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Few-shot learning [ edit ] A prompt may include a few examples for a model to learn from, such as asking the model to complete " maison → house, chat → cat, chien →" (the expected response being dog ), [ 33 ] an approach called few-shot learning .
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)
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.
Autoassociative self-supervised learning is a specific category of self-supervised learning where a neural network is trained to reproduce or reconstruct its own input data. [8] In other words, the model is tasked with learning a representation of the data that captures its essential features or structure, allowing it to regenerate the original ...
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.
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
The design has its origins from pre-training contextual representations, including semi-supervised sequence learning, [23] generative pre-training, ELMo, [24] and ULMFit. [25] Unlike previous models, BERT is a deeply bidirectional, unsupervised language representation, pre-trained using only a plain text corpus .
David W. Johnson (born 1940 in Muncie, Indiana) is a social psychologist whose research has focused on four overlapping areas: [1] cooperative, competitive, and individualistic efforts; constructive controversy; conflict resolution and peer mediation and experiential learning to teach interpersonal and small group skills. [2]