Imagine an AI commentator analyzing a high-stakes Guandan (a complex Chinese card game) match with the same strategic insight and witty banter as a seasoned professional. That's the ambitious goal researchers tackled in a new study exploring how Large Language Models (LLMs) can be used to generate engaging commentary for imperfect information games—games where players don't have a full view of the game state, like poker or, indeed, Guandan. The challenge isn't just about describing the cards played. It's about understanding hidden strategies, predicting player intentions, and explaining the subtle nuances of the game in a captivating way. The researchers developed a unique system that combines reinforcement learning (RL) with the language generation prowess of LLMs. The RL component generates realistic Guandan game scenarios, providing diverse and complex situations for the LLM to comment on. The LLM then uses a three-pronged approach to craft its commentary. First, a "State Commentary Guider" translates the raw game data into a human-readable narrative. Next, a "Theory of Mind (ToM) Strategy Analyzer" steps in, trying to infer the players' intentions and potential strategies—almost like the AI is reading the players' minds. Finally, a "Style Retriever" adds flavor and personality to the commentary, drawing on a database of real commentator phrases and idioms. This approach allows the LLM to move beyond simply reporting the game's events, and instead offer the kind of strategic analysis and engaging storytelling expected from a human expert. The researchers tested their system against several LLMs, including the powerful GPT-4. Impressively, their framework, bolstered by the RL training and smart retrieval system, outperformed even GPT-4 in several aspects, including accurately identifying key game moments and generating detailed explanations. While this research focused on Guandan, the implications are far broader. This innovative combination of RL and LLM technology opens doors to enhancing game experiences across a range of complex games, from esports titles to intricate board games. Imagine watching a StarCraft II match with AI commentary that dissects intricate build orders and predicts strategic shifts in real time. Or picture playing a complex board game with an AI companion that offers strategic guidance while also keeping the atmosphere light and entertaining. Of course, there are challenges ahead. Adapting this system to other games requires careful consideration of the specific rules and intricacies of each game. Furthermore, the "Theory of Mind" component, while promising, still has room for improvement in accurately capturing human strategic thinking. As this technology evolves, it's not just about enhancing entertainment. Imagine the potential for training and education. AI commentators could help players analyze past games, identify strategic errors, and improve their own gameplay. This is just the beginning of a fascinating journey into the world of AI-powered commentary, and it seems likely to reshape how we experience and interact with complex games in the years to come.