Interactive user interfaces have increasingly explored AI's role in enhancing communication efficiency and productivity in collaborative tasks. AI tools such as chatbots and smart replies aim to enhance conversation quality and improve team performance. Early AI assistants, were limited by predefined knowledge bases and decision trees. However, the advent of Large Language Models (LLMs) such as ChatGPT has revolutionized AI assistants, employing advanced deep learning architecture to generate context-aware, coherent, and personalized responses. Consequently, ChatGPT-based AI assistants provide a more natural and efficient user experience across various tasks and domains. In this paper, we study how LLM models such as ChatGPT can be used to improve work efficiency in collaborative workplaces. Specifically, we present an LLM-based Smart Reply (LSR) system utilizing the ChatGPT to generate personalized responses in daily collaborative scenarios, while adapting to context and communication style based on prior responses. Our two-step process involves generating a preliminary response type (e.g., Agree, Disagree) to provide a generalized direction for message generation, thus reducing response drafting time. We conducted an experiment in which participants completed simulated work tasks, involving a Dual N-back test and subtask scheduling through Google Calendar while interacting with researchers posing as co-workers. Our findings indicate that the proposed LSR reduces overall workload, as measured by the NASA TLX, and improves work performance and productivity in the N-back task. We also provide qualitative feedback on participants' experiences as well as design recommendations so as to provide future directions for the design of these technologies.

翻译：交互式用户界面日益探索人工智能在提升协作任务中沟通效率与生产力方面的作用。聊天机器人和智能回复等AI工具旨在增强对话质量并改善团队绩效。早期AI助手受限于预定义的知识库与决策树。然而，ChatGPT等大语言模型的出现，凭借其先进的深度学习架构生成语境感知、连贯且个性化的回应，彻底革新了AI助手。因此，基于ChatGPT的AI助手能够为不同任务和领域提供更自然、高效的用户体验。本文研究如何利用ChatGPT等大语言模型提升协作工作场所的工作效率。具体而言，我们提出一个基于大语言模型的智能回复系统，该系统利用ChatGPT在日常协作场景中生成个性化回应，同时根据历史回应自适应调整语境与沟通风格。我们的两阶段流程包括：首先生成初步回应类型（如“同意”“不同意”），为消息生成提供通用方向，从而缩短回应撰写时间。我们开展了一项实验，参与者需完成模拟工作任务，包括双重N-back测试和通过谷歌日历进行子任务调度，并与扮演同事的研究者互动。结果表明，所提出的LSR系统降低了NASA TLX量表测量的整体工作负荷，并提升了N-back任务中的工作表现与生产力。此外，我们提供了关于参与者体验的定性反馈及设计建议，以期为这些技术的未来发展指明方向。