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Large Language Models (LLMs) have revolutionized Natural Language Processingbut exhibit limitations, particularly in autonomously addressing novelchallenges such as reasoning and problem-solving. Traditional techniques likechain-of-thought prompting necessitate explicit human guidance. This paperintroduces a novel multi-agent communication framework, inspired by the CAMELmodel, to enhance LLMs' autonomous problem-solving capabilities. The frameworkemploys multiple LLM agents, each with a distinct persona, engaged inrole-playing communication, offering a nuanced and adaptable approach todiverse problem scenarios. Extensive experimentation demonstrates theframework's superior performance and adaptability, providing valuable insightsinto the collaborative potential of multiple agents in overcoming thelimitations of individual models.

LLM Harmony: Multi-Agent Communication for Problem Solving