@inproceedings{grecu-cocarascu-2026-topic,
    title = "Topic-Guided Prompting for Argument Stance Classification",
    author = "Grecu, Bogdan  and
      Cocarascu, Oana",
    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-1.4/",
    pages = "32--36",
    ISBN = "979-8-89176-399-9",
    abstract = "Stance classification is a core task in argument mining and subjectivity analysis, crucial for understanding public discourse and opinion dynamics on social media. Despite their impressive few-shot capabilities, Large Language Models (LLMs) remain sensitive to prompt construction, including the selection and ordering of in-context examples. In this paper, we propose a Topic-Guided prompting method for argument stance classification that dynamically integrates topic-specific information into the few-shot context. We evaluate our method on five LLMs across three datasets spanning formal debates and user-generated online comments. Our extensive evaluation shows that our proposed Topic-Guided prompting outperforms standard few-shot prompting and state-of-the-art example selection strategies. Further analysis indicates that our method reduces the bias towards the `support' class observed in several models, resulting in more balanced predictions across stances and thus a more robust approach to stance classification."
}