Text classification is a fundamental task in natural language processing, pivotal to various applications such as query optimization, data integration, and schema matching. While neural network-based models, such as CNN and BERT, have demonstrated remarkable performance in text classification, their effectiveness heavily relies on abundant labeled training data. This dependency makes these models less effective in dynamic few-shot text classification, where labeled data is scarce, and target labels frequently evolve based on application needs. Recently, large language models (LLMs) have shown promise due to their extensive pretraining and contextual understanding. Current approaches provide LLMs with text inputs, candidate labels, and additional side information (e.g., descriptions) to predict text labels. However, their effectiveness is hindered by the increased input size and the noise introduced through side information processing. To address these limitations, we propose a graph-based online retrieval-augmented generation framework, namely GORAG, for dynamic few-shot text classification. GORAG constructs and maintains an adaptive information graph by extracting side information across all target texts, rather than treating each input independently. It employs a weighted edge mechanism to prioritize the importance and reliability of extracted information and dynamically retrieves relevant context using a minimum-cost spanning tree tailored for each text input. Empirical evaluations demonstrate that GORAG outperforms existing approaches by providing more comprehensive and accurate contextual information.

本研究探讨了在标注数据稀缺且目标标签频繁变化的动态少样本文本分类中，现有神经网络模型的局限性。提出了一种名为GORAG的框架，通过构建自适应信息图，从多文本中提取和动态检索相关信息，从而显著提高了文本分类的准确性和全面性。研究表明，GORAG在提供更全面的上下文信息方面优于现有方法，具有重要的应用潜力。

基于图的检索增强生成动态少样本文本分类