用GMM和LLM生成少数类文本，提升不均衡数据的聚类效果
Imbalanced Data Clustering via Targeted Data Augmentation Using GMM and LLM
- 结合GMM定位少数簇，再用LLM生成合成文本扩充
- 在多个不均衡数据集上保持聚类性能并提升可解释性
- 适合处理少样本话题的无监督文本聚类任务
在自然语言处理中，处理低频话题具有挑战性，尤其在无监督聚类任务中，模型可能难以充分捕捉少数类别。本文提出一种新颖的无监督数据增强方法，融合高斯混合模型（GMM）与大语言模型（LLM）。GMM凭借其灵活性和鲁棒性，能有效识别数据中代表性不足的区域；而LLM则生成合成文档以丰富这些区域，改善其表征。在多个不均衡文本数据集上的实验表明，该方法在所有情况下均保持聚类性能，并常显著提升聚类结果的可解释性，为无监督NLP任务中的数据表征优化提供了一种稳健且可扩展的解决方案。
原文摘要 · Abstract (English)
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.
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