Counterfactual Recipe Generation: Exploring Compositional Generalization in a Realistic Scenario

Understand

People can acquire knowledge in an unsupervised manner by reading, and compose the knowledge to make novel combinations.

• In this paper, we investigate whether pretrained language models can perform compositional generalization in a realistic setting: recipe generation.
• We design the counterfactual recipe generation task, which asks models to modify a base recipe according to the change of an ingredient.
• This task requires compositional generalization at two levels: the surface level of incorporating the new ingredient into the base recipe, and the deeper level of adjusting actions related to the changing ingredient.