Cookie Consent
Hi, this website uses essential cookies to ensure its proper operation and tracking cookies to understand how you interact with it. The latter will be set only after consent.
Read our Privacy Policy
VOCABULARY

Few-Shot Learning

Few-shot learning is a concept in machine learning where the designed model is capable of making predictions based on learning from a very small amount of data - as few as 1-5 data points. This approach aims to create systems that can understand new concepts, phenomena or objects with minimal examples presented, imitating human cognitive ability to learn quickly from a few examples.

How Few-Shot Learning works

The process of few-shot learning involves two key stages: training and testing. During the training phase, a model is exposed to a large variety of tasks with plenty of examples for each. The model, which is typically a neural network, learns to extract general knowledge from these tasks and their related examples which can aid in understanding new tasks.

In the second stage, the model is given a new task with only a few examples (hence 'few-shot'). The model must then use the general principles it has learned from the training phase to understand and perform this new task effectively, despite having only a few examples to learn from.

Lakera LLM Security Playbook
Learn how to protect against the most common LLM vulnerabilities

Download this guide to delve into the most common LLM security risks and ways to mitigate them.

Related terms
Activate
untouchable mode.
Get started for free.

Lakera Guard protects your LLM applications from cybersecurity risks with a single line of code. Get started in minutes. Become stronger every day.

Join our Slack Community.

Several people are typing about AI/ML security. 
Come join us and 1000+ others in a chat that’s thoroughly SFW.