让大模型与人对话时能像真人一样抢话，提升交流流畅度。
Beyond Turn-taking: Introducing Text-based Overlap into Human-LLM Interactions
- 设计可支持双方同时发言的聊天机器人，模拟真实对话中的重叠行为。
- 用户实验显示，重叠交互使对话更自然、更沉浸，响应速度更快。
- 适合追求高互动感与真实感对话系统的开发者参考。
传统文本型人机交互通常遵循严格的轮流发言模式。本研究提出一种新方法，引入消息重叠机制，模仿自然人类对话。通过初步调研发现，即使在文本场景中，用户也会自发出现如“我今天去了——”“嗯”这类重叠行为。基于此，我们开发了名为 OverlapBot 的原型聊天机器人，允许用户和AI同时发起消息。用户研究表明，相比传统轮流对话，OverlapBot被感知为更具沟通性与沉浸感，促进了更快、更自然的交互。研究结果深化了对重叠交互设计空间的理解，并为实现具备重叠能力的AI交互提供了实践建议。
原文摘要 · Abstract (English)
Traditional text-based human-AI interactions often adhere to a strict turn-taking approach. In this research, we propose a novel approach that incorporates overlapping messages, mirroring natural human conversations. Through a formative study, we observed that even in text-based contexts, users instinctively engage in overlapping behaviors like "A: Today I went to-" "B: yeah." To capitalize on these insights, we developed OverlapBot, a prototype chatbot where both AI and users can initiate overlapping. Our user study revealed that OverlapBot was perceived as more communicative and immersive than traditional turn-taking chatbot, fostering faster and more natural interactions. Our findings contribute to the understanding of design space for overlapping interactions. We also provide recommendations for implementing overlap-capable AI interactions to enhance the fluidity and engagement of text-based conversations.
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