Synthetic Oversampling: Theory and A Practical Approach Using LLMs to Address Data Imbalance
<|reference_start|>Synthetic Oversampling: Theory and A Practical Approach Using LLMs to Address Data Imbalance: Imbalanced data and spurious correlations are common challenges in machine learning and data science. Oversampling, which artificially increases the number of instances in the underrepresented classes, has been widely adopted to tackle these challenges. In this article, we introduce OPAL (\textbf{O}versam\textbf{P}ling with \textbf{A}rtificial \textbf{L}LM-generated data), a systematic oversampling approach that leverages the capabilities of large language models (LLMs) to generate high-quality synthetic data for minority groups. Recent studies on synthetic data generation using deep generative models mostly target prediction tasks. Our proposal differs in that we focus on handling imbalanced data and spurious correlations. More importantly, we develop a novel theory that rigorously characterizes the benefits of using the synthetic data, and shows the capacity of transformers in generating high-quality synthetic data for both labels and covariates. We further conduct intensive numerical experiments to demonstrate the efficacy of our proposed approach compared to some representative alternative solutions.<|reference_end|>
@article{nakada2024synthetic,
    title={Synthetic Oversampling: Theory and A Practical Approach Using LLMs to
  Address Data Imbalance},
    author={Ryumei Nakada, Yichen Xu, Lexin Li, Linjun Zhang},
    journal={arXiv preprint arXiv:2406.03628},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03628},
    primaryClass={stat.ML cs.LG}
}