SQBC: Active Learning using LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions

arXiv:2404.08078v1 Announce Type: cross
Abstract: 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 …

abstract active learning applications arxiv cs.ai cs.cl cs.lg data detection discussions fine-tuning generated however llm political support synthetic synthetic data transformer type work