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Analyzing a Flawed One-Shot Prompt for Information Extraction
A developer is trying to build a prompt that instructs a language model to extract a product_name and sentiment from user feedback. They have constructed the following prompt, which includes a single demonstration before the final user request:
PROMPT START
Extract the product name and the user's sentiment from the following text.
Demonstration: Input: "The new Lumina-5 camera is absolutely fantastic! I love the picture quality." Output: {"product_name": "Lumina-5 camera"}
User Request: Input: "I'm very disappointed with the performance of the Aero-Drone. It stopped working after one flight." Output:
PROMPT END
Analyze the provided prompt. Explain why the single demonstration is poorly constructed for the stated task and how this flaw is likely to cause the model to produce an incomplete or incorrect output for the 'User Request' section.
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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 of a One-Shot Prompt for Grammar Correction
A developer is creating a prompt to instruct a language model to summarize a block of text into a single, concise sentence. Which of the following prompt structures best demonstrates the one-shot learning technique for this task?
Improving Prompt Reliability for Date Formatting
Analyzing a Flawed One-Shot Prompt for Information Extraction