arXiv:2510.09783v2 Announce Type: replace Abstract: Oversampling is one of the most widely used approaches for addressing imbalanced classification. The core idea is to generate additional minority samples to rebalance the dataset. Most existing methods, such as SMOTE, require converting categorical variables into numerical vectors, which often leads to information loss. Recently, large language model (LLM)-based methods have been introduced to overcome this limitation. However, current LLM-based approaches typically generate minority samples with limited diversity, reducing robustness and gen
The rapid advancement of Large Language Models (LLMs) is pushing their application into more sophisticated data tasks like imbalanced classification, necessitating new methods to overcome inherent limitations.
Improving LLM-based data augmentation for imbalanced datasets can significantly enhance the performance and robustness of AI systems across various applications, from fraud detection to medical diagnostics.
The ability to generate diversified synthetic data using LLMs will reduce the need for labor-intensive data collection in minority classes and improve the accuracy of models trained on skewed distributions.
- · AI/ML researchers
- · Data scientists
- · Companies with imbalanced datasets (e.g., finance, healthcare)
- · LLM developers
- · Traditional oversampling methods without diversity
- · Manual data annotation services (in certain contexts)
More accurate and robust AI models for critical applications previously hindered by data imbalance.
Accelerated deployment of AI solutions in industries with scarce or sensitive minority class data.
Potential for new ethical concerns regarding biases introduced by synthetic data generation, even with diversity improvements.
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Read at arXiv cs.LG