PizzaCommonSense: Learning to Model Commonsense Reasoning about Intermediate Steps in Cooking Recipes
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916430230847488 |
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| author | Diallo, Aissatou Bikakis, Antonis Dickens, Luke Hunter, Anthony Miller, Rob |
| author_facet | Diallo, Aissatou Bikakis, Antonis Dickens, Luke Hunter, Anthony Miller, Rob |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_06930 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PizzaCommonSense: Learning to Model Commonsense Reasoning about Intermediate Steps in Cooking Recipes Diallo, Aissatou Bikakis, Antonis Dickens, Luke Hunter, Anthony Miller, Rob Computation and Language 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. |
| title | PizzaCommonSense: Learning to Model Commonsense Reasoning about Intermediate Steps in Cooking Recipes |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2401.06930 |