Few-shot and zero-shot learning are techniques that leverage a model's pre-trained representations to perform new tasks with minimal or no task-specific examples. In few-shot learning, the model adapts to a new task using only a small set of labeled instances, often by conditioning on example–response pairs within the prompt or through lightweight parameter updates. In zero-shot learning, the model uses its generalized pre-trained knowledge to execute a task based solely on instructions, without any direct examples, relying on semantic understanding and transfer capabilities built during large-scale training.

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