Optimizing Ingredient Substitution Using Large Language Models to Enhance Phytochemical Content in Recipes

TL;DR

This paper demonstrates how large language models can be fine-tuned to optimize ingredient substitutions in recipes, enhancing phytochemical content and contributing to computational gastronomy and healthier eating practices.

Contribution

It introduces a method for fine-tuning LLMs to predict ingredient substitutions that increase phytochemical content, with improved accuracy and a large set of enriched recipes.

Findings

Improved Hit@1 accuracy from 34.53% to 38.03% on the original dataset.

Generated 1,951 phytochemically enriched ingredient pairs.

Created 1,639 unique recipes with enhanced phytochemical content.

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.