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    <abstract>Recent progress in LLMs discussion suggests that multi-agent discussion improves the reasoning abilities of LLMs. In this work, we reevaluate this claim through systematic experiments, where we propose a novel group discussion framework to enrich the set of discussion mechanisms. Interestingly, our results show that a single-agent LLM with strong prompts can achieve almost the same best performance as the best existing discussion approach on a wide range of reasoning tasks and backbone LLMs. We observed that the multi-agent discussion performs better than a single agent only when there is no demonstration in the prompt. Further study reveals the common interaction mechanisms of LLMs during the discussion. Our code can be found in https://github.com/HKUST-KnowComp/LLM-discussion.</abstract>
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%0 Conference Proceedings
%T Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?
%A Wang, Qineng
%A Wang, Zihao
%A Su, Ying
%A Tong, Hanghang
%A Song, Yangqiu
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F wang-etal-2024-rethinking-bounds
%X Recent progress in LLMs discussion suggests that multi-agent discussion improves the reasoning abilities of LLMs. In this work, we reevaluate this claim through systematic experiments, where we propose a novel group discussion framework to enrich the set of discussion mechanisms. Interestingly, our results show that a single-agent LLM with strong prompts can achieve almost the same best performance as the best existing discussion approach on a wide range of reasoning tasks and backbone LLMs. We observed that the multi-agent discussion performs better than a single agent only when there is no demonstration in the prompt. Further study reveals the common interaction mechanisms of LLMs during the discussion. Our code can be found in https://github.com/HKUST-KnowComp/LLM-discussion.
%R 10.18653/v1/2024.acl-long.331
%U https://aclanthology.org/2024.acl-long.331
%U https://doi.org/10.18653/v1/2024.acl-long.331
%P 6106-6131

Markdown (Informal)

[Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?](https://aclanthology.org/2024.acl-long.331) (Wang et al., ACL 2024)

• Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key? (Wang et al., ACL 2024)

ACL

• Qineng Wang, Zihao Wang, Ying Su, Hanghang Tong, and Yangqiu Song. 2024. Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6106–6131, Bangkok, Thailand. Association for Computational Linguistics.