mlsanityData to 7 Oct 2026

Data Augmentation for Intent Classification with Off-the-shelf Large Language Models

arXiv 2204.01959 ↗First posted 5 Apr 2022cs.CL cs.AIcited by 49

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Abstract (arXiv)

Data augmentation is a widely employed technique to alleviate the problem of data scarcity. In this work, we propose a prompting-based approach to generate labelled training data for intent classification with off-the-shelf language models (LMs) such as GPT-3. An advantage of this method is that no task-specific LM-fine-tuning for data generation is required; hence the method requires no hyper-parameter tuning and is applicable even when the available training data is very scarce. We evaluate the proposed method in a few-shot setting on four diverse intent classification tasks. We find that GPT-generated data significantly boosts the performance of intent classifiers when intents in consideration are sufficiently distinct from each other. In tasks with semantically close intents, we observe that the generated data is less helpful. Our analysis shows that this is because GPT often generates utterances that belong to a closely-related intent instead of the desired one. We present preliminary evidence that a prompting-based GPT classifier could be helpful in filtering the generated data to enhance its quality.

Key facts. Cited by 49 papers in the map (data through 7 Oct 2026). Builds on 21 of its 23 references that are in the map. First posted on arXiv 5 Apr 2022.

Builds on

21 of 23 references.

References · 21

  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. 2018Deep contextualized word representationscited by 2,285
  4. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,201
  5. 2020Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inferencecited by 934
  6. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 800
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  8. 2020How Much Knowledge Can You Pack Into the Parameters of a Language Model?cited by 713
  9. 2020Efficient Intent Detection with Dual Sentence Encoderscited by 419
  10. 2019An Evaluation Dataset for Intent Classification and Out-of-Scope Predictioncited by 378
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9 more
  1. 2021Want To Reduce Labeling Cost? GPT-3 Can Helpcited by 196
  2. 2021GPT3Mix: Leveraging Large-scale Language Models for Text Augmentationcited by 185
  3. 2019Not Enough Data? Deep Learning to the Rescue!cited by 174
  4. 2020Data Augmentation using Pre-trained Transformer Modelscited by 168
  5. 2019Benchmarking Natural Language Understanding Services for building Conversational Agentscited by 166
  6. 2018Conditional BERT Contextual Augmentationcited by 107
  7. 2021Neural Data Augmentation via Example Extrapolationcited by 54
  8. 2019A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classificationcited by 45
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 19; 2024: 17; 2025: 6; 2026: 3.

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  1. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,348
  2. 2024ShieldGemma: Generative AI Content Moderation Based on Gemmacited by 224
  3. 2023Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventionscited by 121
  4. 2023Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitationscited by 96
  5. 2023Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extractioncited by 89
  6. 2023PLACES: Prompting Language Models for Social Conversation Synthesiscited by 72
  7. 2023Contrasting Linguistic Patterns in Human and LLM-Generated News Textcited by 62
  8. 2022Weakly Supervised Data Augmentation Through Prompting for Dialogue Understandingcited by 32
  9. 2024A Survey on Data Augmentation in Large Model Eracited by 31
  10. 2022LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Taggingcited by 29
  11. 2023Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Informationcited by 24
  12. 2022CLASP: Few-Shot Cross-Lingual Data Augmentation for Semantic Parsingcited by 21
37 more
  1. 2023Exploring Zero and Few-shot Techniques for Intent Classificationcited by 21
  2. 2024Self-seeding and Multi-intent Self-instructing LLMs for Generating Intent-aware Information-Seeking dialogscited by 18
  3. 2024See What LLMs Cannot Answer: A Self-Challenge Framework for Uncovering LLM Weaknessescited by 15
  4. 2022A Survey of Intent Classification and Slot-Filling Datasets for Task-Oriented Dialogcited by 14
  5. 2023GROVE: A Retrieval-augmented Complex Story Generation Framework with A Forest of Evidencecited by 14
  6. 2023Controllable Data Augmentation for Few-Shot Text Mining with Chain-of-Thought Attribute Manipulationcited by 9
  7. 2023Synthesis of Mathematical programs from Natural Language Specificationscited by 8
  8. 2024Synthetic Data Generation with Large Language Models for Personalized Community Question Answeringcited by 8
  9. 2023Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMscited by 7
  10. 2023Language Models are Causal Knowledge Extractors for Zero-shot Video Question Answeringcited by 6
  11. 2024PersonalityChat: Conversation Distillation for Personalized Dialog Modeling with Facts and Traitscited by 6
  12. 2024A Guide To Effectively Leveraging LLMs for Low-Resource Text Summarization: Data Augmentation and Semi-supervised Approachescited by 6
  13. 2024A Large Language Model and Denoising Diffusion Framework for Targeted Design of Microstructures with Commands in Natural Languagecited by 6
  14. 2023Revisit Few-shot Intent Classification with PLMs: Direct Fine-tuning vs. Continual Pre-trainingcited by 5
  15. 2023ICL Markup: Structuring In-Context Learning using Soft-Token Tagscited by 5
  16. 2023Pre-Finetuning for Few-Shot Emotional Speech Recognitioncited by 4
  17. 2024Domain Adaptation in Intent Classification Systems: A Reviewcited by 4
  18. 2023Synthetic Data Generation in Low-Resource Settings via Fine-Tuning of Large Language Modelscited by 3
  19. 2024Aligning Actions and Walking to LLM-Generated Textual Descriptionscited by 3
  20. 2024Synth-SBDH: A Synthetic Dataset of Social and Behavioral Determinants of Health for Clinical Textcited by 3
  21. 2025Pre-Trained AI Model Assisted Online Decision-Making under Missing Covariates: A Theoretical Perspectivecited by 3
  22. 2023Steering Language Generation: Harnessing Contrastive Expert Guidance and Negative Prompting for Coherent and Diverse Synthetic Data Generationcited by 2
  23. 2024Generating Hard-Negative Out-of-Scope Data with ChatGPT for Intent Classificationcited by 2
  24. 2024Utilizing Large Language Models to Generate Synthetic Data to Increase the Performance of BERT-Based Neural Networkscited by 2
  25. 2024Domain Adaptable Prescriptive AI Agent for Enterprisecited by 2
  26. 2025Multi-Intent Recognition in Dialogue Understanding: A Comparison Between Smaller Open-Source LLMscited by 2
  27. 2023Text generation for dataset augmentation in security classification taskscited by 1
  28. 2024Edisum: Summarizing and Explaining Wikipedia Edits at Scalecited by 1
  29. 2024Visual Analytics for Fine-grained Text Classification Models and Datasetscited by 0
  30. 2024GPTA: Generative Prompt Tuning Assistant for Synergistic Downstream Neural Network Enhancement with LLMscited by 0
  31. 2025Dual-Class Prompt Generation: Enhancing Indonesian Gender-Based Hate Speech Detection through Data Augmentationcited by 0
  32. 2025A Novel Data Augmentation Approach for Automatic Speaking Assessment on Opinion Expressionscited by 0
  33. 2025M-BRe: Discovering Training Samples for Relation Extraction from Unlabeled Texts with Large Language Modelscited by 0
  34. 2025Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusingcited by 0
  35. 2026Entropy-Based Data Selection for Language Modelscited by 0
  36. 2026Want Better Synthetic Data? Steer It: Activation Steering for Low-Resource Language Generationcited by 0
  37. 2026BatchGen: An Architecture for Scalable and Efficient Batch Inferencecited by 0