Stance detection is an important task for many applications that analyse or
support online political discussions. Common approaches include fine-tuning
transformer based models. However, these models require a large amount of
labelled data, which might not be available. In this work, we present two
different ways to leverage LLM-generated synthetic data to train and improve
stance detection agents for online political discussions: first, we show that
augmenting a small fine-tuning dataset with synthetic data can improve the
performance of the stance detection model. Second, we propose a new active
learning method called SQBC based on the "Query-by-Comittee" approach. The key
idea is to use LLM-generated synthetic data as an oracle to identify the most
informative unlabelled samples, that are selected for manual labelling.
Comprehensive experiments show that both ideas can improve the stance detection
performance. Curiously, we observed that fine-tuning on actively selected
samples can exceed the performance of using the full dataset.

通过使用 LLM 生成的合成数据，我们提供了两种不同的方法来训练和改进在线政治讨论的立场检测代理：一是通过合成数据增加微调数据集以改善立场检测模型的性能；二是基于 “Query-by-Committee” 方法提出了一种新的主动学习方法 SQBC，利用 LLM 生成的合成数据作为预测标准，选择最具信息量的未标记样本进行手动标记。综合实验证明，这两种方法都能改善立场检测性能。有趣的是，我们观察到在主动选择样本上微调可以超过使用完整数据集的性能。