@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://aclanthology.org/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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T Chain-of-Discussion: A Multi-Model Framework for Complex Evidence-Based Question Answering
%A Tao, Mingxu
%A Zhao, Dongyan
%A Feng, Yansong
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F tao-etal-2025-chain
%X 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.
%U https://aclanthology.org/2025.coling-main.734/
%P 11070-11085
Markdown (Informal)
[Chain-of-Discussion: A Multi-Model Framework for Complex Evidence-Based Question Answering](https://aclanthology.org/2025.coling-main.734/) (Tao et al., COLING 2025)
ACL