arXiv:2607.03926v1 Announce Type: cross Abstract: Synthetic tabular data support use cases like data sharing, model development under access restrictions, and rapid prototyping of analytical workflows. Modern generative models are evaluated by their statistical similarity, correlation structure, privacy, and downstream machine-learning utility. However, such evaluations leave a gap: they rarely test the structure that matters for analytical queries. We present TabQueryBench, a query-centric benchmark that uses SQL-shaped analytical queries as structural assessors for synthetic data fidelity. I
The proliferation of generative AI models for data synthesis and strict data privacy regulations necessitate more robust and application-specific evaluation benchmarks for synthetic data quality.
A robust method for evaluating synthetic data fidelity using analytical queries directly impacts the reliability and usability of AI-generated data for model development, analysis, and secure data sharing across various industries.
The introduction of a query-centric benchmark shifts the focus of synthetic data evaluation from general statistical similarity to operational utility, enabling more accurate assessment of how well synthetic data supports real-world analytical tasks.
- · AI model developers
- · Data privacy compliance solutions
- · Industries with sensitive data
- · Data analytics platforms
- · Organizations relying solely on basic statistical metrics for synthetic data val
- · Generative models producing statistically similar but analytically unsound data
Improved quality and trustworthiness of synthetic tabular data for diverse applications.
Accelerated development and adoption of AI-driven solutions in data-sensitive sectors due to better data availability and fidelity.
Enhanced data collaboration and sharing capabilities across enterprises and research institutions, fostering innovation while maintaining privacy.
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Read at arXiv cs.AI