Definition
Zero-shot means the model gets a task description but no worked examples. Modern LLMs often succeed via pretraining knowledge and instruction tuning.
In classical ML, zero-shot can mean classifying unseen classes using semantic descriptions.
In simple terms
Being asked to assemble furniture with only the written instructions — no demo video — and still succeeding.
Where you see it
- "Summarize this article in Somali" with no sample summaries.
- Zero-shot image classification with CLIP-style models.
How it works
1.Describe the task
Clear instructions and constraints.
2.Provide the input
Text, image, or other modality.
3.Model generalizes
Uses pretrained skills to comply.
Why it matters
- Zero-shot capability is why general-purpose models can jump into new workflows instantly.
Often confused
Zero-shot means the model never saw related data in training.
It means no examples in this request — pretraining may still include similar tasks.