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.

翻译：凭借其显著增强的文本生成与提示能力，大型语言模型可将现有书面信息转化为更易使用和理解的形式。本研究以食谱为例（其指令复杂多样且应用广泛），开发了一种基于原始食谱与配料清单的提示框架，能将食谱分解为更简明的步骤。我们将该提示应用于多种世界菜系的食谱，并实验了多个大型语言模型（LLMs），发现GPT-3.5效果最佳。我们还设计了一项亚马逊土耳其机器人（Amazon Mechanical Turk）任务，通过精心优化以减少疲劳度，同时收集人类对食谱修订质量的判断。结果显示，标注者通常更偏好修订版而非原始版本，这表明大型语言模型在充当数字化副厨（及更广泛应用场景）中具有良好前景。我们公开了提示框架、代码及MTurk任务模板供公众使用。