虚构世界，真实连接：通过大语言模型开发社区讲故事社交聊天机器人
Fictional Worlds, Real Connections: Developing Community Storytelling Social Chatbots through LLMs
AI总结：
本文提出讲故事社交聊天机器人及故事工程方法，利用GPT-3将虚构游戏角色转化为社区中的“活”社交实体，并通过在Discord游戏社区的混合方法评估证明讲故事能显著提升聊天机器人的参与度和可信度。
摘要：
我们致力于解决讲故事与大语言模型（LLMs）的融合问题，以在社区环境中开发引人入胜且可信的社交聊天机器人（SCs）。受虚构角色增强社交互动的潜力所驱动，我们引入了讲故事社交聊天机器人（SSCs）以及故事工程的概念，将虚构游戏角色转化为玩家社区中的“活”社交实体。我们的故事工程过程包括三个步骤：（1）角色与故事创建，定义社交聊天机器人的个性与世界观；（2）向社区呈现现场故事，允许社交聊天机器人讲述挑战并寻求建议；（3）与社区成员沟通，实现社交聊天机器人与用户之间的互动。我们采用大语言模型GPT-3驱动我们的讲故事社交聊天机器人原型“David”和“Catherine”，并在Discord上的在线游戏社区“DE（化名）”中评估了它们的表现。我们基于对社区成员的问卷（N=15）和访谈（N=8）进行的混合方法分析表明，讲故事显著增强了社交聊天机器人在社区环境中的参与度和可信度。
英文摘要：
We address the integration of storytelling and Large Language Models (LLMs) to develop engaging and believable Social Chatbots (SCs) in community settings. Motivated by the potential of fictional characters to enhance social interactions, we introduce Storytelling Social Chatbots (SSCs) and the concept of story engineering to transform fictional game characters into "live" social entities within player communities. Our story engineering process includes three steps: (1) Character and story creation, defining the SC's personality and worldview, (2) Presenting Live Stories to the Community, allowing the SC to recount challenges and seek suggestions, and (3) Communication with community members, enabling interaction between the SC and users. We employed the LLM GPT-3 to drive our SSC prototypes, "David" and "Catherine," and evaluated their performance in an online gaming community, "DE (Alias)," on Discord. Our mixed-method analysis, based on questionnaires (N=15) and interviews (N=8) with community members, reveals that storytelling significantly enhances the engagement and believability of SCs in community settings.
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。
