TabGen-ICL: Residual-Aware In-Context Example Selection for Tabular Data Generation

• URL: http://arxiv.org/abs/2502.16414v1
• Date: Sun, 23 Feb 2025 02:51:58 GMT
• Title: TabGen-ICL: Residual-Aware In-Context Example Selection for Tabular Data Generation
• Authors: Liancheng Fang, Aiwei Liu, Hengrui Zhang, Henry Peng Zou, Weizhi Zhang, Philip S. Yu,
• Abstract summary: TabGen-ICL operates iteratively, retrieving a subset of real samples that represent the residual between currently generated samples and true data distributions.<n>Experiments on five real-world datasets demonstrate that TabGen-ICL significantly outperforms the random selection strategy.
• Score: 38.08438831075632
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language models (LLMs) have achieved encouraging results in tabular data generation. However, existing approaches require fine-tuning, which is computationally expensive. This paper explores an alternative: prompting a fixed LLM with in-context examples. We observe that using randomly selected in-context examples hampers the LLM's performance, resulting in sub-optimal generation quality. To address this, we propose a novel in-context learning framework: TabGen-ICL, to enhance the in-context learning ability of LLMs for tabular data generation. TabGen-ICL operates iteratively, retrieving a subset of real samples that represent the residual between currently generated samples and true data distributions. This approach serves two purposes: locally, it provides more effective in-context learning examples for the LLM in each iteration; globally, it progressively narrows the gap between generated and real data. Extensive experiments on five real-world tabular datasets demonstrate that TabGen-ICL significantly outperforms the random selection strategy. Specifically, it reduces the error rate by a margin of $3.5\%-42.2\%$ on fidelity metrics. We demonstrate for the first time that prompting a fixed LLM can yield high-quality synthetic tabular data. The code is provided in the \href{https://github.com/fangliancheng/TabGEN-ICL}{link}.

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