Zero-shot labeling
Zero-shot labeling is auto-labeling where a model annotates classes it was never explicitly trained on, by being told the target categories at inference time, usually through a text prompt. It labels new concepts with no task-specific training examples.
In this article
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What is zero-shot labeling?
Zero-shot labeling lets you label categories the model has never seen labeled examples of. Instead of training a detector on thousands of "forklift" images, you give a foundation or vision-language model the class names, often as a text prompt, and it applies them based on the general associations it learned in pretraining.
"Zero-shot" means zero task-specific training examples. The model generalizes from language and broad knowledge to your label set. Few-shot labeling is the close cousin, where you supply a handful of examples to sharpen results.
Key takeaways
- The model labels classes it was never trained on, specified at inference, usually through text.
- Zero task-specific examples are needed, because the knowledge comes from pretraining.
- It is fast and flexible but less reliable than a model trained on your exact classes.
What zero-shot labeling provides
| Approach | Training examples for your classes | Trade-off |
|---|---|---|
| Zero-shot | None | Fastest and most flexible, least precise |
| Few-shot | A handful | A little setup, better fit |
| Trained or fine-tuned | Many | Most accurate, most labeling upfront |
How it works
You give a model your class names, run it over the data, and its predictions become candidate labels.
Why it matters
Zero-shot labeling is how you bootstrap a brand-new label set with no labeled data to start from, which is invaluable for exploration and cold starts. Results hinge on prompt wording far more than people expect. The same class called "person," "pedestrian," or "human" can yield noticeably different recall, because the model is matching your text against its learned associations, not a fixed definition. So zero-shot quality is partly a prompt-engineering problem, and ambiguous or jargon class names are where it silently underperforms.
Frequently asked questions
What does "zero-shot" mean?
The model labels classes with zero task-specific training examples, generalizing from pretraining.
What is the difference between zero-shot and few-shot labeling?
Zero-shot uses no examples of your classes. Few-shot uses a small number to improve accuracy.
Is zero-shot labeling accurate enough to ship?
It is great for a first pass and exploration, but specialized or ambiguous classes usually need human review or a trained model.
Related terms
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Jesse Mostipak is the Director of Growth at Voxel51, where the work is helping humans find and trust what the brand knows, and teaching the Google knowledge graph and the LLMs answering on their behalf to do the same. That question, how knowledge gets built inside a system, is one Jesse has been chasing for years. Earlier versions of it ran through a New York City high school science classroom, data science and machine learning, and developer relations at Kaggle, Posit (formerly RStudio), and Baseten. The answer doesn't change much depending on whether the learner is a teenager, a software engineer, or a knowledge graph. Jesse holds a Master's in Education from CUNY Hunter College. LinkedIn
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Last updated November 3, 2026
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