Zero-Shot Learning
Zero-shot learning asks a model to perform a task from an instruction alone, with no worked examples provided. It relies entirely on knowledge acquired during pre-training and instruction tuning, making it the simplest and cheapest baseline before adding examples or heavier prompting scaffolds.
Definition
Zero-Shot LearningIn a zero-shot setting the prompt describes the task in plain language and the model responds directly, without demonstrations. Instruction-tuned models are trained to follow such directives, so for common, well-understood tasks a clear instruction is often sufficient.
Because nothing anchors the expected output format, zero-shot quality depends heavily on how precisely the instruction is phrased and how familiar the task is. It is the natural first thing to try; if results are inconsistent, adding a few examples (few-shot) or a reasoning directive typically helps.
Frequently asked questions
What is Zero-Shot Learning?
Zero-shot learning asks a model to perform a task from an instruction alone, with no worked examples provided. It relies entirely on knowledge acquired during pre-training and instruction tuning, making it the simplest and cheapest baseline before adding examples or heavier prompting scaffolds.
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