Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs

This paper introduces a novel semi-supervised learning framework specifically designed for text classification tasks, effectively addressing the challenge of vast datasets with limited labeled examples. By integrating multi-level similarity based data augmentation techniques from Retrieval-Augmented Generation (RAG) to Large Language Model (LLM) rewriting and traditional word substitution-we constructed an intelligent augmentation pipeline. This framework innovatively employs the selection of representative landmarks through clustering, which serve as intermediaries in the retrieval and rewriting processes, ensuring that the augmented data maintains a distribution similar to the original dataset. Empirical results show that even in complex text document classification scenarios with over 100 categories, our method achieves state-of-the-art accuracies of 95.41% and 82.43% on the Reuters and Web of Science datasets, respectively. These findings highlight the effectiveness and broad applicability of our semi-supervised learning approach for text classification tasks.