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Why LLMs Are Bad at Synthetic Table Generation (and what to do about it)
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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.
Forward citations
Cited by 7 Pith papers
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The Prompt is Mightier than the Example
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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A Note on Statistically Accurate Tabular Data Generation Using Large Language Models
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.
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Making Sense of Data in the Wild: Data Analysis Automation at Scale
A multi-agent LLM system with retrieval-augmented generation automatically curates datasets from Zenodo and Hugging Face, yielding small retrieval gains and a confounded synthetic-data improvement.
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