Doubling Your Data in Minutes: Ultra-fast Tabular Data Generation via LLM-Induced Dependency Graphs

• URL: http://arxiv.org/abs/2507.19334v1
• Date: Fri, 25 Jul 2025 14:43:50 GMT
• Title: Doubling Your Data in Minutes: Ultra-fast Tabular Data Generation via LLM-Induced Dependency Graphs
• Abstract summary: Tabular data is critical across diverse domains, yet high-quality datasets remain scarce due to privacy concerns and the cost of collection.<n>We propose SPADA for SPArse Dependency-driven Augmentation, a lightweight generative framework that captures sparse dependencies via an LLM-induced graph.<n> Experiments on four datasets show that SPADA reduces constraint violations by 4% compared to diffusion-based methods and accelerates generation by nearly 9,500 times over LLM-based baselines.
• Score: 15.476573983202162
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Tabular data is critical across diverse domains, yet high-quality datasets remain scarce due to privacy concerns and the cost of collection. Contemporary approaches adopt large language models (LLMs) for tabular augmentation, but exhibit two major limitations: (1) dense dependency modeling among tabular features that can introduce bias, and (2) high computational overhead in sampling. To address these issues, we propose SPADA for SPArse Dependency-driven Augmentation, a lightweight generative framework that explicitly captures sparse dependencies via an LLM-induced graph. We treat each feature as a node and synthesize values by traversing the graph, conditioning each feature solely on its parent nodes. We explore two synthesis strategies: a non-parametric method using Gaussian kernel density estimation, and a conditional normalizing flow model that learns invertible mappings for conditional density estimation. Experiments on four datasets show that SPADA reduces constraint violations by 4% compared to diffusion-based methods and accelerates generation by nearly 9,500 times over LLM-based baselines.

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