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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. 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.
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.
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.
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.
Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images, or video.This integration allows for a more holistic understanding of complex data, improving model performance in tasks like visual question answering, cross-modal retrieval, [1] text-to-image generation, [2] aesthetic ranking, [3] and ...
CLIP has been used as a component in multimodal learning. For example, during the training of Google DeepMind's Flamingo (2022), [33] the authors trained a CLIP pair, with BERT as the text encoder and NormalizerFree ResNet F6 [34] as the image encoder. The image encoder of the CLIP pair was taken with parameters frozen and the text encoder was ...