Interactive user interfaces have increasingly explored AI's role in enhancing communication efficiency and productivity in collaborative tasks. The emergence of Large Language Models (LLMs) such as ChatGPT has revolutionized conversational agents, employing advanced deep learning techniques to generate context-aware, coherent, and personalized responses. Consequently, LLM-based AI assistants provide a more natural and efficient user experience across various scenarios. In this paper, we study how LLM models 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 professional 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 where participants completed simulated work tasks involving a Dual N-back test and subtask scheduling through Google Calendar while interacting with 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 analysis based on participants' experiences, as well as design considerations to provide future directions for improving such implementations.

翻译：交互式用户界面日益探索人工智能在协作任务中提升沟通效率与生产力的作用。以ChatGPT为代表的大语言模型（LLMs）的出现，通过运用先进的深度学习技术生成上下文感知、连贯且个性化的回复，彻底革新了对话智能体。因此，基于LLM的AI助手能在多种场景中提供更自然、高效的用户体验。本文研究如何利用LLM模型提升协作工作场所的工作效率。具体而言，我们提出了一种基于大语言模型的智能回复（LSR）系统，该系统借助ChatGPT在专业协作场景中生成个性化回复，并根据先前的回复自适应调整语境与沟通风格。我们的两步流程包括：首先生成初步回复类型（如“同意”、“不同意”），为消息生成提供通用方向，从而减少回复起草时间。我们开展了一项实验，参与者需完成模拟工作任务，包括双N-back测试及通过谷歌日历协调子任务，同时与同事进行交互。研究结果表明，所提出的LSR方法通过NASA TLX量表测量，能降低整体工作负荷，并提升N-back任务中的工作表现与生产力。我们还基于参与者体验提供了定性分析，并提出了设计考量，为改进此类实现提供未来方向。