FoodSky: A food-oriented large language model that can pass the chef and dietetic examinations.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 40486965.
- Also identified by DOI 10.1016/j.patter.2025.101234 and PMC identifier 12142648.
- Licence recorded as CC BY-NC-ND.
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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.