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：虚拟助手在协助用户完成各类任务方面具有重要潜力。然而，现有系统在实际可用性上面临挑战，主要表现为效率低下和难以准确理解用户意图。借助大语言模型（LLMs）的最新进展，我们提出了GPTVoiceTasker，一种旨在提升移动设备用户体验和任务效率的虚拟助手。GPTVoiceTasker擅长智能解析用户指令，并执行相应的设备交互以简化任务流程。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。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。在我们的用户研究中，GPTVoiceTasker在真实场景中将任务效率提升了34.85%，并获得了参与者的积极反馈。我们已将GPTVoiceTasker开源，以鼓励通过提示工程进一步探索大语言模型在多样化任务中的应用，并利用用户使用数据来持续提升效率。