This paper explores the intersection of technology and sleep pattern comprehension, presenting a cutting-edge two-stage framework that harnesses the power of Large Language Models (LLMs). The primary objective is to deliver precise sleep predictions paired with actionable feedback, addressing the limitations of existing solutions. This innovative approach involves leveraging the GLOBEM dataset alongside synthetic data generated by LLMs. The results highlight significant improvements, underlining the efficacy of merging advanced machine-learning techniques with a user-centric design ethos. Through this exploration, we bridge the gap between technological sophistication and user-friendly design, ensuring that our framework yields accurate predictions and translates them into actionable insights.

翻译：本文探讨了技术与睡眠模式理解的交叉领域，提出了一种创新的两阶段框架，充分发挥大语言模型（LLMs）的能力。主要目标是在弥补现有解决方案局限性的同时，提供精确的睡眠预测及可操作的反馈。该方法利用GLOBEM数据集以及LLMs生成的合成数据。研究结果显示出显著改进，突显了将先进机器学习技术与以用户为中心的设计理念相结合的有效性。通过这项探索，我们弥合了技术先进性与用户友好设计之间的鸿沟，确保所提出的框架既能产生精准预测，又能将其转化为可执行的洞见。