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Why LLMs Are Bad at Synthetic Table Generation (and what to do about it)

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arxiv 2406.14541 v3 pith:NBQXGMFI submitted 2024-06-20 cs.LG

Shengzhe Xu , Cho-Ting Lee , Mandar Sharma , Raquib Bin Yousuf , Nikhil Muralidhar , Naren Ramakrishnan This is my paper
classification cs.LG
keywords syntheticdatagenerationllmsfine-tuningtableadvancedas-is
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Synthetic data generation is integral to ML pipelines, e.g., to augment training data, replace sensitive information, and even to power advanced platforms like DeepSeek. While LLMs fine-tuned for synthetic data generation are gaining traction, synthetic table generation -- a critical data type in business and science -- remains under-explored compared to text and image synthesis. This paper shows that LLMs, whether used as-is or after traditional fine-tuning, are inadequate for generating synthetic tables. Their autoregressive nature, combined with random order permutation during fine-tuning, hampers the modeling of functional dependencies and prevents capturing conditional mixtures of distributions essential for real-world constraints. We demonstrate that making LLMs permutation-aware can mitigate these issues.

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Cited by 3 Pith papers

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

  1. 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.

  2. A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data

    cs.AI 2026-01 conditional novelty 5.0 of 10

    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.

  3. 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%.

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