Beyond Turn-taking: Introducing Text-based Overlap into Human-LLM Interactions

• URL: http://arxiv.org/abs/2501.18103v1
• Date: Thu, 30 Jan 2025 03:01:01 GMT
• Title: Beyond Turn-taking: Introducing Text-based Overlap into Human-LLM Interactions
• Authors: JiWoo Kim, Minsuk Chang, JinYeong Bak,
• Abstract summary: We propose a novel approach that incorporates overlapping messages, mirroring natural human conversations. Our user study revealed that OverlapBot was perceived as more communicative and immersive than traditional turn-taking chatbots. We provide recommendations for implementing overlap-capable AI interactions to enhance the fluidity and engagement of text-based conversations.
• Score: 16.854609012936155
• License:
• Abstract: 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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