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As originally proposed by Google, [16] each CoT prompt included a few Q&A examples. This made it a few-shot prompting technique. However, according to a researchers at Google and the University of Tokyo, simply appending the words "Let's think step-by-step", [ 25 ] has also proven effective, which makes CoT a zero-shot prompting technique.
A generative LLM can be prompted in a zero-shot fashion by just asking it to translate a text into another language without giving any further examples in the prompt. 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.
The sections are then concatenated into a single document, which is passed once more to the LLM with a prompt asking it to remove duplications between the sections. Finally, the LLM is called one last time to generate a summary for the lead section. All this internal chattiness and repeated prompting of the LLM for multiple tasks comes at a price.
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)
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.
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