SOURCE-LINKED INTELLIGENCE
Beyond Distribution Matching: Semantics-Consistent Tabular Diffusion with Weak Semantic Priors
Synthetic tabular data can match real data distributions while still violating the semantic constraints that govern valid tabular rows. This reveals a key limitation of existing tabular generators: they mainly optimize distributional fidelity, but do not explicitly model weak semantic priors encoded in tabular schema and textual descriptions. In this paper, we propose \ours, a semantics-consistent tabular diffusion framework for high-fidelity synthetic data generation under weakly specified semantic priors. \ours\ first constructs two types of priors, namely intra-column semantics and inter-co
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-13T14:42:10.000Z
First collected: 2026-09-20T12:21:05.240Z. This is not the publication date.