mlsanityData to 14 Oct 2026

Exploring New Frontiers in Agricultural NLP: Investigating the Potential of Large Language Models for Food Applications

arXiv 2306.11892 ↗First posted 20 Jun 2023cs.CLcited by 21

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Abstract (arXiv)

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.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 51 of its 90 references that are in the map. First posted on arXiv 20 Jun 2023.

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51 of 90 references.

References · 51

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