Optimizing Ingredient Substitution Using Large Language Models to Enhance Phytochemical Content in Recipes
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ID: 281702
2024
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Abstract
In the emerging field of computational gastronomy, aligning culinary
practices with scientifically supported nutritional goals is increasingly
important. This study explores how large language models (LLMs) can be applied
to optimize ingredient substitutions in recipes, specifically to enhance the
phytochemical content of meals. Phytochemicals are bioactive compounds found in
plants, which, based on preclinical studies, may offer potential health
benefits. We fine-tuned models, including OpenAI's GPT-3.5, DaVinci, and Meta's
TinyLlama, using an ingredient substitution dataset. These models were used to
predict substitutions that enhance phytochemical content and create a
corresponding enriched recipe dataset. Our approach improved Hit@1 accuracy on
ingredient substitution tasks, from the baseline 34.53 plus-minus 0.10% to
38.03 plus-minus 0.28% on the original GISMo dataset, and from 40.24 plus-minus
0.36% to 54.46 plus-minus 0.29% on a refined version of the same dataset. These
substitutions led to the creation of 1,951 phytochemically enriched ingredient
pairings and 1,639 unique recipes. While this approach demonstrates potential
in optimizing ingredient substitutions, caution must be taken when drawing
conclusions about health benefits, as the claims are based on preclinical
evidence. Future work should include clinical validation and broader datasets
to further evaluate the nutritional impact of these substitutions. This
research represents a step forward in using AI to promote healthier eating
practices, providing potential pathways for integrating computational methods
with nutritional science.
| Reference Key |
veselkov2024optimizing
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|---|---|
| Authors | Luis Rita; Josh Southern; Ivan Laponogov; Kyle Higgins; Kirill Veselkov |
| Journal | arXiv |
| Year | 2024 |
| DOI |
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