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

arXiv: https://arxiv.org/abs/2306.01805 · HTML: https://mlsanity.com/paper/2306.01805/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 1 Jun 2023.

## Builds on (most cited first, 5 of 5)

- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Knowledge-intensive Language Understanding for Explainable AI](https://mlsanity.com/paper/2108.01174/) (2021)
- [Neurosymbolic AI -- Why, What, and How](https://mlsanity.com/paper/2305.00813/) (2023)
- [Process Knowledge-Infused AI: Towards User-level Explainability, Interpretability, and Safety](https://mlsanity.com/paper/2206.13349/) (2022)
- [Learning Program Representations for Food Images and Cooking Recipes](https://mlsanity.com/paper/2203.16071/) (2022)

## Built on by (most cited first, 3 of 3)

- [Knowledge-Infused Self Attention Transformers](https://mlsanity.com/paper/2306.13501/) (2023)
- [IERL: Interpretable Ensemble Representation Learning -- Combining CrowdSourced Knowledge and Distributed Semantic Representations](https://mlsanity.com/paper/2306.13865/) (2023)
- [RDR: the Recap, Deliberate, and Respond Method for Enhanced Language Understanding](https://mlsanity.com/paper/2312.09932/) (2023)

## Abstract (arXiv)

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.