@inproceedings{diallo-etal-2024-pizzacommonsense,
    title = "{P}izza{C}ommon{S}ense: A Dataset for Commonsense Reasoning about Intermediate Steps in Cooking Recipes",
    author = "Diallo, Aissatou  and
      Bikakis, Antonis  and
      Dickens, Luke  and
      Hunter, Anthony  and
      Miller, Rob",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.findings-emnlp.728/",
    doi = "10.18653/v1/2024.findings-emnlp.728",
    pages = "12482--12496",
    abstract = "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 beeasily memorized. GPT-4 achieves only 26{\%} human-evaluated preference for generations, leaving room for future improvements."
}