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  3. /Few-Shot Learning
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Few-Shot Learning

The ability of a model to learn a new task from just a handful of examples, often provided in the prompt itself.

Definition

The ability of a model to learn a new task from just a handful of examples, often provided in the prompt itself. Large language models excel at this — show them 2-3 examples of a task and they can generalize. Contrasts with traditional ML that needs thousands of labeled examples.

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Related Terms

In-Context Learning

A model's ability to learn new tasks simply from examples provided in the prompt, without any weight updates.

Zero-Shot Learning

A model's ability to perform a task it was never explicitly trained on, with no examples provided.

Prompting

The text input you give to an AI model to direct its behavior.

Activation Function

A mathematical function applied to a neuron's output that introduces non-linearity into the network.

Adam Optimizer

An optimization algorithm that combines the best parts of two other methods — AdaGrad and RMSProp.

AGI

Artificial General Intelligence.

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