Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes
arXiv:2509.09960v2 Announce Type: replace Abstract: Synthetic tabular data generation is increasingly essential in machine learning, supporting downstream applications when real-world, high-quality tabular data is insufficient. Existing tabular generation approaches, such as generative adversarial networks (GANs) and fine-tuned Large Language Models (LLMs), typically require sufficient reference data, limiting their effectiveness in domain-specific datasets with scarce records. While prompt-based LLMs offer flexibility without parameter tuning, they often generate distributionally drifted data
The increasing reliance on AI across industries highlights the critical need for robust data, especially as real-world high-quality datasets become insufficient or scarce, driving research into effective synthetic data generation.
This research addresses a fundamental limitation in AI development—the scarcity of high-quality tabular data—which is crucial for training and deployment across numerous applications, particularly in specialized domains.
The ability to reliably generate synthetic tabular data with limited reference allows AI models to be developed and deployed in data-poor environments, expanding the applicability of AI significantly.
- · AI developers in data-scarce domains
- · Analytics companies
- · Machine learning researchers
- · Sectors with sensitive or proprietary data
- · Companies reliant on large, proprietary datasets for competitive advantage
- · Traditional data collection methods
Improved performance and broader deployment of AI systems in specialized, data-limited fields.
Accelerated innovation and commercialization of AI-driven solutions across various industries due to reduced data acquisition bottlenecks.
Potential for new ethical and regulatory challenges related to the origin and use of increasingly sophisticated synthetic data.
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Read at arXiv cs.LG