让大模型在狼人杀中实现自然的异步发言，像真人一样把握说话时机。
Time to Talk: LLM Agents for Asynchronous Group Communication in Mafia Games
- 设计双模块代理：生成内容与决定发言时机
- 在真实在线狼人杀中表现媲美人类玩家
- 适合研究人机协作社交或开发智能助手
大型语言模型（LLM）主要应用于同步交流场景，即人类与模型交替对话。然而，现实中的许多场景是异步的，如群聊、在线会议或社交游戏，不存在明确的发言轮次。本文提出一种自适应的异步LLM代理，包含两个模块：生成器决定说什么，调度器决定何时说。为评估该代理，我们收集了一个独特的在线狼人杀游戏数据集，让代理与真人玩家共同参与。结果表明，该代理在游戏表现和融入度方面均达到与人类相当的水平。分析显示，其发言时机模式接近人类行为，但在内容表达上仍有差异。代码与数据已全部开源。本工作为将大模型融入真实人际群体互动场景（如团队讨论、教育、职场）提供了新路径。
原文摘要 · Abstract (English)
LLMs are used predominantly in synchronous communication, where a human user and a model communicate in alternating turns. In contrast, many real-world settings are asynchronous. For example, in group chats, online team meetings, or social games, there is no inherent notion of turns. In this work, we develop an adaptive asynchronous LLM agent consisting of two modules: a generator that decides what to say, and a scheduler that decides when to say it. To evaluate our agent, we collect a unique dataset of online Mafia games, where our agent plays with human participants. Overall, our agent performs on par with human players, both in game performance metrics and in its ability to blend in with the other human players. Our analysis shows that the agent's behavior in deciding when to speak closely mirrors human patterns, although differences emerge in message content. We make all of our code and data publicly available. This work paves the way for integration of LLMs into realistic human group settings, from assistance in team discussions to educational and professional environments where complex social dynamics must be navigated.
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