Zero-Shot Prompting

Sources:

• https://github.com/dair-ai/Prompt-Engineering-Guide/blob/main/guides/prompts-advanced-usage.md#zero-shot-prompting

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Overview

About

Large Language Models (LLMs) today trained on large amounts of data and tuned to follow instructions, are capable of performing tasks zero-shot.

Example

Prompt:

Classify the text into neutral, negative, or positive.
 
Text: I think the vacation is okay.
Sentiment:

Output:

neutral

NOTE

Note that in the prompt above we didn’t provide the model with any examples — that’s the zero-shot capabilities at work. When zero-shot doesn’t work, it’s recommended to provide demonstrations or examples in the prompt. Below we discuss the approach known as few-shot prompting.

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Appendix

Note created on 2024-04-29 and last modified on 2024-04-29.

See Also

• MOC - Artificial Intelligence
• Prompt Engineering
• Few-Shot Prompting
• Chain-of-Thought (CoT)
• Zero-Shot CoT
• Self-Consistency
• Generate Knowledge Prompting
• AI Tools Checklist
• AI Starter Guide.pdf
• PromptTools Python Package
• Langchain, Langsmith, Langserve

Backlinks

LIST FROM [[Zero-Shot Prompting]] AND -"CHANGELOG" AND -"//Zero-Shot Prompting"

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