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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.
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)
Large language models (LLM) themselves can be used to compose prompts for large language models. [49] The automatic prompt engineer algorithm uses one LLM to beam search over prompts for another LLM: [50] [51] There are two LLMs. One is the target LLM, and another is the prompting LLM.
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation. LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text. This page lists notable large language models.
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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.
The LLM course of study provides six areas of specialization. [ 7 ] The Maurer School of Law offers various experiential education opportunities, including five clinics (community law, conservation, entrepreneurship, intellectual property, and mediation); externships both in the US and abroad, and noncredit pro bono advocacy projects.
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