几分钟内将您的数据翻倍：通过 LLM 诱导依赖图实现超快速表格数据生成
Doubling Your Data in Minutes: Ultra-fast Tabular Data Generation via LLM-Induced Dependency Graphs
AI总结：
本文提出 SPADA 框架，利用 LLM 诱导的稀疏依赖图遍历生成表格特征，并结合核密度估计或条件归一化流，在降低约束违反的同时实现近 9,500 倍加速。
摘要：
表格数据在众多领域都至关重要，但由于隐私问题和采集成本，高质量数据集仍然稀缺。现有方法采用大型语言模型（LLM）进行表格数据增强，但存在两个主要局限：（1）对表格特征之间的稠密依赖进行建模，这可能引入偏差；（2）采样过程中的高计算开销。为解决这些问题，我们提出了 SPADA（SPArse Dependency-driven Augmentation，稀疏依赖驱动增强），这是一个轻量级生成框架，通过 LLM 诱导的图显式捕捉稀疏依赖。我们将每个特征视为一个节点，并通过遍历该图来合成数值，使每个特征仅以其父节点为条件。我们探索了两种合成策略：一种是使用高斯核密度估计的非参数方法，另一种是学习可逆映射以进行条件密度估计的条件归一化流模型。在四个数据集上的实验表明，与基于扩散的方法相比，SPADA 将约束违反减少了 4%，并且其生成速度比基于 LLM 的基线快近 9,500 倍。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
