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Optimizing a Sentiment Analysis Prompt
An analyst is using a large language model to classify customer feedback. They provide the model with the examples shown in the case study, but the model's performance is inconsistent. Based on principles of effective demonstration, analyze the structure of these examples. Identify the primary weakness in their design and propose a revised, simplified structure that would likely improve the model's performance.
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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
Related
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?
Critique and Refine a Few-Shot Prompt
Optimizing a Sentiment Analysis Prompt