Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?
Qineng Wang, Zihao Wang, Ying Su, Hanghang Tong, Yangqiu Song
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.
BibTeX
@inproceedings{wang-etal-2024-rethinking-bounds,
title = "Rethinking the Bounds of {LLM} Reasoning: Are Multi-Agent Discussions the Key?",
author = "Wang, Qineng and
Wang, Zihao and
Su, Ying and
Tong, Hanghang and
Song, Yangqiu",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.331/",
doi = "10.18653/v1/2024.acl-long.331",
pages = "6106--6131"
}