@patrickvonplaten, I believe too!
I’ve done something similar using T5 and ByT5 by PyTorch, the results were considerable, but in reality, the recipes were very similar. For instance, the models could understand and generate recipes based on either the ingredients used for warm foods or cold foods. However, the generated recipe for every ingredient (as the basis of warm or cold foods) was approximately the same procedure with different combinations and arrangements in words and steps.
I haven’t tested the GPT (2-3) yet, but I believe that the results might be better than T5 derivatives.
Note: I’ve used two datasets, Recipe NLG provided by Poland University and a custom crawled from Yummly.
I hope this one is considered a potential project. I can share the cleaned dataset crawled from Yummly (~8K food recipes).