Completion of ambiguous expressions in cooking recipes using large language models

Abstract

User-generated recipes posted on recipe websites often contain expressions that can be interpreted in various ways by readers or that hinder the accurate reproduction of the dish. Such ambiguous expressions contribute to the difficulty of understanding recipes. The aim of this study is to clarify ambiguous expressions specific to cooking recipes and to provide clear alternatives that can be easily understood by readers. Specifically, we identified and categorized ambiguous expressions using a questionnaire, designed prompts for large language models based on the results, and proposed a method for complementing recipes using retrieval-augmented generation. Analysis of the questionnaire results reveals that ambiguous expressions include omissions, modifiers, among others. Furthermore, cooking experiments demonstrated that the readability of recipes complemented by the proposed method is improved, although the overall validity of the complemented recipes remains uncertain.