mlsanityData to 7 Oct 2026

Comprehensive Exploration of Synthetic Data Generation: A Survey

arXiv 2401.02524 ↗First posted 4 Jan 2024cs.LG cs.AI cs.CVcited by 59

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Abstract (arXiv)

Recent years have witnessed a surge in the popularity of Machine Learning (ML), applied across diverse domains. However, progress is impeded by the scarcity of training data due to expensive acquisition and privacy legislation. Synthetic data emerges as a solution, but the abundance of released models and limited overview literature pose challenges for decision-making. This work surveys 417 Synthetic Data Generation (SDG) models over the last decade, providing a comprehensive overview of model types, functionality, and improvements. Common attributes are identified, leading to a classification and trend analysis. The findings reveal increased model performance and complexity, with neural network-based approaches prevailing, except for privacy-preserving data generation. Computer vision dominates, with GANs as primary generative models, while diffusion models, transformers, and RNNs compete. Implications from our performance evaluation highlight the scarcity of common metrics and datasets, making comparisons challenging. Additionally, the neglect of training and computational costs in literature necessitates attention in future research. This work serves as a guide for SDG model selection and identifies crucial areas for future exploration.

Key facts. Cited by 59 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Jan 2024.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 16; 2025: 34; 2026: 9.

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  1. 2024Scaling Synthetic Data Creation with 1,000,000,000 Personascited by 397
  2. 2024ToolACE: Winning the Points of LLM Function Callingcited by 204
  3. 2024Scaling Laws for Precisioncited by 92
  4. 2024On the Diversity of Synthetic Data and its Impact on Training Large Language Modelscited by 56
  5. 2024Generative AI for Synthetic Data Generation: Methods, Challenges and the Futurecited by 31
  6. 2024ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?cited by 31
  7. 2024Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Modelscited by 31
  8. 2024Montessori-Instruct: Generate Influential Training Data Tailored for Student Learningcited by 12
  9. 2024DiffLM: Controllable Synthetic Data Generation via Diffusion Language Modelscited by 9
  10. 2024Towards Building Specialized Generalist AI with System 1 and System 2 Fusioncited by 8
  11. 2025From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systemscited by 8
  12. 2025A Survey of Inductive Reasoning for Large Language Modelscited by 8
38 more
  1. 2025Beyond Real Faces: Synthetic Datasets Can Achieve Reliable Recognition Performance without Privacy Compromisecited by 8
  2. 2025ToolForge: A Data Synthesis Pipeline for Multi-Hop Search without Real-World APIscited by 8
  3. 2025Logic Unseen: Revealing the Logical Blindspots of Vision-Language Modelscited by 7
  4. 2024Tabular Data Synthesis with Differential Privacy: A Surveycited by 6
  5. 2025Self-Improvement in Multimodal Large Language Models: A Surveycited by 6
  6. 2024Mitigating Sycophancy in Decoder-Only Transformer Architectures: Synthetic Data Interventioncited by 5
  7. 2025Deep Learning in Palmprint Recognition-A Comprehensive Surveycited by 5
  8. 2025A survey of datasets for computer vision in agriculturecited by 5
  9. 2025Enterprise Large Language Model Evaluation Benchmarkcited by 5
  10. 2025Synthetic Data Generation by Supervised Neural Gas Network for Physiological Emotion Recognition Datacited by 4
  11. 2025Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applicationscited by 4
  12. 2025Synthetic Data Generation of Body Motion Data by Neural Gas Network for Emotion Recognitioncited by 2
  13. 2025Feeding LLM Annotations to BERT Classifiers at Your Own Riskcited by 2
  14. 2025Code-driven Number Sequence Calculation: Enhancing the inductive Reasoning Abilities of Large Language Modelscited by 2
  15. 2025Uncertainty Quantification and Data Efficiency in AI: An Information-Theoretic Perspectivecited by 2
  16. 2026GR-SAP: Generative Replay for Safety Alignment Preservation during Fine-Tuningcited by 2
  17. 2025Code-Driven Inductive Synthesis: Enhancing Reasoning Abilities of Large Language Models with Sequencescited by 1
  18. 2025PuckTrick: A Library for Making Synthetic Data More Realisticcited by 1
  19. 2025RulerNet: Learning Perspective-Invariant Ruler Representations for Robust Image Scale Estimationcited by 1
  20. 2025Democratizing Tabular Data Access with an Openunicodex2013Source Syntheticunicodex2013Data SDKcited by 1
  21. 2025Treatment Stitching with Schr"odinger Bridge for Enhancing Offline Reinforcement Learning in Adaptive Treatment Strategiescited by 1
  22. 2025Attribute-Aware Controlled Product Generation with LLMs for E-commercecited by 1
  23. 2026Synthetic Data Generation for Training Diversified Commonsense Reasoning Modelscited by 1
  24. 2026GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Callingcited by 1
  25. 2024EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Modelscited by 0
  26. 2024NeSy is alive and well: A LLM-driven symbolic approach for better code comment data generation and classificationcited by 0
  27. 2024Adaptable Embeddings Network (AEN)cited by 0
  28. 2024Tabular data generation with tensor contraction layers and transformerscited by 0
  29. 2025Engineering Artificial Intelligence: Framework, Challenges, and Future Directioncited by 0
  30. 2025Attributed Synthetic Data Generation for Zero-shot Domain-specific Image Classificationcited by 0
  31. 2025The Synthetic Mirror -- Synthetic Data at the Age of Agentic AIcited by 0
  32. 2025A Comparative Study of Open-Source Libraries for Synthetic Tabular Data Generation: SDV vs. SynthCitycited by 0
  33. 2025Synthetic Data Generation for Emotional Depth Faces: Optimizing Conditional DCGANs via Genetic Algorithms in the Latent Space and Stabilizing Training with Knowledge Distillationcited by 0
  34. 2025Assessment of Using Synthetic Data in Brain Tumor Segmentationcited by 0
  35. 2025FEST: A Unified Framework for Evaluating Synthetic Tabular Datacited by 0
  36. 2025RT-VLM: Re-Thinking Vision Language Model with 4-Clues for Real-World Object Recognition Robustnesscited by 0
  37. 2025Synthetic-to-Real Object Detection using YOLOv11 and Domain Randomization Strategiescited by 0
  38. 2025GEM-T: Generative Tabular Data via Fitting Momentscited by 0

50 most cited of 59 All 59 (JSON)

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  1. 2025Generative Models for Synthetic Data: Transforming Data Mining in the GenAI Era
  2. 2025A Reinforcement Learning Approach to Synthetic Data Generation
  3. 2024A Survey of Data Synthesis Approaches
  4. 2023Boosting Data Analytics With Synthetic Volume Expansion
  5. 2024Best Practices and Lessons Learned on Synthetic Data
  6. 2024Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training
  7. 2023Real-Fake: Effective Training Data Synthesis Through Distribution Matching
  8. 2025Boosting Statistic Learning with Synthetic Data from Pretrained Large Models