Text Overlap: An LLM with Human-like Conversational Behaviors

AbstractTraditional text-based human-AI interactions typically follow a strict turn-taking approach. This rigid structure limits conversational flow, unlike natural human conversations, which can freely incorporate overlapping speech. However, our pilot study suggests that even in text-based interfaces, overlapping behaviors such as backchanneling and proactive responses lead to more natural and functional exchanges. Motivated by these findings, we introduce text-based overlapping interactions as a new challenge in human-AI communication, characterized by real-time typing, diverse response types, and interruptions. To enable AI systems to handle such interactions, we define three core tasks: deciding when to overlap, selecting the response type, and generating utterances. We construct a synthetic dataset for these tasks and train OverlapBot, an LLM-driven chatbot designed to engage in text-based overlapping interactions. Quantitative and qualitative evaluations show that OverlapBot increases turn exchanges compared to traditional turn-taking systems, with users making 72% more turns and the chatbot 130% more turns, which is perceived as efficient by end-users. This finding supports overlapping interactions and enhances communicative efficiency and engagement.