Cook-Gen: Robust Generative Modeling of Cooking Actions from Recipes

TL;DR

Cook-Gen introduces a novel aggregation-based generative AI approach to reliably model cooking actions from recipes, overcoming irregular data patterns and outperforming existing large language models in food computation tasks.

Contribution

This paper presents Cook-Gen, a new aggregation-based generative AI method that effectively models cooking actions from recipes, addressing irregular data patterns and surpassing baseline models.

Findings

Cook-Gen outperforms large language models in generating cooking actions.

The method effectively handles irregular and infrequent data patterns.

Cook-Gen demonstrates improved reliability in modeling complex cooking instructions.

Abstract

As people become more aware of their food choices, food computation models have become increasingly popular in assisting people in maintaining healthy eating habits. For example, food recommendation systems analyze recipe instructions to assess nutritional contents and provide recipe recommendations. The recent and remarkable successes of generative AI methods, such as auto-regressive large language models, can lead to robust methods for a more comprehensive understanding of recipes for healthy food recommendations beyond surface-level nutrition content assessments. In this study, we explore the use of generative AI methods to extend current food computation models, primarily involving the analysis of nutrition and ingredients, to also incorporate cooking actions (e.g., add salt, fry the meat, boil the vegetables, etc.). Cooking actions are notoriously hard to model using statistical learning methods due to irregular data patterns - significantly varying natural language descriptions for the same action (e.g., marinate the meat vs. marinate the meat and leave overnight) and infrequently occurring patterns (e.g., add salt occurs far more frequently than marinating the meat). The prototypical approach to handling irregular data patterns is to increase the volume of data that the model ingests by orders of magnitude. Unfortunately, in the cooking domain, these problems are further compounded with larger data volumes presenting a unique challenge that is not easily handled by simply scaling up. In this work, we propose novel aggregation-based generative AI methods, Cook-Gen, that reliably generate cooking actions from recipes, despite difficulties with irregular data patterns, while also outperforming Large Language Models and other strong baselines.