We have reached a practical and realistic phase in human-support dialogue agents by developing a large language model (LLM). However, when requiring expert knowledge or anticipating the utterance content using the massive size of the dialogue database, we still need help with the utterance content's effectiveness and the efficiency of its output speed, even if using LLM. Therefore, we propose a framework that uses LLM asynchronously in the part of the system that returns an appropriate response and in the part that understands the user's intention and searches the database. In particular, noting that it takes time for the robot to speak, threading related to database searches is performed while the robot is speaking.

翻译：我们已通过开发大型语言模型（LLM）进入了实用且现实的人类支持对话智能体阶段。然而，在需要利用大规模对话数据库获取专家知识或预测话语内容时，即使采用LLM，我们仍需应对话语内容的有效性和输出速度效率的挑战。为此，我们提出了一种异步使用LLM的框架，该系统在返回恰当响应的模块以及理解用户意图并检索数据库的模块中均采用异步机制。特别地，考虑到机器人发声需要时间，框架在机器人说话的同时异步执行数据库检索的相关线程。