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: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
arXiv:2609.22171v1 Announce Type: cross
Abstract: Precision healthcare, particularly for conditions like hypertension and cardiovascular disease, necessitates monitoring of dietary sodium intake. How...
By Mingyu Huang, Weiqing Min, Yuehui Fang, Yuna He, Shuqiang Jiang
The paper examines how the representation of physiological data affects the performance of large language models (LLMs) in predicting post‑meal blood glucose events for people with type 1 diabetes. Using the OhioT1DM dataset, the authors compare zero‑shot and few‑shot prompt‑based LLMs across 30, 60, and 90‑minute horizons, varying the textual encoding of glucose readings, derived descriptors, and contextual variables such as insulin, meals, carbs, and activity. Results show that while conventional supervised models excel at hyperglycemia prediction, certain prompt‑based LLM configurations outperform them for hypoglycemia, and that the way data is presented to the model is a key determinant of success, with added context not consistently improving outcomes.
arXiv:2609.22099v1 Announce Type: new
Abstract: Cooking is a complex process that transforms raw ingredients into delicious and nutritious dishes, yet the recipes that encode this process remain larg...
By Mansi Goel, Sumit Bhagat, Saloni Srivastava, Malav Patel, Shlok Vinodkumar Mehroliya, Ganesh Bagler
arXiv:2609.08772v1 Announce Type: new
Abstract: Large Language Models (LLMs) are increasingly being investigated for physiological time-series prediction, yet their effectiveness may depend not only...
By Andrea Apicella, Pasquale Arpaia, Matteo Orefice, Andrea Pollastro, Roberto Prevete