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.

翻译：虚拟助手在帮助用户完成各类任务方面具有重要潜力。然而，这些系统在实际可用性上面临挑战，主要表现为效率低下及难以准确理解用户意图。借助大语言模型（LLMs）的最新进展，我们推出了GptVoiceTasker——一款旨在提升移动设备用户体验与任务效率的虚拟助手。GptVoiceTasker擅长智能解析用户指令，并通过执行相关设备交互来简化任务完成流程。该系统持续从历史用户指令中学习，以自动化后续使用场景，从而进一步提升执行效率。我们的实验证实了GptVoiceTasker卓越的指令解析能力及其任务自动化模块的精确性。在用户研究中，GptVoiceTasker将真实场景下的任务效率提升了34.85%，并获得参与者的积极反馈。我们已将GptVoiceTasker开源，以期通过提示工程促进LLMs在多样化任务中的应用研究，并利用用户使用数据持续提升系统效率。