MAFALDA: A Benchmark and Comprehensive Study of Fallacy Detection and Classification
Chadi Helwe, Tom Calamai, Pierre-Henri Paris, Chloé Clavel, Fabian Suchanek
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.- Anthology ID:
- 2024.naacl-long.270
- Volume:
- Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Kevin Duh, Helena Gomez, Steven Bethard
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4810–4845
- Language:
- URL:
- https://aclanthology.org/2024.naacl-long.270
- DOI:
- 10.18653/v1/2024.naacl-long.270
- Cite (ACL):
- Chadi Helwe, Tom Calamai, Pierre-Henri Paris, Chloé Clavel, and Fabian Suchanek. 2024. MAFALDA: A Benchmark and Comprehensive Study of Fallacy Detection and Classification. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 4810–4845, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- MAFALDA: A Benchmark and Comprehensive Study of Fallacy Detection and Classification (Helwe et al., NAACL 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.naacl-long.270.pdf