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.
@article{arxiv.2310.16242,
title = {ZzzGPT: An Interactive GPT Approach to Enhance Sleep Quality},
author = {Yonchanok Khaokaew and Kaixin Ji and Thuc Hanh Nguyen and Hiruni Kegalle and Marwah Alaofi and Hao Xue and Flora D. Salim},
journal= {arXiv preprint arXiv:2310.16242},
year = {2024}
}