Few-Shot Learning
A machine learning paradigm in which a system learns new tasks from only a small number of examples. It serves as an important measure of AI generalization capability and represents a notable advantage of human intelligence over current AI systems.
Timeline (last 90 days)
Models with fewer than 7B parameters almost always must be used together with few-shot examples.
Human learning of new skills relies on prior knowledge transfer, causal reasoning, and highly compressed conceptual representations, a capability referred to in cognitive science as inductive bias.
Runtime policy definition is a technique combining prompt engineering and few-shot learning that allows temporarily describing in natural language, at each inference call, the content to be intercepted or permitted.
All Facts (3)
Runtime policy definition is a technique combining prompt engineering and few-shot learning that allows temporarily describing in natural language, at each inference call, the content to be intercepted or permitted.
60%UnverifiedModels with fewer than 7B parameters almost always must be used together with few-shot examples.
50%UnverifiedHuman learning of new skills relies on prior knowledge transfer, causal reasoning, and highly compressed conceptual representations, a capability referred to in cognitive science as inductive bias.
50%