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Constructing a Few-Shot Prompt for a Novel Task
You need a large language model to perform a novel task: extracting the primary programming language mentioned in a block of text and formatting it as 'Language: [name]'. Construct a complete, self-contained prompt that uses the few-shot learning technique to accomplish this. Your prompt must include at least two distinct examples (demonstrations) of the task before presenting the final input that you want the model to process.
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Ch.1 Pre-training - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Ch.2 Generative Models - Foundations of Large Language Models
Ch.3 Prompting - Foundations of Large Language Models
Creation in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
Science
Related
Examples of Few-Shot Learning Applications in NLP
Enabling Few-Shot Learning with Multiple Demonstrations
Input-Output Patterns in Few-Shot Learning
Sufficiency of Demonstrations in Few-Shot Learning
Applying Few-Shot Learning to Complex Reasoning Tasks
A user provides the following text to a large language model to get it to classify movie reviews:
Review: The plot was predictable and the acting was wooden. I was bored the entire time. Sentiment: Negative
Review: An absolute masterpiece! The cinematography was stunning and the story was deeply moving. Sentiment: Positive
Review: It was a decent film. Not the best I've seen this year, but it had some good moments. Sentiment: Neutral
Review: I couldn't stop laughing from beginning to end. A brilliant comedy. Sentiment:
The model correctly responds with "Positive". Which statement best analyzes the primary reason for the model's successful performance on this task?
Constructing a Few-Shot Prompt for a Novel Task
Critiquing a Prompt for a Custom Extraction Task
Example of a Few-Shot Prompt for Polarity Classification