🤖 AI Summary
To address the scalability bottleneck in multi-agent debate—specifically, the exponential growth in token consumption with increasing agent count and debate rounds—this paper proposes a *grouped multi-agent debate* architecture. Agents are partitioned into disjoint subgroups that conduct parallel internal debates; inter-group information exchange and a dynamic consensus mechanism then aggregate intermediate results efficiently. This approach breaks the traditional linear scaling constraint and represents the first systematic integration of grouping principles into multi-agent debate frameworks. Extensive experiments across multiple logical reasoning benchmarks demonstrate that our method reduces token consumption by up to 51.7% relative to baseline methods, while simultaneously improving accuracy by up to 25%. The architecture thus achieves a significant trade-off improvement between computational efficiency and reasoning performance.
📝 Abstract
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.