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EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Models

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arxiv 2404.12404 v4 pith:BSWBJSFD submitted 2024-04-15 cs.LG cs.AI

Jinhee Kim , Taesung Kim , Jaegul Choo This is my paper · ORCID
classification cs.LGcs.AI
keywords dataepicllmssynthetictabularacrossclassificationdatasets
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Large language models (LLMs) have demonstrated remarkable in-context learning capabilities across diverse applications. In this work, we explore the effectiveness of LLMs for generating realistic synthetic tabular data, identifying key prompt design elements to optimize performance. We introduce EPIC, a novel approach that leverages balanced, grouped data samples and consistent formatting with unique variable mapping to guide LLMs in generating accurate synthetic data across all classes, even for imbalanced datasets. Evaluations on real-world datasets show that EPIC achieves state-of-the-art machine learning classification performance, significantly improving generation efficiency. These findings highlight the effectiveness of EPIC for synthetic tabular data generation, particularly in addressing class imbalance. Our source code for our work is available at: https://seharanul17.github.io/project-synthetic-tabular-llm/

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Forward citations

Cited by 5 Pith papers

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

  1. Risk In Context: Benchmarking Privacy Leakage of Foundation Models in Synthetic Tabular Data Generation

    cs.LG 2025-07 conditional novelty 6.0 of 10

    LLM-based tabular generators reproduce seed rows often enough that membership-inference attacks succeed more against them than against GAN, VAE, or diffusion baselines.

  2. Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data

    cs.LG 2025-04 conditional novelty 6.0 of 10

    LLM-generated surrogate public data, built from schema metadata alone, can substitute for traditional public data when pretraining differentially private tabular classifiers in small-data settings.

  3. Improving Equity in Health Modeling with GPT4-Turbo Generated Synthetic Data: A Comparative Study

    cs.LG 2024-12 conditional novelty 6.0 of 10

    LLM-generated synthetic data improved AUROC for minority groups in 13 of 17 tested settings, but group-specific prompting added no consistent benefit over generic prompts.

  4. 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.

  5. A Note on Statistically Accurate Tabular Data Generation Using Large Language Models

    cs.LG 2025-05 conditional novelty 5.0 of 10

    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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