Summary of Chain-of-discussion: a Multi-model Framework For Complex Evidence-based Question Answering, by Mingxu Tao and Dongyan Zhao and Yansong Feng
Chain-of-Discussion: A Multi-Model Framework for Complex Evidence-Based Question Answering
by Mingxu Tao, Dongyan Zhao, Yansong Feng
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel framework called Chain-of-Discussion to improve the performance of open-source Large Language Models (LLMs) in generating comprehensive and helpful answers for open-ended question answering tasks. The current state-of-the-art LLMs can produce coherent answers, but they often lack reliable evidence selection and in-depth question analysis capabilities. By leveraging the synergy among multiple LLMs, the Chain-of-Discussion framework aims to provide more correct and comprehensive answers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Chain-of-Discussion framework uses open-source LLMs to engage in extended discussions on potential scenarios closely related to the question. This approach enables the models to find appropriate evidence and form well-reasoned answers. The authors demonstrate that discussions among multiple LLMs play a vital role in enhancing the quality of answers. |
Keywords
» Artificial intelligence » Question answering
