Text Overlap: An LLM with Human-like Conversational Behaviors

Traditional 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 textbased 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 Over-lapBot, 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.