What is Few-shot Learning?
It is the ability of artificial intelligence to learn a new task with very few examples.
Overview
Few-sample learning is when artificial intelligence learns a task it has never seen before by seeing only a few examples. This method allows the model to adapt quickly without needing to be trained from scratch each time.
How it works
The model recognizes patterns based on a few examples given to it. These examples allow the model to focus the general knowledge it already has on the new task.
Where it is used
It is especially used in language models, where the user wants the model to respond in a specific format.
Commonly confused with
Mixed with fine-tuning; While fine-tuning changes the structure of the model, the few-shot method simply adds examples to the current command.
Frequently asked questions
How many examples are enough?
Generally, 1 to 5 examples are sufficient for the model to understand the task.
Is it always successful?
If the task is very complex or the model has low capacity, the examples may not be sufficient.
Related terms
This explanation was written in plain language for TreScout and machine-translated from the Turkish original · the Turkish version prevails. If something looks wrong or missing, write to hello@trescout.com. Read in Turkish →