Communication traits in text-based human-AI conversations play pivotal roles in shaping user experiences and perceptions of systems. With the advancement of large language models (LLMs), it is now feasible to analyze these traits at a more granular level. In this study, we explore the preferences of information workers regarding chatbot communication traits across seven applications. Participants were invited to participate in an interactive survey, which featured adjustable sliders, allowing them to adjust and express their preferences for five key communication traits: formality, personification, empathy, sociability, and humor. Our findings reveal distinct communication preferences across different applications; for instance, there was a preference for relatively high empathy in wellbeing contexts and relatively low personification in coding. Similarities in preferences were also noted between applications such as chatbots for customer service and scheduling. These insights offer crucial design guidelines for future chatbots, emphasizing the need for nuanced trait adjustments for each application.

翻译：文本人机对话中的沟通特质在塑造用户体验和系统认知方面发挥着关键作用。随着大语言模型（LLM）的进步，现在可以在更细粒度上分析这些特质。本研究探讨了信息工作者在七类应用场景中对聊天机器人沟通特质的偏好。参与者受邀参与一项交互式调查，该调查采用可调节滑块设计，允许他们调整并表达对五项关键沟通特质的偏好：正式性、拟人化、共情能力、社交性与幽默感。我们的研究揭示了不同应用场景间存在明显的沟通偏好差异；例如，在健康关怀场景中倾向于较高的共情能力，而在编程场景中则偏好较低的拟人化程度。同时，在客户服务和日程安排等应用场景中也观察到相似的偏好模式。这些发现为未来聊天机器人的设计提供了关键指导，强调了针对不同应用场景进行精细化特质调整的必要性。