SQBC: Active Learning using LLM-Generated Synthetic Data for Stance
  Detection in Online Political Discussions
Stefan Sylvius Wagner Maike Behrendt Marc Ziegele Stefan Harmeling
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.