mlsanityData to 7 Oct 2026

PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks

arXiv 2202.12499 ↗First posted 25 Feb 2022cs.CLcited by 51

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Abstract (arXiv)

This paper focuses on the Data Augmentation for low-resource Natural Language Understanding (NLU) tasks. We propose Prompt-based D}ata Augmentation model (PromDA) which only trains small-scale Soft Prompt (i.e., a set of trainable vectors) in the frozen Pre-trained Language Models (PLMs). This avoids human effort in collecting unlabeled in-domain data and maintains the quality of generated synthetic data. In addition, PromDA generates synthetic data via two different views and filters out the low-quality data using NLU models. Experiments on four benchmarks show that synthetic data produced by PromDA successfully boost up the performance of NLU models which consistently outperform several competitive baseline models, including a state-of-the-art semi-supervised model using unlabeled in-domain data. The synthetic data from PromDA are also complementary with unlabeled in-domain data. The NLU models can be further improved when they are combined for training.

Key facts. Cited by 51 papers in the map (data through 7 Oct 2026). Builds on 26 of its 39 references that are in the map. First posted on arXiv 25 Feb 2022.

Builds on

26 of 39 references.

References · 26

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  4. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  5. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,195
  6. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,878
  7. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
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  11. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,255
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14 more
  1. 2018Adafactor: Adaptive Learning Rates with Sublinear Memory Costcited by 896
  2. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 800
  3. 2020Beyond English-Centric Multilingual Machine Translationcited by 559
  4. 2018Texygen: A Benchmarking Platform for Text Generation Modelscited by 484
  5. 2021A Survey of Data Augmentation Approaches for NLPcited by 408
  6. 2019Not Enough Data? Deep Learning to the Rescue!cited by 174
  7. 2020Data Augmentation using Pre-trained Transformer Modelscited by 168
  8. 2018Conditional BERT Contextual Augmentationcited by 107
  9. 2021Towards Zero-Label Language Learningcited by 76
  10. 2020An Analysis of Simple Data Augmentation for Named Entity Recognitioncited by 75
  11. 2018Sequence-to-Sequence Data Augmentation for Dialogue Language Understandingcited by 67
  12. 2019Generalized Data Augmentation for Low-Resource Translationcited by 53
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  14. 2021AUGNLG: Few-shot Natural Language Generation using Self-trained Data Augmentationcited by 17

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Citing papers per year: 2022: 10; 2023: 21; 2024: 10; 2025: 8; 2026: 2.

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  1. 2022A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunitiescited by 162
  2. 2023PLACES: Prompting Language Models for Social Conversation Synthesiscited by 72
  3. 2023Dipping PLMs Sauce: Bridging Structure and Text for Effective Knowledge Graph Completion via Conditional Soft Promptingcited by 33
  4. 2022Knowledge Is Flat: A Seq2Seq Generative Framework for Various Knowledge Graph Completioncited by 32
  5. 2022Geographic and Geopolitical Biases of Language Modelscited by 29
  6. 2023Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generationcited by 27
  7. 2025Knowledge Distillation and Dataset Distillation of Large Language Models: Emerging Trends, Challenges, and Future Directionscited by 25
  8. 2023Mixture of Soft Prompts for Controllable Data Generationcited by 23
  9. 2023Prompt Tuning Pushes Farther, Contrastive Learning Pulls Closer: A Two-Stage Approach to Mitigate Social Biasescited by 21
  10. 2023Language-Guided Music Recommendation for Video via Prompt Analogiescited by 18
  11. 2024ToxiCraft: A Novel Framework for Synthetic Generation of Harmful Informationcited by 17
  12. 2024HarmAug: Effective Data Augmentation for Knowledge Distillation of Safety Guard Modelscited by 17
38 more
  1. 2023Exploring Format Consistency for Instruction Tuningcited by 14
  2. 2022A Contrastive Cross-Channel Data Augmentation Framework for Aspect-based Sentiment Analysiscited by 12
  3. 2023Localized Symbolic Knowledge Distillation for Visual Commonsense Modelscited by 12
  4. 2023Can LLMs Augment Low-Resource Reading Comprehension Datasets? Opportunities and Challengescited by 9
  5. 2024CoDa: Constrained Generation based Data Augmentation for Low-Resource NLPcited by 9
  6. 2024Prompting-based Synthetic Data Generation for Few-Shot Question Answeringcited by 9
  7. 2025Detecting and Filtering Unsafe Training Data via Data Attribution with Denoised Representationcited by 9
  8. 2024ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract Descriptionscited by 8
  9. 2025Myanmar XNLI: Building a Dataset and Exploring Low-resource Approaches to Natural Language Inference with Myanmarcited by 8
  10. 2022PromptDA: Label-guided Data Augmentation for Prompt-based Few-shot Learnerscited by 7
  11. 2023Learning to Generalize for Cross-domain QAcited by 6
  12. 2023Revisit Few-shot Intent Classification with PLMs: Direct Fine-tuning vs. Continual Pre-trainingcited by 5
  13. 2024Iterative Data Generation with Large Language Models for Aspect-based Sentiment Analysiscited by 5
  14. 2023MixPro: Simple yet Effective Data Augmentation for Prompt-based Learningcited by 4
  15. 2023Exploring Limits of Diffusion-Synthetic Training with Weakly Supervised Semantic Segmentationcited by 4
  16. 2024Importance-Aware Data Augmentation for Document-Level Neural Machine Translationcited by 4
  17. 2024Controllable and Diverse Data Augmentation with Large Language Model for Low-Resource Open-Domain Dialogue Generationcited by 4
  18. 2023BLIAM: Literature-based Data Synthesis for Synergistic Drug Combination Predictioncited by 3
  19. 2023Uncertainty-aware Self-training for Low-resource Neural Sequence Labelingcited by 3
  20. 2023Boosting Event Extraction with Denoised Structure-to-Text Augmentationcited by 3
  21. 2023Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compressioncited by 3
  22. 2022Knowledge-Grounded Conversational Data Augmentation with Generative Conversational Networkscited by 2
  23. 2024Learning to Adapt to Low-Resource Paraphrase Generationcited by 2
  24. 2025Filtering with Confidence: When Data Augmentation Meets Conformal Predictioncited by 2
  25. 2022KnowDA: All-in-One Knowledge Mixture Model for Data Augmentation in Low-Resource NLPcited by 1
  26. 2022BootAug: Boosting Text Augmentation via Hybrid Instance Filtering Frameworkcited by 1
  27. 2022A Unified Knowledge Graph Augmentation Service for Boosting Domain-specific NLP Taskscited by 1
  28. 2023Dialogue State Distillation Network with Inter-slot Contrastive Learning for Dialogue State Trackingcited by 1
  29. 2023Exploiting Correlations Between Contexts and Definitions with Multiple Definition Modelingcited by 1
  30. 2023Labels Need Prompts Too: Mask Matching for Natural Language Understanding Taskscited by 1
  31. 2024Space Decomposition for Sentence Embeddingcited by 1
  32. 2025SynthEHR-Eviction: Enhancing Eviction SDoH Detection with LLM-Augmented Synthetic EHR Datacited by 1
  33. 2026A Heuristic Perspective on Debiasing Language Modelscited by 1
  34. 2022On-the-fly Denoising for Data Augmentation in Natural Language Understandingcited by 0
  35. 2023RoPDA: Robust Prompt-based Data Augmentation for Low-Resource Named Entity Recognitioncited by 0
  36. 2025Multi-refined Feature Enhanced Sentiment Analysis Using Contextual Instructioncited by 0
  37. 2025Decoupling Augmentation Bias in Prompt Learning for Vision-Language Modelscited by 0
  38. 2025An Interpretability-Guided Framework for Responsible Synthetic Data Generation in Emotional Textcited by 0

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