AI Glossary
Zero-Shot Learning
Understanding AI Terminology
An AI's ability to perform tasks without any task-specific examples.
What It Means
Zero-shot learning refers to an AI model's ability to perform a task without being shown any examples of that specific task. The model relies entirely on its pre-training and the task description in the prompt. This contrasts with few-shot learning (providing examples) or fine-tuning (training on task-specific data). Modern LLMs have strong zero-shot capabilities across many tasks.
Examples
- Asking a model to translate without showing translation examples
- Requesting sentiment analysis with just a description of the task
- Classifying documents into new categories defined in the prompt
How This Applies to ARKA-AI
ARKA-AI's models support zero-shot learning, allowing you to describe tasks in plain language without needing to provide examples.
Frequently Asked Questions
Common questions about Zero-Shot Learning
Use few-shot (providing examples) when zero-shot results aren't satisfactory, for specialized formats, or when the task is unusual. Examples help the model understand exactly what you want.
Not necessarily. For common tasks, modern LLMs often perform well zero-shot. Few-shot helps more with unusual or nuanced tasks, or when specific formatting is required.
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