@inproceedings{yoffe-etal-2025-debunc,
    title = "{D}eb{U}nc: Improving Large Language Model Agent Communication With Uncertainty Metrics",
    author = "Yoffe, Luke  and
      Amayuelas, Alfonso  and
      Wang, William Yang",
    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/author-page-yu-wang-polytechnic/2025.findings-emnlp.1265/",
    doi = "10.18653/v1/2025.findings-emnlp.1265",
    pages = "23299--23315",
    ISBN = "979-8-89176-335-7",
    abstract = "Multi-agent debates have been introduced to improve the accuracy of Large Language Models (LLMs) by having multiple agents discuss solutions to a problem over several rounds of debate. However, models often generate incorrect yet confident-sounding responses, which can mislead the others. This issue arises partly because agents do not consider how confident their peers are. To address this, we propose DebUnc, a debate framework that uses uncertainty metrics to assess agent confidence. Confidence is then conveyed through textual prompts or via a modified attention mechanism that adjusts token weights. Evaluations across benchmarks show that attention-based methods are particularly effective and that performance continues to improve as uncertainty estimation becomes more reliable. The code is available at https://github.com/lukeyoffe/debunc."
}