The paper examines how large language models (LLMs) can assess whether recipes are suitable for people with diabetes. It introduces a benchmark of 7,607 recipes, split evenly between suitable and unsuitable, and tests three prompting strategies that embed varying levels of diabetes dietary guidelines. Results show that LLMs tend to be cautious in labeling recipes as suitable, and those that can reason with the guidelines—particularly Mistral‑7B and Llama‑70B—perform best.
arXiv:2412.15957v2 Announce Type: replace-cross
Abstract: The rapid development of large language models (LLMs) has transformed many industries, including healthcare. In practice, hospitals and patie...
By Ruize Shi, Hong Huang, Wei Zhou, Kehan Yin, Kai Zhao, Yun Zhao
arXiv:2606. 10120v1 Announce Type: cross Abstract: Postprandial hyperglycemia is a key risk factor for metabolic disorders; however, existing dietary guidance is often static, impractical, and insufficiently personalized, providing recommendations that are difficult to follow or not impactful.
By Asiful Arefeen, Carol Johnston, Hassan Ghasemzadeh
The paper investigates how well large language models (LLMs) can assess whether recipes are suitable for people with diabetes. It introduces a benchmark of 7,607 recipes, split evenly between suitable and unsuitable, and tests three prompting strategies that vary in how much diabetes dietary guidance they provide. Results show that LLMs tend to be cautious in labeling recipes as suitable, and those that can reason with dietary guidelines—particularly Mistral‑7B and Llama‑70B—perform best.
By Revathy Venkataramanan, Aditya Luthra, Venkatesan Nadimuthu, Amit Sheth
arXiv:2605. 18937v2 Announce Type: replace Abstract: Patient-managed Personal Health Records (PHRs) promises to empower patients to better understand their health; but information in the record is complex, potentially hindering insights.
By Rory Sayres, Kejia Chen, Ayush Jain, Matthew Thompson, Jonathan Richina, Xiang Yin, Jimmy Hu, Fan Zhang, Bob Lou, Mike Sanchez, Ines Mezerreg, Meredith Schreier, Hamsa Subramaniam, I-Ching Lee, Yugang Jia, Daniel Mcduff, Yossi Matias, Avinatan Hassidim, Dale Webster, Yun Liu, Jackie Barr, Quang Duong
arXiv:2603. 09995v2 Announce Type: replace-cross Abstract: Behavioral interview evaluation using large language models presents unique challenges that require structured assessment, realistic interviewer behavior simulation, and pedagogical value for candidate training.
By Kewen Zhu, Zixi Liu, Yanjing Li, Jing Chen