  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.
