Towards Fine-Dining Recipe Generation with Generative Pre-trained Transformers
This addresses recipe generation for culinary applications, but it appears incremental as it applies existing methods to a new domain.
The paper tackled generating fine-dining recipes from scratch using auto-regressive language models, achieving results by training on a small dataset to identify cooking techniques and propose novel recipes with minimal fine-tuning.
Food is essential to human survival. So much so that we have developed different recipes to suit our taste needs. In this work, we propose a novel way of creating new, fine-dining recipes from scratch using Transformers, specifically auto-regressive language models. Given a small dataset of food recipes, we try to train models to identify cooking techniques, propose novel recipes, and test the power of fine-tuning with minimal data.