Large Language Models for Imbalanced Classification: Diversity makes the difference

📰 ArXiv cs.AI

Learn how Large Language Models (LLMs) can improve imbalanced classification by generating diverse samples, and why diversity matters in this context.

advanced Published 9 Jun 2026

Action Steps

Apply LLMs to generate additional minority samples for imbalanced classification datasets
Use techniques like SMOTE as a baseline for comparison
Configure LLMs to produce diverse samples by experimenting with different parameters and prompts
Test the performance of LLM-based methods against traditional oversampling techniques
Compare the results of LLM-based methods with other state-of-the-art approaches for imbalanced classification

Who Needs to Know This

Data scientists and machine learning engineers working on classification tasks can benefit from this knowledge to improve their models' performance on imbalanced datasets. This is particularly useful for teams dealing with real-world datasets where class imbalance is common.

Key Insight

💡 Diversity in generated samples is crucial for improving the performance of LLMs in imbalanced classification tasks.

Key Takeaways

Learn how Large Language Models (LLMs) can improve imbalanced classification by generating diverse samples, and why diversity matters in this context.

Full Article

Title: Large Language Models for Imbalanced Classification: Diversity makes the difference

Abstract:
arXiv:2510.09783v2 Announce Type: replace-cross 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 LL

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