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For example, a prompt may include a few examples for a model to learn from, such as asking the model to complete "maison → house, chat → cat, chien →" (the expected response being dog), [23] an approach called few-shot learning. [24] In-context learning is an emergent ability [25] of large language models.
The goal of response prompting is to transfer stimulus control from the prompt to the desired discriminative stimulus. [1] Several response prompting procedures are commonly used in special education research: (a) system of least prompts, (b) most to least prompting, (c) progressive and constant time delay, and (d) simultaneous prompting.
One-shot learning is an object categorization problem, found mostly in computer vision. Whereas most machine learning -based object categorization algorithms require training on hundreds or thousands of examples, one-shot learning aims to classify objects from one, or only a few, examples.
Few-shot learning and one-shot learning may refer to: Few-shot learning, a form of prompt engineering in generative AI; One-shot learning (computer vision)
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A response to an SAT math question marked on an optical answer sheet. An optical answer sheet or bubble sheet is a special type of form used in multiple choice question examinations. OMR is used to detect answers. The Scantron Corporation creates many optical answer sheets, although certain uses require their own customized system. [citation ...
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