Imbalanced Data Clustering via Targeted Data Augmentation Using GMM and LLM

Researchers propose a new unsupervised data augmentation method combining Gaussian Mixture Models (GMMs) and Large Language Models (LLMs) to improve clustering of underrepresented topics in imbalanced text datasets. The method uses GMMs to detect minority clusters and LLMs to generate synthetic documents that enrich these clusters, preserving clustering performance and often enhancing interpretability. The approach offers a scalable solution for improving data representation in unsupervised NLP tasks.

arXiv:2607.28635v1 Announce Type: new Abstract: In Natural Language Processing NLP , dealing with underrepresented topics is challenging, especially in unsupervised tasks where clustering might not adequately capture minority topics. To tackle this challenge, our paper presents a novel unsupervised data augmentation method that integrates Gaussian Mixture Models GMMs and Large Language Models LLMs . Due to their flexibility and robustness, GMMs can detect clusters corresponding to underrepresented areas in the data, while LLMs create synthetic documents to enrich these clusters and improve their representation. Experiments on various imbalanced text datasets demonstrate that our approach preserves clustering performance in all cases and often enhances cluster interpretability, offering a robust and scalable solution for improving data representation in unsupervised NLP tasks.