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Hugging Face Trending Papers

Investigating the Ability of Large Language Models to Analyze Recipes for Diabetes

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.

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arXiv AI
Sep 4

Investigating the Ability of Large Language Models to Analyze Recipes for Diabetes

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
llmsbenchmarks
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arXiv AI
Jun 10

MetaPlate: Counterfactual-Guided RAG-LLM Tool for Personalized Food Recommendation and Hyperglycemia Prevention

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
llmsragmultimodalsafety
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Hugging Face Trending Papers
Sep 8

It's All in the Way You Say It: The Role of Information Representation in LLM-Based Glycemic-Event Prediction

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.

llms
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arXiv Machine Learning
Aug 27

PA-CoT: Profile-Adaptive Chain-of-Thought for Personalized Nutritional Consulting

PA-CoT (Profile-Adaptive Chain-of-Thought) is a multi-stage prompting method that explicitly analyzes user profiles before generating responses for personalized nutritional consulting. The authors introduce the QPA benchmark, comprising 200 samples with structured profiles scored on four criteria, to evaluate personalization and safety. In a comparative study against 11 other methods, PA-CoT achieves the highest average score (4.21) and leads in both personalization (4.71) and safety (4.68) with statistically significant margins.

By Evgenii Garmashov, Nikita Kulin, Artur Khairullin, Viktor Zhuravlev, Daniil Sukhorukov, Mikhail Mozikov, Ilya Makarov, Sergey Muravyov
llmsbenchmarkssafety
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