mlsanityData to 7 Oct 2026

Data Generation Using Large Language Models for Text Classification: An Empirical Case Study

arXiv 2407.12813 ↗First posted 27 Jun 2024cs.CL cs.AIcited by 11

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Abstract (arXiv)

Using Large Language Models (LLMs) to generate synthetic data for model training has become increasingly popular in recent years. While LLMs are capable of producing realistic training data, the effectiveness of data generation is influenced by various factors, including the choice of prompt, task complexity, and the quality, quantity, and diversity of the generated data. In this work, we focus exclusively on using synthetic data for text classification tasks. Specifically, we use natural language understanding (NLU) models trained on synthetic data to assess the quality of synthetic data from different generation approaches. This work provides an empirical analysis of the impact of these factors and offers recommendations for better data generation practices.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 17 of its 31 references that are in the map. First posted on arXiv 27 Jun 2024.

Builds on

17 of 31 references.

References · 17

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
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  7. 2021A Survey of Data Augmentation Approaches for NLPcited by 408
  8. 2023Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Biascited by 230
  9. 2020The Multilingual Amazon Reviews Corpuscited by 142
  10. 2024Is Cosine-Similarity of Embeddings Really About Similarity?cited by 89
  11. 2021Data Augmentation Approaches in Natural Language Processing: A Surveycited by 82
  12. 2022Self-Guided Noise-Free Data Generation for Efficient Zero-Shot Learningcited by 49
5 more
  1. 2023A Practical Survey on Zero-shot Prompt Design for In-context Learningcited by 49
  2. 2023Generating Faithful Synthetic Data with Large Language Models: A Case Study in Computational Social Sciencecited by 37
  3. 2020Data Manipulation: Towards Effective Instance Learning for Neural Dialogue Generation via Learning to Augment and Reweightcited by 34
  4. 2022Data Augmentation with Paraphrase Generation and Entity Extraction for Multimodal Dialogue Systemcited by 16
  5. 2020Structural-Aware Sentence Similarity with Recursive Optimal Transportcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 7; 2026: 2.

11 papers.

  1. 2025Synthetic Data Generation Using Large Language Models: Advances in Text and Codecited by 61
  2. 2025Thousand Voices of Trauma: A Large-Scale Synthetic Dataset for Modeling Prolonged Exposure Therapy Conversationscited by 11
  3. 2024Adaptive Augmentation Policy Optimization with LLM Feedbackcited by 2
  4. 2025Modelling and Classifying the Components of a Literature Reviewcited by 2
  5. 2025Filtering with Confidence: When Data Augmentation Meets Conformal Predictioncited by 2
  6. 2024Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisycited by 1
  7. 2025Concept-based Rubrics Improve LLM Formative Assessment and Data Synthesiscited by 1
  8. 2025Do Biased Models Have Biased Thoughts?cited by 1
  9. 2025Enhancing Retinal Vessel Segmentation Generalization via Layout-Aware Generative Modellingcited by 0
  10. 2026Class-Structure Preservation Beats Diversity: A Comprehensive Benchmark of Text Augmentation Methods for Imbalanced Text Classificationcited by 0
  11. 2026The average-farmer illusion in language-model simulations of agricultural decisionscited by 0