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Fallacies can be used to spread disinformation, fake news, and propaganda,underlining the importance of their detection. Automated detection andclassification of fallacies, however, remain challenging, mainly because of theinnate subjectivity of the task and the need for a comprehensive, unifiedapproach in existing research. Addressing these limitations, our studyintroduces a novel taxonomy of fallacies that aligns and refines previousclassifications, a new annotation scheme tailored for subjective NLP tasks, anda new evaluation method designed to handle subjectivity, adapted to precision,recall, and F1-Score metrics. Using our annotation scheme, the paper introducesMAFALDA (Multi-level Annotated FALlacy DAtaset), a gold standard dataset.MAFALDA is based on examples from various previously existing fallacy datasetsunder our unified taxonomy across three levels of granularity. We then evaluateseveral language models under a zero-shot learning setting using MAFALDA toassess their fallacy detection and classification capability. Our comprehensiveevaluation not only benchmarks the performance of these models but alsoprovides valuable insights into their strengths and limitations in addressingfallacious reasoning.

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