zero-shot learning (ZSL)

Zero-shot learning (ZSL) is a machine learning setting where a model is required to handle classes or tasks it did not encounter during training by leveraging additional semantic information or generalized representations acquired during pretraining.

Classical ZSL methods link seen and unseen classes using this information so that, at test time, the model can infer correct labels for previously unseen classes.

In the context of large language models (LLMs), zero-shot usually refers to zero-shot prompting: a prompt that instructs a pretrained model to perform a new task with no worked examples or demonstrations, and without updating the model’s parameters.

Related Resources

Tutorial

Prompt Engineering: A Practical Example

Learn prompt engineering techniques with a practical, real-world project to get better results from large language models. This tutorial covers zero-shot and few-shot prompting, delimiters, numbered steps, role prompts, chain-of-thought prompting, and more. Improve your LLM-assisted projects today.

intermediate ai data-science

For additional information on related topics, take a look at the following resources:

• Practical Prompt Engineering (Quiz)