@inproceedings{papadopulos-etal-2026-beyond,
    title = "Beyond Logical Forms: {LLM}-Extracted Patterns for Fallacy Classification",
    author = "Papadopulos, Eleni  and
      Alam, Firoj  and
      Da San Martino, Giovanni",
    editor = "Elaraby, Mohamed  and
      Hautli-Janisz, Annette  and
      Romberg, Julia  and
      Musi, Elena  and
      Ruggeri, Federico  and
      Lawrence, John",
    booktitle = "Proceedings of the 13th Workshop on Argument Mining and Reasoning",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.argmining-1.2/",
    pages = "5--18",
    ISBN = "979-8-89176-399-9",
    abstract = "In today{'}s fast-paced information era, logical fallacies, defined as defective patterns of reasoning, inevitably contribute to the growth of information disorder. However, often fallacies appear in nuanced forms that complicate automated classification. In this study, we investigate whether merging abstract logical structures with context-level linguistic cues proves beneficial for fallacy classification, developing a framework that inductively extracts such patterns from fallacious examples and their explanations using Large Language Models (LLMs). We evaluate the impact of these patterns across different LLMs and experimental zero- and one-shot configurations, showing statistically significant improvements over zero-shot baselines and outperforming competing approaches. Cross-dataset experiments validate generalization, establishing data-driven pattern extraction as an effective method for generating logical representations."
}