Exploring Prompting Methods for Mitigating Class Imbalance through Synthetic Data Generation with Large Language Models
<|reference_start|>Exploring Prompting Methods for Mitigating Class Imbalance through Synthetic Data Generation with Large Language Models: Large language models (LLMs) have demonstrated impressive in-context learning capabilities across various domains. Inspired by this, our study explores the effectiveness of LLMs in generating realistic tabular data to mitigate class imbalance. We investigate and identify key prompt design elements such as data format, class presentation, and variable mapping to optimize the generation performance. Our findings indicate that using CSV format, balancing classes, and employing unique variable mapping produces realistic and reliable data, significantly enhancing machine learning performance for minor classes in imbalanced datasets. Additionally, these approaches improve the stability and efficiency of LLM data generation. We validate our approach using six real-world datasets and a toy dataset, achieving state-of-the-art performance in classification tasks. The code is available at: https://github.com/seharanul17/synthetic-tabular-LLM<|reference_end|>
@article{kim2024epic:,
    title={EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular
  Data Classification via Large Language Models},
    author={Jinhee Kim, Taesung Kim, Jaegul Choo},
    journal={arXiv preprint arXiv:2404.12404},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.12404},
    primaryClass={cs.LG cs.AI}
}