Prototype of recipe simplification and RDF generation method using GPT-3.5 for inheritance of local food

Abstract

Local food contains important information on regional characteristics and culture, but opportunities to pass on Local food are decreasing due to changes in modern dietary habits. Therefore, this study aims to construct Linked Open Data that contributes to the inheritance of Local food. However, it was not clear what kind of data would contribute to the inheritance of Local food. Therefore, we conducted a survey on how people would like to cook their Local food. The most common reason for wanting to cook Local food was "to pass it on," while the most common reasons for not wanting to cook Local food were "labor-intensive, troublesome, time-consuming," and "it is difficult to prepare the ingredients. Based on the survey results, we hypothesized that simplifying existing Local food recipes and making them easier to prepare using a large-scale language model would contribute to the transmission of Local food. Based on this hypothesis, a recipe simplification system using a large-scale language model of Local food was developed. However, the recipes actually generated were not highly accurate in terms of simplification and regional characteristics. Therefore, by introducing "ingredient substitution knowledge data," we improved the accuracy of the generated recipes, especially the accuracy of ingredient substitution, which was inaccurate.