Tabby: Tabular Data Synthesis with Language Models

• URL: http://arxiv.org/abs/2503.02152v1
• Date: Tue, 04 Mar 2025 00:32:15 GMT
• Title: Tabby: Tabular Data Synthesis with Language Models
• Authors: Sonia Cromp, Satya Sai Srinath Namburi GNVV, Mohammed Alkhudhayri, Catherine Cao, Samuel Guo, Nicholas Roberts, Frederic Sala,
• Abstract summary: Tabby is a simple but powerful post-training modification to the standard Transformer language model architecture.<n>We show that Tabby results in data quality near or equal to that of real data.
• Score: 11.309789039228496
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While advances in large language models (LLMs) have greatly improved the quality of synthetic text data in recent years, synthesizing tabular data has received relatively less attention. We address this disparity with Tabby, a simple but powerful post-training modification to the standard Transformer language model architecture, enabling its use for tabular dataset synthesis. Tabby enables the representation of differences across columns using Gated Mixture-of-Experts, with column-specific sets of parameters. Empirically, Tabby results in data quality near or equal to that of real data. By pairing our novel LLM table training technique, Plain, with Tabby, we observe up to a 44% improvement in quality over previous methods. We also show that Tabby extends beyond tables to more general structured data, reaching parity with real data on a nested JSON dataset as well.

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