Zero-shot learning
Definition
Zero-shot learning is solving a task without any labeled examples for that task. LLMs do this via prompting; vision models can do it with text-conditioned classifiers (e.g. CLIP).
No fine-tuning or few-shot examples are used; the task is specified only by description or by mapping to a shared space (e.g. text). LLMs excel at zero-shot for many NLP tasks; CLIP and similar models enable zero-shot image classification from text. Quality depends on how well pretraining covered the task or similar ones.
How it works
The task is described in natural language (e.g. prompt: “Classify the sentiment as positive or negative”) or via a shared representation (e.g. attribute vectors, text embeddings). Input (e.g. a sentence or image) is fed to the model along with the task description. The model produces an output (e.g. label, summary) using only what it learned at pretraining—no gradient updates on the target task. For CLIP: image and text are embedded in a shared space; zero-shot classification is done by comparing the image embedding to class name embeddings. For LLMs: the prompt states the task and format; the model completes accordingly.
Use cases
Zero-shot learning fits when you want to run a task with no target-task training—only a task description (e.g. prompt or class names).
- LLM tasks via prompts (e.g. classification, summarization) without fine-tuning
- CLIP-style image classification from text descriptions
- New categories or languages without labeled examples