用分层框架生成更真实表格数据，兼顾逻辑一致与统计特性。
Hierarchical Synthetic Tabular Data Generation: A Hybrid Top-Down and Bottom-Up Framework
- 分上下两路生成：上层定规则，下层学模式，再融合迭代优化。
- 在金融多模态数据上，合成数据训练效果优于纯神经网络方法。
- 适合需要高一致性与稀有事件覆盖的低数据量场景使用。
现有合成表格数据生成方法或依赖纯生成模型，或基于大语言模型，均面临数据异质性、逻辑不一致、罕见事件覆盖不足及低数据场景下鲁棒性差的问题。本文提出一种分层混合自上而下与自下而上的（H-TDBU）框架，将语义结构与随机纹理解耦。上层路径构建结构驱动的逻辑约束与跨模态对齐规则；下层路径采用轻量级表格生成器，从真实数据中学习局部统计模式。两条路径通过统一合成引擎整合，并引入迭代反馈机制。我们在结合表格与情感文本的弱多模态金融基准上进行评估，实验表明，所提H-TDBU方法在训练-合成-测试-真实性能上优于神经基线模型，同时保持了语义一致性。结果表明，分层规则引导的合成机制可有效实现可控性、语义连贯性与统计保真度的平衡。
原文摘要 · Abstract (English)
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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