Beyond Marginals: A Multi-Dimensional Evaluation Framework for Multi-Table Synthetic Data Generation

Excerpt

arXiv:2610.06854v1 Announce Type: cross Abstract: Synthetic data generation is critical for privacy compliance, machine learning augmentation, and software testing. While single-table evaluation is well established, multi-table (relational) synthesis, the dominant enterprise use case, lacks a unified evaluation framework. Existing approaches assess marginal column distributions in isolation, overlooking joint distributions, cross-table structural integrity, downstream utility, and production-rea