We introduce MAFALDA, a benchmark for fallacy classification that merges and unites previous fallacy datasets. It comes with a taxonomy that aligns, refines, and unifies existing classifications of fallacies. We further provide a manual annotation of a part of the dataset together with manual explanations for each annotation. We propose a new annotation scheme tailored for subjective NLP tasks, and a new evaluation method designed to handle subjectivity. We then evaluate several language models under a zero-shot learning setting and human performances on MAFALDA to assess their capability to detect and classify fallacies.
@article{arxiv.2311.09761,
title = {MAFALDA: A Benchmark and Comprehensive Study of Fallacy Detection and Classification},
author = {Chadi Helwe and Tom Calamai and Pierre-Henri Paris and Chloé Clavel and Fabian Suchanek},
journal= {arXiv preprint arXiv:2311.09761},
year = {2024}
}