Hierarchical Synthetic Tabular Data Generation: A Hybrid Top-Down and Bottom-Up Framework

• URL: http://arxiv.org/abs/2605.28198v1
• Date: Wed, 27 May 2026 09:21:47 GMT
• Title: Hierarchical Synthetic Tabular Data Generation: A Hybrid Top-Down and Bottom-Up Framework
• Abstract summary: We propose a hierarchical hybrid top-down and bottom-up (H-TDBU) framework that decouples semantic structures from texture.<n> Experimental results show that our H-TDBU approach improves train-synthetic-test-real performance over neural baseline methods.<n>Our results suggest that hierarchical rule-guided synthesis provides an effective mechanism for combining controllability, semantic coherence, and statistical fidelity in synthetic data generation.
• Score: 14.715376615336536
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing approaches for synthetic tabular data generation are based on either purely generative models or LLMs, both of which struggle with data heterogeneity, logical consistency, rare-event coverage, and robustness in low-data regimes. In this paper, we propose a hierarchical hybrid top-down and bottom-up (H-TDBU) framework that decouples semantic structures from stochastic texture. In the top-down path, structure-driven logical constraints and cross-modal alignment rules are constructed, while in the bottom-up path, lightweight tabular generators are used to learn local statistical patterns from real data. The two paths are consolidated in a unified synthesis engine with an iterative feedback loop. We evaluate the framework on weak multimodal financial benchmarks combining tabular and sentiment-text data. Experimental results show that our H-TDBU approach improves train-synthetic-test-real performance over neural baseline methods while preserving semantic consistency. Our results suggest that hierarchical rule-guided synthesis provides an effective mechanism for combining controllability, semantic coherence, and statistical fidelity in synthetic data generation.

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