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The term zero-shot learning itself first appeared in the literature in a 2009 paper from Palatucci, Hinton, Pomerleau, and Mitchell at NIPS’09. [5] This terminology was repeated later in another computer vision paper [6] and the term zero-shot learning caught on, as a take-off on one-shot learning that was introduced in computer vision years ...
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", [21] has also proven effective, which makes CoT a zero-shot prompting technique. OpenAI claims that this prompt allows for better scaling as a user no longer needs to ...
Or one can include one or several example translations in the prompt before asking to translate the text in question. This is then called one-shot or few-shot learning, respectively. For example, the following prompts were used by Hendy et al. (2023) for zero-shot and one-shot translation: [35] ### Translate this sentence from [source language ...
GPT-3 is capable of performing zero-shot and few-shot learning (including one-shot). [1] In June 2022, Almira Osmanovic Thunström wrote that GPT-3 was the primary author on an article on itself, that they had submitted it for publication, [24] and that it had been pre-published while waiting for completion of its review. [25]
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.
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Download as PDF; Printable version; In other projects ... move to sidebar hide. Few-shot learning and one-shot learning may refer to: Few-shot learning, a form of ...
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