For extremely weak-supervised text classification, pioneer research generates pseudo labels by mining texts similar to the class names from the raw corpus, which may end up with very limited or even no samples for the minority classes. Recent works have started to generate the relevant texts by prompting LLMs using the class names or definitions; however, there is a high risk that LLMs cannot generate in-distribution (i.e., similar to the corpus where the text classifier will be applied) data, leading to ungeneralizable classifiers. In this paper, we combine the advantages of these two approaches and propose to bridge the gap via a novel framework, \emph{text grafting}, which aims to obtain clean and near-distribution weak supervision for minority classes. Specifically, we first use LLM-based logits to mine masked templates from the raw corpus, which have a high potential for data synthesis into the target minority class. Then, the templates are filled by state-of-the-art LLMs to synthesize near-distribution texts falling into minority classes. Text grafting shows significant improvement over direct mining or synthesis on minority classes. We also use analysis and case studies to comprehend the property of text grafting.

综合了弱监督文本分类、伪标签、LLM以及少数类数据合成的优势，提出了一种名为文本嫁接的新框架，旨在获得少数类的干净且接近分布的弱监督，通过使用基于LLM的logits从原始语料库中挖掘掩码模板，并通过先进的LLM填充模板来合成接近分布的文本，从而显著改进了直接挖掘或合成少数类数据的效果。同时通过分析和案例研究来理解文本嫁接的特性。

文本嫁接：用于文本分类中少数类的近分布弱监督