通过分组辩论大幅降低大模型推理的计算成本。
GroupDebate: Enhancing the Efficiency of Multi-Agent Debate Using Group Discussion
- 将代理分成小组内辩论，跨组共享中间结果。
- 最多节省51.7%的令牌消耗，准确率提升25%。
- 适合需要高效逻辑推理的复杂任务场景。
近年来，大语言模型在多种自然语言处理任务中展现出卓越能力。研究广泛探索如何提升逻辑推理能力，如思维链、自一致思维链、思维树及多智能体辩论。在多智能体辩论中，增加代理数量和辩论轮次可显著提升性能，但也会急剧增加令牌消耗，限制了该技术的可扩展性。本文提出一种新方法，显著降低多智能体辩论中的令牌开销：将所有代理划分为多个辩论小组，代理在组内进行辩论，并在组间共享中间结果。在多个数据集上的对比实验表明，该方法可在辩论过程中最多减少51.7%的总令牌消耗，同时可能提升准确率高达25%。该方法显著提升了多智能体辩论的性能与效率。
原文摘要 · Abstract (English)
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.
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