  Recent advancements in large language models (LLMs) have unlocked the
potential for generating high-quality game commentary. However, producing
insightful and engaging commentary for complex games with incomplete
information remains a significant challenge. In this paper, we introduce a
novel commentary method that combine Reinforcement Learning (RL) and LLMs,
tailored specifically for the Chinese card game \textit{Guandan}. Our system
leverages RL to generate intricate card-playing scenarios and employs LLMs to
generate corresponding commentary text, effectively emulating the strategic
analysis and narrative prowess of professional commentators. The framework
comprises a state commentary guide, a Theory of Mind (ToM)-based strategy
analyzer, and a style retrieval module, which seamlessly collaborate to deliver
detailed and context-relevant game commentary in the Chinese language
environment. We empower LLMs with ToM capabilities and refine both retrieval
and information filtering mechanisms. This facilitates the generation of
personalized commentary content. Our experimental results showcase the
substantial enhancement in performance achieved by the proposed commentary
framework when applied to open-source LLMs, surpassing the performance of GPT-4
across multiple evaluation metrics.
