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Example of a Zero-Shot Prompt for Polarity Classification (Positive Sentiment)
A zero-shot prompt applies a task instruction to a new input without any preceding examples. To classify the sentiment of a positive text, the prompt could be structured like this:
'Assume that the polarity of a text is a label chosen from {positive, negative, neutral}. Identify the polarity of the input.
Input: The weather here is wonderful.'
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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
Related
Example of a Complete Prompt for Polarity Classification
Components of an Instruction-based Prompt
Zero-Shot Learning with LLMs
Example of a Zero-Shot Prompt for Polarity Classification (Negative Sentiment)
Examples of Instruction-based Prompts for Polarity Classification
Using Descriptive Prompts for Novel Classification Tasks
Challenge of Prompting LLMs for Many-Category Classification
Example of a Zero-Shot Prompt for Polarity Classification (Positive Sentiment)
Example of a Zero-Shot Prompt for Polarity Classification (Positive Sentiment on Food)
Adapting Prompt Detail to an LLM's Task Familiarity
A developer needs a large language model to classify incoming customer support tickets. The goal is to sort each ticket into one of three specific categories: 'Technical Issue', 'Billing Inquiry', or 'General Feedback'. Which of the following prompts is best structured to achieve this task reliably and consistently?
Diagnosing Ineffective Prompt Instructions
Crafting an Instruction for a Novel Task
Instructing LLMs with Detailed Descriptions
Learn After
A developer needs to determine if a customer review is 'positive', 'negative', or 'neutral' without providing any examples in the prompt itself. Which of the following prompts is best structured to accomplish this task reliably?
Critique of a Zero-Shot Prompt for Sentiment Classification
Constructing a Zero-Shot Prompt for Sentiment Analysis