Improving a News Headline Classifier
A researcher is using a language model with the prompt: Classify the following headline as 'Factual Reporting' or 'Opinion Piece': [headline]. The model consistently misclassifies headlines such as 'New Study Suggests Potential Link Between Coffee and Longevity' as an 'Opinion Piece'. Explain the most likely reason for this error and describe how you would modify the prompt by explicitly defining the classification categories to improve its accuracy.
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Ch.3 Prompting - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Analysis in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
Science
Related
Example Definition of the 'Positive' Category for Polarity Classification
Definition of the 'Negative' Category in Polarity Classification
A developer is using a language model to sort user feedback into three categories: 'Bug Report', 'Feature Request', and 'Praise'. The initial prompt is:
Classify the following feedback: [feedback text]. The developer observes that the model often misclassifies suggestions for improvement (e.g., 'It would be great if this button did X') as bug reports. Which of the following prompt modifications is most likely to resolve this specific ambiguity between the 'Bug Report' and 'Feature Request' categories?Definition of the 'Positive' Category in Polarity Classification
Refining a Polarity Classification Prompt
Improving a News Headline Classifier