@inproceedings{jose-greenstadt-2025-llms,
    title = "{LLM}s for Detection and Classification of Persuasion Techniques in {S}lavic Parliamentary Debates and Social Media Texts",
    author = "Jose, Julia  and
      Greenstadt, Rachel",
    editor = "Piskorski, Jakub  and
      P{\v{r}}ib{\'a}{\v{n}}, Pavel  and
      Nakov, Preslav  and
      Yangarber, Roman  and
      Marcinczuk, Michal",
    booktitle = "Proceedings of the 10th Workshop on Slavic Natural Language Processing (Slavic NLP 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.bsnlp-1.23/",
    pages = "202--216",
    ISBN = "978-1-959429-57-9",
    abstract = "We present an LLM-based method for the Slavic NLP 2025 shared task on detection and classification of persuasion techniques in parliamentary debates and social media. Our system uses OpenAI{'}s GPT models (gpt-4o-mini) and reasoning models (o4-mini) with chain-of-thought prompting, enforcing a geq 0.99 confidence threshold for verbatim span extraction. For subtask 1, each paragraph in the text is labeled ``true'' if any of the 25 persuasion techniques is present. For subtask 2, the model returns the full set of techniques used per paragraph. Across Bulgarian, Croatian, Polish, Russian, and Slovenian, we achieve Subtask 1 micro-F1 of 81.7{\%}, 83.3{\%}, 81.6{\%}, 73.5{\%}, 62.0{\%}, respectively, and Subtask 2 F1 of 41.0{\%}, 44.4{\%}, 41.9{\%}, 29.3{\%}, 29.9{\%}, respectively. Our system ranked in the top 2 for Subtask 2 and top 7 for Subtask 1."
}