[1E4-OS-3a] OS-3

[1E4-OS-3a-02] Completion of ambiguous expressions in cooking recipes using large language models

〇Akane Ueda¹, Kazushi Okamoto¹, Kei Harada¹, Atsushi Shibata², Koki Karube¹ (1. The University of Electro-Communications, 2. Advanced Institute of Industrial Technology)

Keywords:Cooking Recipe, Large Language Models, Retrieval-Augmented Generation, Understanding of Procedure, Cooking Experiment

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.