  Communication games, which we refer to as incomplete information games that
heavily depend on natural language communication, hold significant research
value in fields such as economics, social science, and artificial intelligence.
In this work, we explore the problem of how to engage large language models
(LLMs) in communication games, and in response, propose a tuning-free
framework. Our approach keeps LLMs frozen, and relies on the retrieval and
reflection on past communications and experiences for improvement. An empirical
study on the representative and widely-studied communication game,
``Werewolf'', demonstrates that our framework can effectively play Werewolf
game without tuning the parameters of the LLMs. More importantly, strategic
behaviors begin to emerge in our experiments, suggesting that it will be a
fruitful journey to engage LLMs in communication games and associated domains.
