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As originally proposed by Google, [11] each CoT prompt included a few Q&A examples. This made it a few-shot prompting technique. However, according to researchers at Google and the University of Tokyo, simply appending the words "Let's think step-by-step", [22] has also proven effective, which makes CoT a zero-shot prompting technique.
Usually, a specific criterion is set for each prompt change (e.g., after three days of correct performance of the behavior with the use of a partial physical prompt, a verbal prompt will be used). If the individual fails to perform the behavior correctly with the less intrusive prompt, the instructor would return to a more intrusive prompt for ...
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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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.
The name is a play on words based on the earlier concept of one-shot learning, in which classification can be learned from only one, or a few, examples. Zero-shot methods generally work by associating observed and non-observed classes through some form of auxiliary information, which encodes observable distinguishing properties of objects. [ 1 ]
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Prompt injection is a family of related computer security exploits carried out by getting a machine learning model which was trained to follow human-given instructions (such as an LLM) to follow instructions provided by a malicious user. This stands in contrast to the intended operation of instruction-following systems, wherein the ML model is ...