Citation notice #1007 · 2026-07-11 03:18:53.715838+00:00
Hierarchical Synthetic Tabular Data Generation: A Hybrid Top-Down and Bottom-Up Framework
Correction
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cites AI models collapse when trained on recursively generated data.Nature, 631:755–759, 2024, which carries a correction notice dated 2025-03-21. One-hop deterministic notice: the citation edge exists in the Pith bibliography graph; no model judged whether the citation was load-bearing.
Citing paper Event page Original DOI Notice DOI File a formal challenge All reference changes
01Evidence
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doi: 10.1038/s41586-024-07566-y. URL https: //doi.org/10.1038/s41586-024-07566-y. Solatorio, A. V . and Dupriez, O. Realtabformer: Generating realistic relational and tabular data using transformers. arXiv preprint arXiv:2302.02041, 2023. Turanyksel, S. Adult income dataset. https://www. kaggle.com/datasets/serpilturanyksel/ adult-income, 2021. Umesh, C., Seegel-Schultz, K., Mahendra, M., Bej, S., and Wolkenhauer, O. Dependency-aware synthetic tabular data generation.Pattern Recog- nition, 179:113819, 2026. ISSN 0031-3203. doi: https://doi.org/10.1016/j.patcog.2026.113819. URL https://www.sciencedirect.com/ science/article/pii/S0031320326007843. Wang, Y ., Feng, D., Dai, Y ., Chen, Z., Huang, J., Anani- adou, S., Xie, Q., and Wang, H. Harmonic: Harnessing llms for tabular data synthesis and privacy protection. In Globerson, A., Mackey, L., Belgrave, D., Fan, A., Pa- quet, U., Tomczak, J., and Zhang, C. (eds.),Advances in Neural Information Processing Systems, volume 37, pp. 100196–100212. Curran Associates, Inc., 2024. doi: 10.52202/079017-3179. Xu, L., Skoularidou, M., Cuesta-Infante, A., and Veeramachaneni, K. Modeling tabular data using conditional gan. In Wallach, H., Larochell
02Event
- Type
- Correction
- Source
- Crossref
- Original DOI
- 10.1038/s41586-024-07566-y
- Notice DOI
- 10.1038/s41586-025-08905-3
- Date
- 2025-03-21
- Title
- Author Correction: AI models collapse when trained on recursively generated data
- Reasons
- ['Correction']
- Work
- AI models collapse when trained on recursively generated data.Nature, 631:755–759, 2024 (2024) Nature
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