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A developer is crafting a prompt to have a large language model extract the primary color from a product description. Below are two approaches for the examples (demonstrations) within the prompt:
Approach A: Text: "This t-shirt comes in a vibrant shade of royal blue." Color: The main color is blue. Text: "The car's exterior is painted a deep, glossy black." Color: The main color is black.
Approach B: Text: "This t-shirt comes in a vibrant shade of royal blue." -> blue Text: "The car's exterior is painted a deep, glossy black." -> black
Based on common principles for guiding language models, which approach is generally more effective and why?
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Ch.3 Prompting - Foundations of Large Language Models
Foundations of Large Language Models
Computing Sciences
Foundations of Large Language Models Course
Analysis in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
Science
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Example of a Few-Shot Prompt for Chinese-to-English Translation
A developer is crafting a prompt to have a large language model extract the primary color from a product description. Below are two approaches for the examples (demonstrations) within the prompt:
Approach A: Text: "This t-shirt comes in a vibrant shade of royal blue." Color: The main color is blue. Text: "The car's exterior is painted a deep, glossy black." Color: The main color is black.
Approach B: Text: "This t-shirt comes in a vibrant shade of royal blue." -> blue Text: "The car's exterior is painted a deep, glossy black." -> black
Based on common principles for guiding language models, which approach is generally more effective and why?
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