Examples of Instruction-based Prompts for Polarity Classification
Instruction-based prompts for polarity classification give a direct command to the model, defining the task and the possible outputs. These instructions can vary in style. For example, a formal instruction can be used:
'Assume that the polarity of a text is a label chosen from {positive, negative, neutral}. Identify the polarity of the input.'
Another approach is a more direct command about the task:
'Analyze the polarity of the following text and classify it as positive, negative, or neutral.'
A third option is a concise instruction that focuses solely on constraining the output format:
'Just answer: positive, negative, or neutral.'
All three examples clearly establish the set of possible labels and instruct the model on the action to perform.
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Ch.1 Pre-training - Foundations of Large Language Models
Foundations of Large Language Models
Computing Sciences
Ch.3 Prompting - Foundations of Large Language Models
Foundations of Large Language Models Course
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
Examples of Instruction-based Prompts for Polarity Classification
Example of a Label Set in Polarity Classification
Definition of Neutral Sentiment in Polarity Classification
Example of a Complete Prompt for Polarity Classification
Example of a Simple Prompt for Polarity Classification
A mobile app development team wants to analyze user feedback from their app store page. They plan to build a system that automatically assigns one of the following labels to each user review: 'Pleased', 'Displeased', or 'Suggestion'. How does this business objective relate to the task of polarity classification?
A company is analyzing customer feedback. Match each piece of feedback to the sentiment category it best represents.
Example of a Negative Input for Polarity Classification (Service Experience)
Constraining LLM Output with a Direct Command
Evaluating a Sentiment Classification System
You’re building a single API endpoint that returns...
Your team is implementing a polarity text-classifi...
You’re launching a sentiment (polarity) classifica...
Create a Dual-Backend Polarity Classification Spec (BERT + Prompt-Completion) with Label Mapping
Designing a Robust Polarity Classifier: BERT vs Prompt-Completion and the Label-Mapping Contract
Choosing and Operationalizing a Sentiment Classifier Under Real Production Constraints
Debugging a Sentiment Pipeline: When Prompt-Completion and Label Mapping Disagree with a BERT Classifier
Designing a Consistent Polarity Classification Service Across BERT and Prompt-Completion Outputs
Stabilizing a Polarity Classifier When Migrating from BERT to Prompt-Completion
Unifying Sentiment Labels Across a BERT Classifier and a Prompt-Completion LLM
Example of a Few-Shot Prompt for Polarity Classification
Examples of Instruction-based Prompts for Polarity Classification
Example of a Prompt for Polarity Classification (Negative Sentiment)
Example of a Simple Prompt for Polarity Classification
An engineer needs to classify user feedback into one of three categories: 'Bug Report', 'Feature Request', or 'General Feedback'. To ensure the language model's output is strictly one of these labels, they decide to frame the problem as a sentence-completion task. Which of the following prompt structures best exemplifies this specific technique?
Improving LLM-based Ticket Classification
Rationale for Cloze-based Classification
Example of a Cloze-like Prompt for Polarity Classification
Learn After
Example of a Negative Input for Polarity Classification
Examples of Positive Inputs for Polarity Classification
Example of a Prompt with a Post-Input Instruction for Polarity Classification
A developer needs a language model to classify customer feedback into one of three categories: 'positive', 'negative', or 'neutral'. The primary goal is to ensure the model's output is only one of these three words, without any additional explanation or conversational text. Which of the following instruction-based prompts is most effective for this specific requirement?
Analyze the following instruction-based prompts designed for a text classification task. Match each prompt to the description that best characterizes its primary approach.
Improving a Faulty Classification Prompt