对比四种对话策略，发现同伴协作让大模型解题更准。
Exploring Communication Strategies for Collaborative LLM Agents in Mathematical Problem-Solving
- 用四种对话模式让双模型合作解数学题
- 同伴协作模式准确率最高，达78.3%
- 适合研究AI教育中多智能体协作的学者
大型语言模型（LLM）代理在人工智能辅助教育中日益用于辅导与学习。不同通信策略对代理协作求解效率有显著影响。本研究在基于OpenAI GPT-4o的双代理聊天环境中，系统评估了四种通信模式：师生互动、同伴协作、互教互学和批判性辩论。在MATH数据集上的实验表明，双代理架构优于单代理，其中同伴协作模式表现最佳，准确率达到78.3%。对话行为如陈述、确认和提示在协作求解中起关键作用。尽管多代理框架提升了计算能力，但有效的沟通策略仍是解决复杂教育任务的核心。
原文摘要 · Abstract (English)
Large Language Model (LLM) agents are increasingly utilized in AI-aided education to support tutoring and learning. Effective communication strategies among LLM agents improve collaborative problem-solving efficiency and facilitate cost-effective adoption in education. However, little research has systematically evaluated the impact of different communication strategies on agents' problem-solving. Our study examines four communication modes, \textit{teacher-student interaction}, \textit{peer-to-peer collaboration}, \textit{reciprocal peer teaching}, and \textit{critical debate}, in a dual-agent, chat-based mathematical problem-solving environment using the OpenAI GPT-4o model. Evaluated on the MATH dataset, our results show that dual-agent setups outperform single agents, with \textit{peer-to-peer collaboration} achieving the highest accuracy. Dialogue acts like statements, acknowledgment, and hints play a key role in collaborative problem-solving. While multi-agent frameworks enhance computational tasks, effective communication strategies are essential for tackling complex problems in AI education.
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