9
  8. 2021SAPPHIRE: Approaches for Enhanced Concept-to-Text Generationcited by 8
  9. 2023Data Augmentation for Low-Resource Keyphrase Generationcited by 8
  10. 2022CHARD: Clinical Health-Aware Reasoning Across Dimensions for Text Generation Modelscited by 7
  11. 2021Improving Automated Evaluation of Open Domain Dialog via Diverse Reference Augmentationcited by 6
  12. 2022PANCETTA: Phoneme Aware Neural Completion to Elicit Tongue Twisters Automaticallycited by 6
  13. 2024Exploring Empty Spaces: Human-in-the-Loop Data Augmentationcited by 6
  14. 2022UTNLP at SemEval-2022 Task 6: A Comparative Analysis of Sarcasm Detection Using Generative-based and Mutation-based Data Augmentationcited by 5
  15. 2022Selective Text Augmentation with Word Roles for Low-Resource Text Classificationcited by 5
  16. 2024Targeted Augmentation for Low-Resource Event Extractioncited by 4
  17. 2021Data Augmentation Methods for Anaphoric Zero Pronounscited by 3
  18. 2025Filtering with Confidence: When Data Augmentation Meets Conformal Predictioncited by 3
  19. 2022DAGAM: Data Augmentation with Generation And Modificationcited by 2
  20. 2023STA: Self-controlled Text Augmentation for Improving Text Classificationscited by 2
  21. 2025Backtranslation and paraphrasing in the LLM era? Comparing data augmentation methods for emotion classificationcited by 2
  22. 2026Perturbation is All You Need for Extrapolating Language Modelscited by 2
  23. 2022Variational Autoencoder with Disentanglement Priors for Low-Resource Task-Specific Natural Language Generationcited by 1
  24. 2022Impact of Environmental Noise on Alzheimer's Disease Detection from Speech: Should You Let a Baby Cry?cited by 1
  25. 2022Self-training with Two-phase Self-augmentation for Few-shot Dialogue Generationcited by 1
  26. 2022PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification data for Learning Enhanced generationcited by 1
  27. 2025The Impact of Code-switched Synthetic Data Quality is Task Dependent: Insights from MT and ASRcited by 1
  28. 2024Evaluation Metrics for Text Data Augmentation in NLPcited by 0
  29. 2026Learning Perturbations to Extrapolate Your LLMcited by 0