@inproceedings{nguyen-nguyen-2025-tdnguyen,
    title = "Tdnguyen at {CQ}s-Gen 2025: Adapt Large Language Models with Multi-Step Reasoning for Critical Questions Generation",
    author = "Nguyen, Tien-Dat  and
      Nguyen, Duc-Vu",
    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/landing_page/2025.argmining-1.25/",
    doi = "10.18653/v1/2025.argmining-1.25",
    pages = "265--280",
    ISBN = "979-8-89176-258-9",
    abstract = "This paper explores the generation of Critical Questions (CQs) from argumentative texts using multi-step reasoning techniques, specifically Chain-of-Thoughts (CoT) and Tree-of-Thoughts (ToT) prompting frameworks. CQs are essential for enhancing critical thinking and improving decision-making across various domains. Despite the promise of Large Language Models (LLMs) in this task, generating contextually relevant and logically sound questions remains a challenge. Our experiments show that CoT-based prompting strategies, including Zero-shot and One-shot methods, significantly outperform baseline models in generating high-quality CQs. While ToT prompting offers a more flexible reasoning structure, it was less effective than CoT in this task. We suggest exploring more advanced or computationally intense multi-step reasoning techniques, as well as alternative tree structures for the ToT framework, to further improve CQs-Gen systems."
}