研究群体互动中对话设计对人机交互的影响，发现现有方法效果有限。
Are We Generalizing from the Exception? An In-the-Wild Study on Group-Sensitive Conversation Design in Human-Agent Interactions
- 在真实场景中测试两种社交代理的群体自适应对话设计
- 188名参与者在博物馆与机器人或虚拟人互动，未显著提升满意度
- 提示需多模态策略，而不仅是语言复数化
本文通过两项真实世界研究，探究群体自适应对话设计在两类社会性交互代理（SIAs）中的影响。这两类代理——社交机器人Furhat和虚拟人MetaHuman——均搭载结合混合检索与生成模型的对话人工智能（CAI）后端。研究在德国博物馆开展，共招募188名参与者，以两人、三人或更大群体形式与代理互动。尽管结果未显示群体敏感对话设计对感知满意度有显著影响，但揭示了在多人互动及不同实体形态（机器人与虚拟人）间适配CAI所面临的挑战，强调需采用超越语言复数化的多模态策略。这些发现为人类-代理交互（HAI）、人类-机器人交互（HRI）及更广泛的人机交互（HMI）领域提供了重要启示，助力未来群组对话自适应的研究。
原文摘要 · Abstract (English)
This paper investigates the impact of a group-adaptive conversation design in two socially interactive agents (SIAs) through two real-world studies. Both SIAs - Furhat, a social robot, and MetaHuman, a virtual agent - were equipped with a conversational artificial intelligence (CAI) backend combining hybrid retrieval and generative models. The studies were carried out in an in-the-wild setting with a total of $N = 188$ participants who interacted with the SIAs - in dyads, triads or larger groups - at a German museum. Although the results did not reveal a significant effect of the group-sensitive conversation design on perceived satisfaction, the findings provide valuable insights into the challenges of adapting CAI for multi-party interactions and across different embodiments (robot vs.\ virtual agent), highlighting the need for multimodal strategies beyond linguistic pluralization. These insights contribute to the fields of Human-Agent Interaction (HAI), Human-Robot Interaction (HRI), and broader Human-Machine Interaction (HMI), providing insights for future research on effective dialogue adaptation in group settings.
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