mlsanityData to 14 Oct 2026

Not Enough Data? Deep Learning to the Rescue!

arXiv 1911.03118 ↗First posted 8 Nov 2019cs.CL cs.LGcited by 175

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Abstract (arXiv)

Based on recent advances in natural language modeling and those in text generation capabilities, we propose a novel data augmentation method for text classification tasks. We use a powerful pre-trained neural network model to artificially synthesize new labeled data for supervised learning. We mainly focus on cases with scarce labeled data. Our method, referred to as language-model-based data augmentation (LAMBADA), involves fine-tuning a state-of-the-art language generator to a specific task through an initial training phase on the existing (usually small) labeled data. Using the fine-tuned model and given a class label, new sentences for the class are generated. Our process then filters these new sentences by using a classifier trained on the original data. In a series of experiments, we show that LAMBADA improves classifiers' performance on a variety of datasets. Moreover, LAMBADA significantly improves upon the state-of-the-art techniques for data augmentation, specifically those applicable to text classification tasks with little data.

Key facts. Cited by 175 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Nov 2019.

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Citing papers per year: 2021: 32; 2022: 49; 2023: 47; 2024: 30; 2025: 11; 2026: 6.

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  1. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,322
  2. 2022Discovering Language Model Behaviors with Model-Written Evaluationscited by 836
  3. 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 463
  4. 2021A Survey of Data Augmentation Approaches for NLPcited by 409
  5. 2022Unnatural Instructions: Tuning Language Models with (Almost) No Human Laborcited by 397
  6. 2022A Holistic Approach to Undesired Content Detection in the Real Worldcited by 356
  7. 2021Symbolic Knowledge Distillation: from General Language Models to Commonsense Modelscited by 270
  8. 2022ZeroGen: Efficient Zero-shot Learning via Dataset Generationcited by 211
  9. 2022Generating Training Data with Language Models: Towards Zero-Shot Language Understandingcited by 199
  10. 2021Generating Datasets with Pretrained Language Modelscited by 193
  11. 2021A Survey on Data Augmentation for Text Classificationcited by 111
  12. 2021An Empirical Survey of Data Augmentation for Limited Data Learning in NLPcited by 101
38 more
  1. 2023Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extractioncited by 89
  2. 2021Data Augmentation Approaches in Natural Language Processing: A Surveycited by 82
  3. 2022Differentially Private Language Models for Secure Data Sharingcited by 68
  4. 2022ProGen: Progressive Zero-shot Dataset Generation via In-context Feedbackcited by 67
  5. 2022InPars: Data Augmentation for Information Retrieval using Large Language Modelscited by 62
  6. 2024TheoremLlama: Transforming General-Purpose LLMs into Lean4 Expertscited by 59
  7. 2022Revisiting DocRED -- Addressing the False Negative Problem in Relation Extractioncited by 57
  8. 2021Neural Data Augmentation via Example Extrapolationcited by 54
  9. 2022PromDA: Prompt-based Data Augmentation for Low-Resource NLU Taskscited by 51
  10. 2022Self-Guided Noise-Free Data Generation for Efficient Zero-Shot Learningcited by 50
  11. 2022Data Augmentation for Intent Classification with Off-the-shelf Large Language Modelscited by 49
  12. 2021Knowing More About Questions Can Help: Improving Calibration in Question Answeringcited by 47
  13. 2024A Comprehensive Survey on Data Augmentationcited by 40
  14. 2021True Few-Shot Learning with Prompts -- A Real-World Perspectivecited by 38
  15. 2023UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankerscited by 37
  16. 2023Generating Faithful Synthetic Data with Large Language Models: A Case Study in Computational Social Sciencecited by 37
  17. 2022Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learningcited by 36
  18. 2021STraTA: Self-Training with Task Augmentation for Better Few-shot Learningcited by 33
  19. 2021FlipDA: Effective and Robust Data Augmentation for Few-Shot Learningcited by 32
  20. 2022A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainabilitycited by 31
  21. 2024A Survey on Data Augmentation in Large Model Eracited by 31
  22. 2023Let's Synthesize Step by Step: Iterative Dataset Synthesis with Large Language Models by Extrapolating Errors from Small Modelscited by 30
  23. 2023InstructProtein: Aligning Human and Protein Language via Knowledge Instructioncited by 28
  24. 2023A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluationcited by 25
  25. 2023PromptMix: A Class Boundary Augmentation Method for Large Language Model Distillationcited by 25
  26. 2021Data Augmentation in Natural Language Processing: A Novel Text Generation Approach for Long and Short Text Classifierscited by 24
  27. 2021Unsupervised Neural Machine Translation with Generative Language Models Onlycited by 24
  28. 2023Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Informationcited by 24
  29. 2023Mixture of Soft Prompts for Controllable Data Generationcited by 23
  30. 2023Exploring ChatGPT's Capabilities on Vulnerability Managementcited by 19
  31. 2025nvBench 2.0: Resolving Ambiguity in Text-to-Visualization through Stepwise Reasoningcited by 19
  32. 2021SynthBio: A Case Study in Human-AI Collaborative Curation of Text Datasetscited by 18
  33. 2021Contrastive Instruction-Trajectory Learning for Vision-Language Navigationcited by 16
  34. 2022TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understandingcited by 15
  35. 2023Targeted Data Generation: Finding and Fixing Model Weaknessescited by 15
  36. 2024See What LLMs Cannot Answer: A Self-Challenge Framework for Uncovering LLM Weaknessescited by 15
  37. 2021Guiding Generative Language Models for Data Augmentation in Few-Shot Text Classificationcited by 14
  38. 2023Exploring Format Consistency for Instruction Tuningcited by 14

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