用大模型指导半监督学习，提升灾情微博分类效果。
LLM-guided Semi-Supervised Approaches for Social Media Crisis Data Classification
- 用大模型生成伪标签，引导小模型在少量标注数据下训练
- 5~25个标注样本时，新方法平均宏F1显著优于传统方法
- 小模型经大模型指导后，可超越零样本大模型，适合实际应用
半监督学习被用于提升灾难管理中社交媒体数据分析效果。本文首次对大语言模型（LLM）指导的半监督学习在灾情微博分类中的应用进行实证评估。我们对比了两种近期的LLM辅助半监督方法：VerifyMatch和LLM引导的协同训练（LG-CoTrain），以及经典半监督基线方法。结果表明，在每类仅5、10和25个标注样本的低资源场景下，LG-CoTrain在跨事件平均宏F1上显著优于传统方法；VerifyMatch表现也具竞争力，并展现出良好的校准能力。随着标注样本增加，性能差距缩小，自训练成为强基线。此外，我们发现紧凑的半监督模型在某些情况下能超越零样本的大规模语言模型。这一发现凸显了通过LLM指导的半监督学习，将大模型知识迁移至更小、更易部署模型的潜力，为真实世界灾情响应提供了可行路径。
原文摘要 · Abstract (English)
Semi-supervised learning approaches have been investigated as a means to enhance the analysis of social media data in disaster management contexts. In this work, we present the first empirical evaluation of large language model (LLM) guided semi-supervised learning for crisis related tweet classification. We compare two recent LLM assisted semi-supervised methods, VerifyMatch and LLM guided Co-Training ( LG-CoTrain), against established semi-supervised baselines. Our results show that LG-CoTrain significantly outperforms classical semi-supervised approaches in low resource settings with 5, 10 and 25 labeled examples per class, achieving the highest averaged Macro F1 across events. VerifyMatch achieves competitive performance while also demonstrating strong calibration properties. As the number of labeled examples increases, the performance gap narrows and Self Training emerges as a strong baseline. We further observe that compact semi-supervised models can, in some cases, outperform very large LLMs operating in zero-shot settings. This finding highlights the potential of transferring knowledge from LLMs into smaller and more deployable models through LLM guided semi-supervised learning, offering a practical pathway for real world disaster response applications. Our project repository on Github is here.
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