  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.
