Chain-of-Discussion: A Multi-Model Framework for Complex Evidence-Based Question Answering
<|reference_start|>Chain-of-Discussion: A Multi-Model Framework for Complex Evidence-Based Question Answering: Open-ended question answering requires models 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 evidence 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 \textbf{more correct} and \textbf{more comprehensive} answers for open-ended QA, although they are not strong enough individually. Our experiments show that discussions among multiple LLMs play a vital role in enhancing the quality of answers. We release our data and code at \url{https://github.com/kobayashikanna01/Chain-of-Discussion}.<|reference_end|>
@article{tao2024chain-of-discussion:,
    title={Chain-of-Discussion: A Multi-Model Framework for Complex Evidence-Based
  Question Answering},
    author={Mingxu Tao and Dongyan Zhao and Yansong Feng},
    journal={arXiv preprint arXiv:2402.16313},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16313},
    primaryClass={cs.CL cs.AI}
}