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

arXiv 2505.02659 ↗First posted 5 May 2025cs.LG cs.AIcited by 0

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Abstract (arXiv)

Large language models (LLMs) have shown promise in synthetic tabular data generation, yet existing methods struggle to preserve complex feature dependencies, particularly among categorical variables. This work introduces a probability-driven prompting approach that leverages LLMs to estimate conditional distributions, enabling more accurate and scalable data synthesis. The results highlight the potential of prompting probability distributions to enhance the statistical fidelity of LLM-generated tabular data.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 17 references that are in the map. First posted on arXiv 5 May 2025.

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References · 16

  1. 2023A Survey of Large Language Modelscited by 2,537
  2. 2019Modeling Tabular data using Conditional GANcited by 695
  3. 2017Real-valued (Medical) Time Series Generation with Recurrent Conditional GANscited by 329
  4. 2022Language Models are Realistic Tabular Data Generatorscited by 290
  5. 2018Data Synthesis based on Generative Adversarial Networkscited by 184
  6. 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 157
  7. 2023TabuLa: Harnessing Language Models for Tabular Data Synthesiscited by 42
  8. 2025A Comprehensive Survey of Synthetic Tabular Data Generationcited by 27
  9. 2019CorrGAN: Sampling Realistic Financial Correlation Matrices Using Generative Adversarial Networkscited by 15
  10. 2024EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Modelscited by 14
  11. 2025TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Datacited by 13
  12. 2024Why LLMs Are Bad at Synthetic Table Generation (and what to do about it)cited by 9
4 more
  1. 2025LLM-TabLogic: Preserving Inter-Column Logical Relationships in Synthetic Tabular Data via Prompt-Guided Latent Diffusioncited by 8
  2. 2024AIGT: AI Generative Table Based on Promptcited by 7
  3. 2024Generating Tables from the Parametric Knowledge of Language Modelscited by 3
  4. 2025GReaTER: Generate Realistic Tabular data after data Enhancement and Reductioncited by 3

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