Jan Eliasz – Personalizing Offensiveness Detection in PoliticalTweets with Reasoning LLMs

We show that decomposing multi-label classification into a set of independent binary decisions offers clear practical advantages over structured output formulations: it substantially reduces parsing errors, works seamlessly with decoder-only architectures, and delivers faster inference when combined with prefix caching, without requiring any model retraining.