Skip to content

Glossary

What is zero-shot learning?

Zero-shot learning is handling a task with no task-specific labelled examples at evaluation time. Whoever reports it should say what the model had not seen.

Updated 21 Aug 2026

01

Where this one gets misread

A zero-shot claim only means something with a statement of what the model had not seen. A great deal of what is described as zero-shot is closer to well-represented in training, and nobody can check without that statement.

02

Questions to ask

Ask for a worked example on your own material, and the evidence needed to reproduce it.

  • What is the evidence the model had not seen this task?
  • How does it compare with giving it a few examples?
  • What is the variance across repeated runs?
  • On whose data was this measured?
03

How Marella uses the term

We use “Zero-shot learning” only where a product mechanism or an evaluation method backs it up, and we say when the behaviour depends on how a deployment is configured.

  • Backed by a product mechanism or an evaluation method
  • Deployment differences flagged

What this page does not prove

  1. B1A definition is not a claim about how the product performs.
  2. B2Vendor implementations vary.
  3. B3Test the term against a representative workflow.

Test the claim on your documents

Pick a real piece of work, agree what a good answer looks like, then go through the results together.