@inproceedings{zhang-etal-2025-statecloud,
    title = "{S}tate{C}loud at Critical Questions Generation: Prompt Engineering for Critical Question Generation",
    author = "Zhang, Jinghui  and
      Yang, Dongming  and
      Lin, Binghuai",
    editor = "Chistova, Elena  and
      Cimiano, Philipp  and
      Haddadan, Shohreh  and
      Lapesa, Gabriella  and
      Ruiz-Dolz, Ramon",
    booktitle = "Proceedings of the 12th Argument mining Workshop",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.argmining-1.24/",
    pages = "258--264",
    ISBN = "979-8-89176-258-9",
    abstract = "This paper presents StateCloud{'}s submission to the Critical Questions Generation (CQs-Gen) shared task at the Argument Mining Workshop 2025. To generate high-quality critical questions from argumentative texts, we propose a framework that combines prompt engineering with few-shot learning to effectively guide generative models. Additionally, we ensemble outputs from diverse large language models (LLMs) to enhance accuracy. Notably, our approach achieved 3rd place in the competition, demonstrating the viability of prompt engineering strategies for argumentative tasks."
}