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Language Models are Realistic Tabular Data Generators

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arxiv 2210.06280 v2 pith:DN4PKUTH submitted 2022-10-12 cs.LG

Vadim Borisov , Kathrin Se{ss}ler , Tobias Leemann , Martin Pawelczyk , Gjergji Kasneci This is my paper · ORCID
classification cs.LG
keywords datatabulargenerativegenerationgreatmodelsrealisticsynthetic
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Tabular data is among the oldest and most ubiquitous forms of data. However, the generation of synthetic samples with the original data's characteristics remains a significant challenge for tabular data. While many generative models from the computer vision domain, such as variational autoencoders or generative adversarial networks, have been adapted for tabular data generation, less research has been directed towards recent transformer-based large language models (LLMs), which are also generative in nature. To this end, we propose GReaT (Generation of Realistic Tabular data), which exploits an auto-regressive generative LLM to sample synthetic and yet highly realistic tabular data. Furthermore, GReaT can model tabular data distributions by conditioning on any subset of features; the remaining features are sampled without additional overhead. We demonstrate the effectiveness of the proposed approach in a series of experiments that quantify the validity and quality of the produced data samples from multiple angles. We find that GReaT maintains state-of-the-art performance across numerous real-world and synthetic data sets with heterogeneous feature types coming in various sizes.

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Cited by 16 Pith papers

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    FairDiffuseVQVAE reaches state-of-the-art fairness on the standard tabular benchmark (DPR 0.702, EOR 0.686) by uniform protected-attribute sampling at inference, paying ~15 AUC points of utility.

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    DiffusionRank, a diffusion-based generative model over feature-label tuples, improves learning-to-rank over discriminative baselines on MQ2007 and MSLR-WEB10K, but not consistently on MQ2008.

  3. TAGAL: Tabular Data Generation using Agentic LLM Methods

    cs.LG 2025-09 conditional novelty 6.0 of 10

    TAGAL uses an agentic LLM loop, generation plus feedback, to produce synthetic tabular data without LLM training, matching trained models on some datasets and beating the training-free EPIC baseline.

  4. Ensembling Membership Inference Attacks Against Tabular Generative Models

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    No single membership inference attack dominates across tabular generative models, and unsupervised ensembles of attacks achieve better average rankings.

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    A metric-oriented survey that classifies intrinsic quality and trustworthiness metrics for LLM-generated data across six modalities and documents systematic evaluation gaps in the current literature.

  11. Automatic Demonstration Selection for LLM-based Tabular Data Classification

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    A spectral-gap heuristic on a token-ID similarity graph automatically chooses the number of in-context demonstrations for LLM-based tabular classification, with mixed empirical results against random selection.

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  15. Synthetic Tabular Data Generation: A Comparative Survey for Modern Techniques

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  16. Synthetic Tabular Data: Methods, Attacks and Defenses

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    A review of tabular synthetic data generation, privacy attacks, and defenses, whose central message is that synthetic data alone does not guarantee privacy.

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