Human-in-the-Loop, Active Learning, Large Language Models, Dissonance, Stance

Transfer and active learning for dissonance detection: Addressing the rare-class challenge
Transfer and active learning for dissonance detection: Addressing the rare-class challenge

This study explores transfer- and active learning solutions for rare-class problems, focusing on detecting cognitive dissonance in social media. We propose a probability-of-rare-class (PRC) approach for selecting samples and evaluate various acquisition strategies. We find that PRC effectively guides annotations and improves model accuracy, while specific transfer-learning sequences enhance initial performance but don't benefit subsequent active learning iterations.

Jan 1, 2023