MAFALDA: A Benchmark and Comprehensive Study of Fallacy Detection and Classification

• URL: http://arxiv.org/abs/2311.09761v2
• Date: Tue, 9 Apr 2024 20:49:26 GMT
• Title: MAFALDA: A Benchmark and Comprehensive Study of Fallacy Detection and Classification
• Authors: Chadi Helwe, Tom Calamai, Pierre-Henri Paris, Chloé Clavel, Fabian Suchanek,
• Abstract summary: 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 propose a new annotation scheme tailored for subjective NLP tasks, and a new evaluation method designed to handle subjectivity.
• Score: 8.687019236393123
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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