Term tag: in-context learning

Few-shot Learning (FSL)

Teaching a model a task by showing it a handful of worked examples. In its modern form, few-shot prompting, no weights are updated at all.

Meta-Learning

Learning to learn: training a model across thousands of tiny tasks so it adapts from a handful of examples — and how that differs from fine-tuning.

One-shot Learning

Learning a new class from exactly one example — and the split that matters: one-shot prompting updates no weights; classical one-shot training does.

Prompt Engineering

Writing the input to a fixed, already-trained language model so it reliably produces the output you want — steering the model, not retraining it.

Zero-shot Learning

Performing a task with zero examples, on prior knowledge alone — and the split that matters: zero-shot prompting versus classical zero-shot learning.