Imbalanced Data Clustering via Targeted Data Augmentation Using GMM and LLM

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.

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2607.28635 [cs.CL]

(or arXiv:2607.28635v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.28635 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1007/978-3-031-91398-3 Focus to learn more DOI(s) linking to related resources

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.