Understanding procedural texts, such as cooking recipes, is essential for enabling machines to follow instructions and reason about tasks, a key aspect of intelligent reasoning. In cooking, these instructions can be interpreted as a series of modifications to a food preparation. For a model to effectively reason about cooking recipes, it must accurately discern and understand the inputs and outputs of intermediate steps within the recipe. We present a new corpus of cooking recipes enriched with descriptions of intermediate steps that describe the input and output for each step. PizzaCommonsense serves as a benchmark for the reasoning capabilities of LLMs because it demands rigorous explicit input-output descriptions to demonstrate the acquisition of implicit commonsense knowledge, which is unlikely to be easily memorized. GPT-4 achieves only 26\% human-evaluated preference for generations, leaving room for future improvements.
@article{arxiv.2401.06930,
title = {PizzaCommonSense: Learning to Model Commonsense Reasoning about Intermediate Steps in Cooking Recipes},
author = {Aissatou Diallo and Antonis Bikakis and Luke Dickens and Anthony Hunter and Rob Miller},
journal= {arXiv preprint arXiv:2401.06930},
year = {2024}
}
Comments
Findings of EMNLP 2024. The data is available at: https://github.com/adiallo07/PizzaCommonsense