ArticlePatterns (New York, N.Y.)2025
FoodSky: A food-oriented large language model that can pass the chef and dietetic examinations.
Article in Patterns (New York, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Applying Artificial Intelligence and machine learning in precision nutrition.Nature communications · 2026Review
- Nutrient-Aware Personalized Meal Recommendation Using Structured Food Knowledge and Constraint Verification.Foods (Basel, Switzerland) · 2026Article
- Using Large Language Models to Generate Dietary Feedback Similar to Human Experts in Weight Management: Experiments on Real-World Scenario Data.Bioengineering (Basel, Switzerland) · 2026Article
- Large language models in food and nutrition science: Opportunities, challenges, and the case of FoodyLLM.Current research in food science · 2026Article
- A comparative study of vision-language models for food ingredient recognition and nutrient estimation.Current research in food science · 2026Article
- An AI-driven multivariate approach for personalized healthy eating recommendations aligned with sustainable healthy diet food-group guidelines.Frontiers in nutrition · 2026Article
- Performance Evaluation of 3 Large Language Models for Nutritional Content Estimation from Food Images.Current developments in nutrition · 2025Article
- Evaluation of LLMs accuracy and consistency in the registered dietitian exam through prompt engineering and knowledge retrieval.Scientific reports · 2025Article
- The case for computational and artificial intelligence-based approaches for sustainable functional food systems for sub-Saharan Africa.Frontiers in nutrition · 2025Article
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Authors and funding
8 authors.
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Abstract
Food is the cornerstone of both survival and social life. With the increasing complexity of global dietary cultures, there is a growing demand for food intelligence to enable tasks like recipe recommendations and diet-disease correlation discovery. To address this, we introduce the food-oriented large language model (LLM) FoodSky, which offers fine-grained perception and reasoning on food data. We constructed a food corpus, FoodEarth, from various authoritative sources to enhance FoodSky's knowledge. We also developed the topic-based selective state space model and hierarchical topic retrieval augmented generation algorithms to improve FoodSky's ability to capture fine-grained food semantics and generate context-aware food-relevant text. Extensive experiments show that FoodSky significantly outperforms general-purpose LLMs on the Chinese National Chef Examination and Dietetic Examination, achieving an accuracy of 83.3% and 91.2%, respectively. Beyond enhancing culinary creativity and promoting healthier eating patterns, FoodSky aims to establish a new benchmark for domain-specific LLMs in addressing real-world food-related challenges.
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