Social robotics researchers are increasingly interested in multi-party
trained conversational agents. With a growing demand for real-world
evaluations, our study presents Large Language Models (LLMs) deployed in a
month-long live show at the Edinburgh Festival Fringe. This case study
investigates human improvisers co-creating with conversational agents in a
professional theatre setting. We explore the technical capabilities and
constraints of on-the-spot multi-party dialogue, providing comprehensive
insights from both audience and performer experiences with AI on stage. Our
human-in-the-loop methodology underlines the challenges of these LLMs in
generating context-relevant responses, stressing the user interface's crucial
role. Audience feedback indicates an evolving interest for AI-driven live
entertainment, direct human-AI interaction, and a diverse range of expectations
about AI's conversational competence and utility as a creativity support tool.
Human performers express immense enthusiasm, varied satisfaction, and the
evolving public opinion highlights mixed emotions about AI's role in arts.

社交机器人研究者对于多方训练的对话代理越来越感兴趣。本研究在爱丁堡节的一个月长的现场表演中使用大型语言模型（LLMs），调查了在职业剧院环境中，人类演员如何与对话代理合作创作。我们探索了即兴多方对话的技术能力和限制，并从观众和表演者的经验中提供了全面的见解。我们的 “人在环环” 的方法强调了这些 LLMs 在生成与上下文相关的回复方面的挑战，并强调了用户界面的关键作用。观众的反馈表明了对 AI 驱动的现场娱乐、人机直接互动以及对 AI 在创造力支持工具方面多样化的期望。人类演员表达了巨大的热情和不同程度的满意度，而不断演变的公众舆论则凸显了人们对于 AI 在艺术中的角色的复杂情感。