FoodSky: A Food-oriented Large Language Model that Passes the Chef and Dietetic Examination
<|reference_start|>FoodSky: A Food-oriented Large Language Model that Passes the Chef and Dietetic Examination: 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 http://222.92.101.211:8200.<|reference_end|>
@article{zhou2024foodsky:,
    title={FoodSky: A Food-oriented Large Language Model that Passes the Chef and
  Dietetic Examination},
    author={Pengfei Zhou, Weiqing Min, Chaoran Fu, Ying Jin, Mingyu Huang,
  Xiangyang Li, Shuhuan Mei and Shuqiang Jiang},
    journal={arXiv preprint arXiv:2406.10261},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10261},
    primaryClass={cs.CL cs.AI}
}