Generative Models for Synthetic Data: Transforming Data Mining in the GenAI Era
On the map: Diffusion models → Diffusion foundations and sampling
Abstract (arXiv)
Generative models such as Large Language Models, Diffusion Models, and generative adversarial networks have recently revolutionized the creation of synthetic data, offering scalable solutions to data scarcity, privacy, and annotation challenges in data mining. This tutorial introduces the foundations and latest advances in synthetic data generation, covers key methodologies and practical frameworks, and discusses evaluation strategies and applications. Attendees will gain actionable insights into leveraging generative synthetic data to enhance data mining research and practice. More information can be found on our website: https://syndata4dm.github.io/.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 21 of its 34 references that are in the map. First posted on arXiv 27 Aug 2025.
Builds on
21 of 34 references.
References · 21
- 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
- 2022TabDDPM: Modelling Tabular Data with Diffusion Modelscited by 377
- 2024Justice or Prejudice? Quantifying Biases in LLM-as-a-Judgecited by 368
- 2024Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothingcited by 346
- 2023Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Biascited by 232
- 2023Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifoldcited by 219
- 2023StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learnerscited by 177
- 2024A Note on Shumailov et al. (2024): `AI Models Collapse When Trained on Recursively Generated Data'cited by 115
- 2023DiffuRec: A Diffusion Model for Sequential Recommendationcited by 93
- 2023Generative Diffusion Models on Graphs: Methods and Applicationscited by 81
- 2023LLMaAA: Making Large Language Models as Active Annotatorscited by 79
- 2024TabDiff: a Mixed-type Diffusion Model for Tabular Data Generationcited by 63
9 more
- 2025Synthetic Data Generation Using Large Language Models: Advances in Text and Codecited by 61
- 2024Dynamic Evaluation of Large Language Models by Meta Probing Agentscited by 54
- 2024DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer's Disease Questions with Scientific Literaturecited by 50
- 2024Evaluating Language Models as Synthetic Data Generatorscited by 33
- 2024Task Me Anythingcited by 30
- 2024Efficacy of Synthetic Data as a Benchmarkcited by 30
- 2024AutoBench-V: Can Large Vision-Language Models Benchmark Themselves?cited by 21
- 2025Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Modelscited by 15
- 2025Diffusion on Graph: Augmentation of Graph Structure for Node Classificationcited by 4
Built on by
Citing papers per year: 2026: 2.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2024Generative AI for Synthetic Data Generation: Methods, Challenges and the Future
- 2024Synthetic data: How could it be used for infectious disease research?
- 2024Comprehensive Exploration of Synthetic Data Generation: A Survey
- 2023Boosting Data Analytics With Synthetic Volume Expansion
- 2024On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey
- 2025Boosting Statistic Learning with Synthetic Data from Pretrained Large Models
- 2024Exploring the Landscape for Generative Sequence Models for Specialized Data Synthesis
- 2024Best Practices and Lessons Learned on Synthetic Data