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

arXiv: https://arxiv.org/abs/2507.19334 · HTML: https://mlsanity.com/paper/2507.19334/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 63 references that are in the map.
- First posted on arXiv 25 Jul 2025.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [GLU Variants Improve Transformer](https://mlsanity.com/paper/2002.05202/) (2020)
- [Glow: Generative Flow with Invertible 1x1 Convolutions](https://mlsanity.com/paper/1807.03039/) (2018)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [DAGs with NO TEARS: Continuous Optimization for Structure Learning](https://mlsanity.com/paper/1803.01422/) (2018)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [TabDDPM: Modelling Tabular Data with Diffusion Models](https://mlsanity.com/paper/2209.15421/) (2022)
- [Language Models are Realistic Tabular Data Generators](https://mlsanity.com/paper/2210.06280/) (2022)
- [Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Space](https://mlsanity.com/paper/2310.09656/) (2023)
- [Trust Issues: Uncertainty Estimation Does Not Enable Reliable OOD Detection On Medical Tabular Data](https://mlsanity.com/paper/2011.03274/) (2020)
- [Understanding the Performance and Estimating the Cost of LLM Fine-Tuning](https://mlsanity.com/paper/2408.04693/) (2024)
- [Prompt Design and Engineering: Introduction and Advanced Methods](https://mlsanity.com/paper/2401.14423/) (2024)
- [FinDiff: Diffusion Models for Financial Tabular Data Generation](https://mlsanity.com/paper/2309.01472/) (2023)
- [Copula Flows for Synthetic Data Generation](https://mlsanity.com/paper/2101.00598/) (2021)
- [Beyond the convexity assumption: Realistic tabular data generation under quantifier-free real linear constraints](https://mlsanity.com/paper/2502.18237/) (2025)
- [Fidelity and Privacy of Synthetic Medical Data](https://mlsanity.com/paper/2101.08658/) (2021)
- [A Survey on Deep Learning Approaches for Tabular Data Generation: Utility, Alignment, Fidelity, Privacy, Diversity, and Beyond](https://mlsanity.com/paper/2503.05954/) (2025)
- [Why LLMs Are Bad at Synthetic Table Generation (and what to do about it)](https://mlsanity.com/paper/2406.14541/) (2024)
- [Deep Learning within Tabular Data: Foundations, Challenges, Advances and Future Directions](https://mlsanity.com/paper/2501.03540/) (2025)
- [Not All Features Deserve Attention: Graph-Guided Dependency Learning for Tabular Data Generation with Language Models](https://mlsanity.com/paper/2507.18504/) (2025)
- [When are Deep Networks really better than Decision Forests at small sample sizes, and how?](https://mlsanity.com/paper/2108.13637/) (2021)

## Built on by (most cited first, 9 of 9)

- [Attributes as Textual Genes: Leveraging LLMs as Genetic Algorithm Simulators for Conditional Synthetic Data Generation](https://mlsanity.com/paper/2509.02040/) (2025)
- [Causal-Aware Tabular GANs with Reinforcement Learning](https://mlsanity.com/paper/2510.24046/) (2025)
- [SAGE: Sparse Adaptive Guidance for Dependency-Aware Tabular Data Generation](https://mlsanity.com/paper/2604.24368/) (2026)
- [Active Tabular Augmentation via Policy-Guided Diffusion Inpainting](https://mlsanity.com/paper/2605.10315/) (2026)
- [StructSynth: Dependency Graphs as Generation Plans for Low-Data Tabular Synthesis with Language Models](https://mlsanity.com/paper/2508.02601/) (2025)
- [SeaPO: Strategic Error Amplification for Robust Preference Optimization of Large Language Models](https://mlsanity.com/paper/2509.24781/) (2025)
- [LAB-Tab: LLM-Augmented Bayesian Network Adaptation for Few-Shot Tabular Generation](https://mlsanity.com/paper/2608.01879/) (2026)
- [Synthesis Without Training: An Inference-Only Pipeline for Tabular, Temporal, and Relational Synthetic Data](https://mlsanity.com/paper/2609.38414/) (2026)
- [Relational Learning as World Modeling](https://mlsanity.com/paper/2610.16763/) (2026)

## Abstract (arXiv)

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.