  Virtual assistants have the potential to play an important role in helping
users achieves different tasks. However, these systems face challenges in their
real-world usability, characterized by inefficiency and struggles in grasping
user intentions. Leveraging recent advances in Large Language Models (LLMs), we
introduce GptVoiceTasker, a virtual assistant poised to enhance user
experiences and task efficiency on mobile devices. GptVoiceTasker excels at
intelligently deciphering user commands and executing relevant device
interactions to streamline task completion. The system continually learns from
historical user commands to automate subsequent usages, further enhancing
execution efficiency. Our experiments affirm GptVoiceTasker's exceptional
command interpretation abilities and the precision of its task automation
module. In our user study, GptVoiceTasker boosted task efficiency in real-world
scenarios by 34.85%, accompanied by positive participant feedback. We made
GptVoiceTasker open-source, inviting further research into LLMs utilization for
diverse tasks through prompt engineering and leveraging user usage data to
improve efficiency.
