Imbalanced Data Clustering via Targeted Data Augmentation Using GMM and LLM
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Computer Science > Computation and Language
Title: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.
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.28635 [cs.CL] |
| (or arXiv:2607.28635v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.28635
arXiv-issued DOI via DataCite
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| Journal reference: | Advances in Intelligent Data Analysis: 23rd International Symposium on Intelligent Data Analysis; IDA 2025; Proceedings; pp 246-260 |
| Related DOI: | https://doi.org/10.1007/978-3-031-91398-3
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