Exploring New Frontiers in Agricultural NLP: Investigating the Potential of Large Language Models for Food Applications
This work addresses the problem of improving agricultural NLP applications, such as food-related semantic matching, but it is incremental as it builds on existing transformer models and proposes new avenues rather than presenting concrete results.
The paper investigates using large language models for agricultural NLP, focusing on semantic matching between food descriptions and nutrition data, and proposes exploring GPT-based models and ChatGPT as knowledge sources to potentially enhance performance.
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.