The paper explores the use of prompt engineering techniques to generate cooking task trees using the Gemini language model. Three prompting approaches are investigated:
The performance of these approaches is evaluated based on accuracy and completeness metrics. Example-based Prompting is found to be the most effective, likely due to the structured and high-quality inputs that the model can easily interpret and replicate.
The paper also discusses the experimental setup, where a RecipeGenerator class is used to read input data from a JSON file, generate recipes using the Gemini model, and save the results in a structured format. The results show that the model can effectively integrate multiple data points (ingredients, tools) into coherent and creative culinary instructions, with the use of template-based prompts ensuring consistency in the style and structure of the generated recipes.
Overall, the paper provides valuable insights into the potential of prompt engineering and the Gemini language model for robotic cooking task planning, highlighting the importance of prompt design in achieving high-quality AI outputs.
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by Pallavi Tand... at arxiv.org 05-07-2024
https://arxiv.org/pdf/2405.03671.pdfDeeper Inquiries