用大模型生成稀有事件数据，通过图传播精准找正例。
SYNAPSE-G: Bridging Large Language Models and Graph Learning for Rare Event Classification
- 大模型生成合成数据，构建相似性图进行半监督传播
- 在SST2和MHS数据集上显著提升正例召回率
- 适合标注稀缺的罕见事件分类任务
稀有事件的标注数据匮乏严重制约了机器学习模型的训练效果。本文提出SYNAPSE-G（基于图扩展的正样本合成增强），利用大语言模型生成合成训练数据，解决冷启动问题。这些合成数据作为种子，在与大规模未标注数据构建的相似性图上进行半监督标签传播，识别候选正例，并由人工或大模型标注。扩充后的数据用于训练分类器。理论分析揭示合成数据的质量（有效性与多样性）对方法精度与召回率的影响。在数据不平衡的SST2和MHS数据集上的实验表明，SYNAPSE-G在发现正例方面优于近邻搜索等基线方法。
原文摘要 · Abstract (English)
Scarcity of labeled data, especially for rare events, hinders training effective machine learning models. This paper proposes SYNAPSE-G (Synthetic Augmentation for Positive Sampling via Expansion on Graphs), a novel pipeline leveraging Large Language Models (LLMs) to generate synthetic training data for rare event classification, addressing the cold-start problem. This synthetic data serve as seeds for semi-supervised label propagation on a similarity graph constructed between the seeds and a large unlabeled dataset. This identifies candidate positive examples, subsequently labeled by an oracle (human or LLM). The expanded dataset then trains/fine-tunes a classifier. We theoretically analyze how the quality (validity and diversity) of the synthetic data impacts the precision and recall of our method. Experiments on the imbalanced SST2 and MHS datasets demonstrate SYNAPSE-G's effectiveness in finding positive labels, outperforming baselines including nearest neighbor search.
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