Exploring New Frontiers in Agricultural NLP: Investigating the Potential
  of Large Language Models for Food Applications
Saed Rezayi Zhengliang Liu Zihao Wu Chandra Dhakal Bao Ge Haixing Dai Gengchen Mai Ninghao Liu Chen Zhen Tianming Liu Sheng Li
This paper explores new frontiers in agricultural natural language processing
by investigating the effectiveness of using food-related text corpora for
pretraining transformer-based language models. In particular, we focus on the
task of semantic matching, which involves establishing mappings between food
descriptions and nutrition data. To accomplish this, we fine-tune a pre-trained
transformer-based language model, AgriBERT, on this task, utilizing an external
source of knowledge, such as the FoodOn ontology. To advance the field of
agricultural NLP, we propose two new avenues of exploration: (1) utilizing
GPT-based models as a baseline and (2) leveraging ChatGPT as an external source
of knowledge. ChatGPT has shown to be a strong baseline in many NLP tasks, and
we believe it has the potential to improve our model in the task of semantic
matching and enhance our model's understanding of food-related concepts and
relationships. Additionally, we experiment with other applications, such as
cuisine prediction based on food ingredients, and expand the scope of our
research to include other NLP tasks beyond semantic matching. Overall, this
paper provides promising avenues for future research in this field, with
potential implications for improving the performance of agricultural NLP
applications.