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TabuLa: Harnessing Language Models for Tabular Data Synthesis

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arxiv 2310.12746 v3 pith:PI6LQ2YM submitted 2023-10-19 cs.LG

Zilong Zhao , Robert Birke , Lydia Chen This is my paper · ORCID
classification cs.LG
keywords datatabulartabulatrainingdatasetslanguagepaddingwhile
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Tabular data synthesis is crucial for addressing privacy and security concerns in industries reliant on tabular data. While recent advancements adopt large language models (LLMs) for realistic tabular data generation, their long training times and limited reusability hinder practical applications. In this paper, we propose Tabula, a tabular data synthesizer that leverages the structure of LLM. Unlike state-of-the-art (SOTA) LLM-based tabular data synthesizers that rely on pre-trained LLMs, Tabula discards the pre-trained weights originally designed for natural language tasks, focusing instead on a tailored approach for tabular data. In addition, Tabula introduces a token sequence compression strategy that significantly reduces training time while maintaining data quality, alongside a novel token padding method that improves sequence alignment across training batches. Experiments on six datasets show that Tabula achieves superior synthetic data utility compared to current SOTA methods. Additionally, the results demonstrate that Tabula model trained on tabular datasets serves effectively as a foundational model for synthesizing new tabular datasets. Furthermore, the proposed padding method outperforms the conventional left and right padding strategies. Finally, the results highlight that Tabula averagely reduces training time per epoch by 46.2% compared to state-of-the-art LLM approaches while achieving higher data utility. Our code is available at https://github.com/zhao-zilong/Tabula

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Cited by 12 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer

    cs.LG 2025-01 conditional novelty 7.0 of 10

    A hybrid tree-transformer with leaf-index prompting and dual-quantization tokenization improves synthetic tabular data utility on nine benchmarks, with the top no-mask variant gaining 44 percent utility over its maske...

  2. LAB-Tab: LLM-Augmented Bayesian Network Adaptation for Few-Shot Tabular Generation

    cs.LG 2026-08 conditional novelty 6.0 of 10

    A source-trained Bayesian network, augmented with LLM-proposed edges and calibrated by PPO, generates synthetic tabular data that outperforms six baselines in six ACS distribution-shift scenarios.

  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. Risk In Context: Benchmarking Privacy Leakage of Foundation Models in Synthetic Tabular Data Generation

    cs.LG 2025-07 conditional novelty 6.0 of 10

    LLM-based tabular generators reproduce seed rows often enough that membership-inference attacks succeed more against them than against GAN, VAE, or diffusion baselines.

  5. What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models

    cs.LG 2025-04 conditional novelty 6.0 of 10

    Explainable AI applied to a real-versus-synthetic detection classifier pinpoints which features, dependencies, and value ranges a tabular generative model reproduces poorly.

  6. Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data

    cs.LG 2025-04 conditional novelty 6.0 of 10

    LLM-generated surrogate public data, built from schema metadata alone, can substitute for traditional public data when pretraining differentially private tabular classifiers in small-data settings.

  7. Synthetic Tabular Data Generation for Imbalanced Classification: The Surprising Effectiveness of an Overlap Class

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    Adding an 'overlap' class label for boundary majority points during generative model training improves synthetic minority data quality and downstream classifier accuracy on imbalanced tabular data.

  8. A text-to-tabular approach to generate synthetic patient data using LLMs

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    A frozen LLM prompted with a text description and one average patient example generates synthetic Parkinson's and Alzheimer's cohorts with preserved correlations, but with lower fidelity than models trained on original data.

  9. The Prompt is Mightier than the Example

    cs.LG 2025-05 conditional novelty 5.0 of 10

    Injecting domain knowledge into prompts can substitute for many in-context examples in LLM-based synthetic tabular data generation, cutting required example counts by 40-90%.

  10. A Note on Statistically Accurate Tabular Data Generation Using Large Language Models

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    A probability-driven prompting method, where an LLM estimates conditional categorical distributions and rows are sampled from them, outperforms table-wide and cell-by-cell generation on a California demographics dataset.

  11. A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs

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    Commercial LLMs generate usable synthetic ICU records only for small feature sets, with fidelity and downstream prediction quality degrading sharply as dimensionality grows.

  12. A Comprehensive Survey of Synthetic Tabular Data Generation

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    A structured survey that categorizes synthetic tabular data generation into traditional, diffusion, and LLM-based methods, with a comparative benchmark and a taxonomy of post-processing and evaluation.

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