Cook-Gen: Robust Generative Modeling of Cooking Actions from Recipes
Revathy Venkataramanan Kaushik Roy Kanak Raj Renjith Prasad Yuxin Zi Vignesh Narayanan Amit Sheth
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.