FoodSky: A food-oriented large language model that can pass the chef and dietetic examinations - Pengfei Zhou; Weiqing Min; Chaoran Fu; Ying Jin; Mingyu Huang; Xiangyang Li; Shuhuan Mei; Shuqiang Jiang (Patterns)
Summary: 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. The bigger picture: Food and nutrition are fundamental to human well-being, but understanding and managing dietary information can be complex and challenging. Our study on FoodSky shows that a domain-specific large language model (LLM) has greater potential to enable real-world food-related tasks. FoodSky is designed to comprehend and reason about food-oriented knowledge, allowing it to perform at a more professional level in both cooking and dietary tasks. By addressing the unique challenges of food computing, FoodSky offers more precise recipe recommendations, improved nutritional guidance, and enhanced culinary creativity. Furthermore, by training on a high-quality food instruction dataset and utilizing algorithms specifically tailored to the food domain, FoodSky demonstrates the capability to pass the chef and dietetic examinations, which are typically difficult for non-professionals. This bridges the gap between general AI capabilities and the specialized needs of the food sector while also inspiring domain-specific LLM methods across other disciplines. Overall, our work advances food computing research and offers practical benefits for public health, culinary education, and food industry innovation. By making complex food-related information more accessible and actionable, FoodSky contributes to a future where AI helps improve public dietary health, supports culinary education, and fosters a deeper understanding of food, ultimately leading to more sustainable outcomes.