FurChat：使用大语言模型的具身对话代理，结合开放与封闭域对话及面部表情
FurChat: An Embodied Conversational Agent using LLMs, Combining Open and Closed-Domain Dialogue with Facial Expressions
AI总结：
FurChat利用GPT-3.5和Furhat机器人，实现兼具开放与封闭域对话及面部表情的接待员具身代理，用于国家机器人馆访客互动。
摘要：
我们展示了一个具身对话代理，它能够充当接待员，并通过使用大语言模型（LLM）生成开放与封闭域对话的混合以及面部表情，从而开展引人入胜的对话。我们将该系统部署到Furhat机器人上，该机器人具有高度表现力，在交互过程中能够使用言语和非言语线索。该系统专为国家机器人馆（National Robotarium）设计，通过自然对话与访客互动，向他们提供关于设施、研究、新闻、即将举办的活动等信息。该系统利用最先进的GPT-3.5模型，基于提示工程生成此类信息以及领域通用对话和面部表情。
英文摘要：
We demonstrate an embodied conversational agent that can function as a receptionist and generate a mixture of open and closed-domain dialogue along with facial expressions, by using a large language model (LLM) to develop an engaging conversation. We deployed the system onto a Furhat robot, which is highly expressive and capable of using both verbal and nonverbal cues during interaction. The system was designed specifically for the National Robotarium to interact with visitors through natural conversations, providing them with information about the facilities, research, news, upcoming events, etc. The system utilises the state-of-the-art GPT-3.5 model to generate such information along with domain-general conversations and facial expressions based on prompt engineering.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
