GroupDebate: Enhancing the Efficiency of Multi-Agent Debate Using Group Discussion
<|reference_start|>GroupDebate: Enhancing the Efficiency of Multi-Agent Debate Using Group Discussion: In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse NLP tasks. Extensive research has explored how to enhance the logical reasoning abilities such as Chain-of-Thought, Chain-of-Thought with Self-Consistency, Tree-Of-Thoughts, and multi-agent debates. In the context of multi-agent debates, significant performance improvements can be achieved with an increasing number of agents and debate rounds. However, the escalation in the number of agents and debate rounds can drastically raise the tokens cost of debates, thereby limiting the scalability of the multi-agent debate technique. To better harness the advantages of multi-agent debates in logical reasoning tasks, this paper proposes a method to significantly reduce token cost in multi-agent debates. This approach involves dividing all agents into multiple debate groups, with agents engaging in debates within their respective groups and sharing interim debate results between groups. Comparative experiments across multiple datasets have demonstrated that this method can reduce the total tokens by up to 51.7% during debates and while potentially enhancing accuracy by as much as 25%. Our method significantly enhances the performance and efficiency of interactions in the multi-agent debate.<|reference_end|>
@article{liu2024groupdebate:,
    title={GroupDebate: Enhancing the Efficiency of Multi-Agent Debate Using Group
  Discussion},
    author={Tongxuan Liu, Xingyu Wang, Weizhe Huang, Wenjiang Xu, Yuting Zeng, Lei
  Jiang, Hailong Yang, Jing Li},
    journal={arXiv preprint arXiv:2409.14051},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.14051},
    primaryClass={cs.CL cs.AI}
}