Large Language Models as Sous Chefs: Revising Recipes with GPT-3 Article Swipe
Alyssa Hwang
,
Bryan Li
,
Zhaoyi Hou
,
Dan Roth
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2306.13986
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2306.13986
With their remarkably improved text generation and prompting capabilities, large language models can adapt existing written information into forms that are easier to use and understand. In our work, we focus on recipes as an example of complex, diverse, and widely used instructions. We develop a prompt grounded in the original recipe and ingredients list that breaks recipes down into simpler steps. We apply this prompt to recipes from various world cuisines, and experiment with several large language models (LLMs), finding best results with GPT-3.5. We also contribute an Amazon Mechanical Turk task that is carefully designed to reduce fatigue while collecting human judgment of the quality of recipe revisions. We find that annotators usually prefer the revision over the original, demonstrating a promising application of LLMs in serving as digital sous chefs for recipes and beyond. We release our prompt, code, and MTurk template for public use.
Related Topics
Concepts
Recipe
Task (project management)
Computer science
Focus (optics)
Code (set theory)
Quality (philosophy)
Language model
Work (physics)
Natural language processing
Artificial intelligence
Data science
Programming language
Engineering
History
Archaeology
Optics
Philosophy
Systems engineering
Mechanical engineering
Set (abstract data type)
Epistemology
Physics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2306.13986
- https://arxiv.org/pdf/2306.13986
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4382322236
All OpenAlex metadata
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https://openalex.org/W4382322236Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2306.13986Digital Object Identifier
- Title
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Large Language Models as Sous Chefs: Revising Recipes with GPT-3Work title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-06-24Full publication date if available
- Authors
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Alyssa Hwang, Bryan Li, Zhaoyi Hou, Dan RothList of authors in order
- Landing page
-
https://arxiv.org/abs/2306.13986Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2306.13986Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2306.13986Direct OA link when available
- Concepts
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Recipe, Task (project management), Computer science, Focus (optics), Code (set theory), Quality (philosophy), Language model, Work (physics), Natural language processing, Artificial intelligence, Data science, Programming language, Engineering, History, Archaeology, Optics, Philosophy, Systems engineering, Mechanical engineering, Set (abstract data type), Epistemology, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(LLMs), | 79 |
| abstract_inverted_index.beyond. | 136 |
| abstract_inverted_index.develop | 44 |
| abstract_inverted_index.digital | 130 |
| abstract_inverted_index.example | 35 |
| abstract_inverted_index.fatigue | 99 |
| abstract_inverted_index.finding | 80 |
| abstract_inverted_index.prompt, | 140 |
| abstract_inverted_index.quality | 106 |
| abstract_inverted_index.recipes | 32, 57, 67, 134 |
| abstract_inverted_index.release | 138 |
| abstract_inverted_index.results | 82 |
| abstract_inverted_index.serving | 128 |
| abstract_inverted_index.several | 75 |
| abstract_inverted_index.simpler | 60 |
| abstract_inverted_index.usually | 114 |
| abstract_inverted_index.various | 69 |
| abstract_inverted_index.written | 15 |
| abstract_inverted_index.GPT-3.5. | 84 |
| abstract_inverted_index.complex, | 37 |
| abstract_inverted_index.designed | 96 |
| abstract_inverted_index.diverse, | 38 |
| abstract_inverted_index.existing | 14 |
| abstract_inverted_index.grounded | 47 |
| abstract_inverted_index.improved | 3 |
| abstract_inverted_index.judgment | 103 |
| abstract_inverted_index.language | 10, 77 |
| abstract_inverted_index.original | 50 |
| abstract_inverted_index.revision | 117 |
| abstract_inverted_index.template | 144 |
| abstract_inverted_index.carefully | 95 |
| abstract_inverted_index.cuisines, | 71 |
| abstract_inverted_index.original, | 120 |
| abstract_inverted_index.promising | 123 |
| abstract_inverted_index.prompting | 7 |
| abstract_inverted_index.Mechanical | 90 |
| abstract_inverted_index.annotators | 113 |
| abstract_inverted_index.collecting | 101 |
| abstract_inverted_index.contribute | 87 |
| abstract_inverted_index.experiment | 73 |
| abstract_inverted_index.generation | 5 |
| abstract_inverted_index.remarkably | 2 |
| abstract_inverted_index.revisions. | 109 |
| abstract_inverted_index.application | 124 |
| abstract_inverted_index.information | 16 |
| abstract_inverted_index.ingredients | 53 |
| abstract_inverted_index.understand. | 25 |
| abstract_inverted_index.capabilities, | 8 |
| abstract_inverted_index.demonstrating | 121 |
| abstract_inverted_index.instructions. | 42 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |