Synthetic Oversampling: Theory and A Practical Approach Using LLMs to Address Data Imbalance

TL;DR

This paper develops theoretical foundations for using large language models to generate synthetic data for addressing class imbalance and spurious correlations, supported by extensive experiments.

Contribution

It introduces a systematic theoretical analysis of synthetic oversampling with LLMs, including benefits quantification, scaling laws, and sample quality assessment.

Findings

Synthetic oversampling benefits are explicitly quantified.

Scaling laws for data augmentation are derived.

High-quality synthetic samples can be generated by transformer models.

Abstract

Imbalanced classification and spurious correlation are common challenges in data science and machine learning. Both issues are linked to data imbalance, with certain groups of data samples significantly underrepresented, which in turn would compromise the accuracy, robustness and generalizability of the learned models. Recent advances have proposed leveraging the flexibility and generative capabilities of large language models (LLMs), typically built on transformer architectures, to generate synthetic samples and to augment the observed data. In the context of imbalanced data, LLMs are used to oversample underrepresented groups and have shown promising improvements. However, there is a clear lack of theoretical understanding of such synthetic data approaches. In this article, we develop novel theoretical foundations to systematically study the roles of synthetic samples in addressing imbalanced classification and spurious correlation. Specifically, we first explicitly quantify the benefits of synthetic oversampling. Next, we analyze the scaling dynamics in synthetic data augmentation, and derive the corresponding scaling law. Finally, we demonstrate the capacity of transformer models to generate high-quality synthetic samples. We further conduct extensive numerical experiments to validate the efficacy of the LLM-based synthetic oversampling and augmentation.