@inproceedings{zhou-etal-2025-evaluating,
    title = "Evaluating Uncertainty Quantification Methods in Argumentative Large Language Models",
    author = "Zhou, Kevin  and
      Dejl, Adam  and
      Freedman, Gabriel  and
      Chen, Lihu  and
      Rago, Antonio  and
      Toni, Francesca",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.1184/",
    doi = "10.18653/v1/2025.findings-emnlp.1184",
    pages = "21700--21711",
    ISBN = "979-8-89176-335-7",
    abstract = "Research in uncertainty quantification (UQ) for large language models (LLMs) is increasingly important towards guaranteeing the reliability of this groundbreaking technology. We explore the integration of LLM UQ methods in argumentative LLMs (ArgLLMs), an explainable LLM framework for decision-making based on computational argumentation in which UQ plays a critical role. We conduct experiments to evaluate ArgLLMs' performance on claim verification tasks when using different LLM UQ methods, inherently performing an assessment of the UQ methods' effectiveness. Moreover, the experimental procedure itself is a novel way of evaluating the effectiveness of UQ methods, especially when intricate and potentially contentious statements are present. Our results demonstrate that, despite its simplicity, direct prompting is an effective UQ strategy in ArgLLMs, outperforming considerably more complex approaches."
}