Food is foundational to human life, serving not only as a source of
nourishment but also as a cornerstone of cultural identity and social
interaction. As the complexity of global dietary needs and preferences grows,
food intelligence is needed to enable food perception and reasoning for various
tasks, ranging from recipe generation and dietary recommendation to
diet-disease correlation discovery and understanding. Towards this goal, for
powerful capabilities across various domains and tasks in Large Language Models
(LLMs), we introduce Food-oriented LLM FoodSky to comprehend food data through
perception and reasoning. Considering the complexity and typicality of Chinese
cuisine, we first construct one comprehensive Chinese food corpus FoodEarth
from various authoritative sources, which can be leveraged by FoodSky to
achieve deep understanding of food-related data. We then propose Topic-based
Selective State Space Model (TS3M) and the Hierarchical Topic Retrieval
Augmented Generation (HTRAG) mechanism to enhance FoodSky in capturing
fine-grained food semantics and generating context-aware food-relevant text,
respectively. Our extensive evaluations demonstrate that FoodSky significantly
outperforms general-purpose LLMs in both chef and dietetic examinations, with
an accuracy of 67.2% and 66.4% on the Chinese National Chef Exam and the
National Dietetic Exam, respectively. FoodSky not only promises to enhance
culinary creativity and promote healthier eating patterns, but also sets a new
standard for domain-specific LLMs that address complex real-world issues in the
food domain. An online demonstration of FoodSky is available at
this http URL

通过感知和推理实现食物数据的深度理解，FoodSky 在厨师和营养师考试中的准确度分别为 67.2％和 66.4％，将大幅超越通用类型的大型语言模型，并且可以提升烹饪创造力，推广更健康的饮食模式，并为解决食品领域的复杂现实问题设定了新的标准。