用LLM生成稀疏依赖图，快速低成本扩增表格数据
Doubling Your Data in Minutes: Ultra-fast Tabular Data Generation via LLM-Induced Dependency Graphs
- 通过LLM构建稀疏依赖图，仅根据父节点生成特征值
- 生成速度比LLM基线快9500倍，约束违反率降低4%
- 适合需要快速生成高质量表格数据的场景
表格数据在多个领域至关重要，但因隐私顾虑和收集成本高，高质量数据集仍稀缺。现有基于大语言模型（LLMs）的表格增强方法存在两大缺陷：(1) 特征间过度依赖建模易引入偏差；(2) 采样计算开销大。为此，我们提出SPADA（SPArse Dependency-driven Augmentation），一种轻量级生成框架，通过LLM诱导的图结构显式建模稀疏依赖。将每个特征视为节点，通过遍历图生成值，仅依赖其父节点。探索两种合成策略：基于高斯核密度估计的非参数方法，以及学习可逆映射的条件归一化流模型，用于条件密度估计。在四个数据集上的实验表明，SPADA相比扩散方法减少4%的约束违反，生成速度较LLM基线提升近9500倍。
原文摘要 · Abstract (English)
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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