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，这些系统在实际应用中面临可用性挑战，主要表现为效率低下且难以准确理解用户意图。借助大型语言模型（LLM）的最新进展，我们提出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执行相关设备操作，以简化任务完成流程。该系统通过持续学习历史用户指令来优化后续使用，进一步提升了执行效率。实验结果表明，GptVoiceTasker具有卓越的指令理解能力与精准的任务自动化模块。用户研究显示，GptVoiceTasker在真实场景中可将任务效率提升34.85%，并获得了参与者的积极反馈。我们已将GptVoiceTasker开源，期待通过提示工程与用户使用数据挖掘，进一步探索LLM在多任务场景下的应用以提升效率。