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      Human and LLM-Based Voice Assistant Interaction: An Analytical Framework for User Verbal and Nonverbal Behaviors

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          Abstract

          Recent progress in large language model (LLM) technology has significantly enhanced the interaction experience between humans and voice assistants (VAs). This project aims to explore a user's continuous interaction with LLM-based VA (LLM-VA) during a complex task. We recruited 12 participants to interact with an LLM-VA during a cooking task, selected for its complexity and the requirement for continuous interaction. We observed that users show both verbal and nonverbal behaviors, though they know that the LLM-VA can not capture those nonverbal signals. Despite the prevalence of nonverbal behavior in human-human communication, there is no established analytical methodology or framework for exploring it in human-VA interactions. After analyzing 3 hours and 39 minutes of video recordings, we developed an analytical framework with three dimensions: 1) behavior characteristics, including both verbal and nonverbal behaviors, 2) interaction stages--exploration, conflict, and integration--that illustrate the progression of user interactions, and 3) stage transition throughout the task. This analytical framework identifies key verbal and nonverbal behaviors that provide a foundation for future research and practical applications in optimizing human and LLM-VA interactions.

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          Journal
          29 August 2024
          Article
          2408.16465
          bfd162b5-cb8d-46c3-9649-be20730865d2

          http://creativecommons.org/licenses/by/4.0/

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          Custom metadata
          cs.HC

          Human-computer-interaction
          Human-computer-interaction

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