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

no code implementations • 11 Apr 2024 • Stefan Sylvius Wagner, Maike Behrendt, Marc Ziegele, Stefan Harmeling

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.

Active Learning Stance Detection

AQuA -- Combining Experts' and Non-Experts' Views To Assess Deliberation Quality in Online Discussions Using LLMs

1 code implementation • 3 Apr 2024 • Maike Behrendt, Stefan Sylvius Wagner, Marc Ziegele, Lena Wilms, Anke Stoll, Dominique Heinbach, Stefan Harmeling

In this work, we introduce AQuA, an additive score that calculates a unified deliberative quality score from multiple indices for each discussion post.