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Large language models (LLM) themselves can be used to compose prompts for large language models. [56] [57] The automatic prompt engineer algorithm uses one LLM to beam search over prompts for another LLM: [58] [59] There are two LLMs. One is the target LLM, and another is the prompting LLM.
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)
The Journal of Social Work Education is a quarterly peer-reviewed academic journal dedicated to education in the fields of social work and social welfare. It was established in 1965 as the Journal of Education for Social Work, obtaining its current name in 1985. It is published by Taylor & Francis on behalf of the Council on Social Work Education.
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation. As language models , LLMs acquire these abilities by learning statistical relationships from vast amounts of text during a self-supervised and semi-supervised training process.
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The Journal of Social Work is a peer-reviewed academic journal that covers research in the field of social work. The editor-in-chief is Steven M. Shardlow ( Keele University ). It was established in 2001 and is published by SAGE Publishing .
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.
A language model is a probabilistic model of a natural language. [1] In 1980, the first significant statistical language model was proposed, and during the decade IBM performed ‘Shannon-style’ experiments, in which potential sources for language modeling improvement were identified by observing and analyzing the performance of human subjects in predicting or correcting text.