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arXiv · 2406.14541

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

Shengzhe Xu·Cho-Ting Lee·Mandar Sharma·Raquib Bin Yousuf·Nikhil Muralidhar·Naren Ramakrishnan
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Abstract

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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Shengzhe Xu, Cho-Ting Lee, Mandar Sharma, Raquib Bin Yousuf, Nikhil Muralidhar, Naren Ramakrishnan. 2024-06-20. Why LLMs Are Bad at Synthetic Table Generation (and what to do about it). https://arxiv.org/abs/2406.14541

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