@inproceedings{tao-etal-2025-chain,
    title = "Chain-of-Discussion: A Multi-Model Framework for Complex Evidence-Based Question Answering",
    author = "Tao, Mingxu  and
      Zhao, Dongyan  and
      Feng, Yansong",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.coling-main.734/",
    pages = "11070--11085",
    abstract = "Open-ended question answering requires mod- els to find appropriate evidence to form well-reasoned, comprehensive and helpful answers. In practical applications, models also need to engage in extended discussions on potential scenarios closely relevant to the question. With augmentation of retrieval module, open-source Large Language Models (LLMs) can produce coherent answers often with different focuses, but are still sub-optimal in terms of reliable ev- idence selection and in-depth question analysis. In this paper, we propose a novel Chain-of- Discussion framework to leverage the synergy among multiple open-source LLMs aiming to provide more correct and more comprehensive answers for open-ended QA, although they are not strong enough individually. Our exper- iments show that discussions among multiple LLMs play a vital role in enhancing the quality of answers."
}