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It is a general-purpose learner and its ability to perform the various tasks was a consequence of its general ability to accurately predict the next item in a sequence, [2] [7] which enabled it to translate texts, answer questions about a topic from a text, summarize passages from a larger text, [7] and generate text output on a level sometimes ...
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.
GPT-2, a text generating model developed by OpenAI Topics referred to by the same term This disambiguation page lists articles associated with the same title formed as a letter–number combination.
Generative Pre-trained Transformer 3 (GPT-3) is a large language model released by OpenAI in 2020.. Like its predecessor, GPT-2, it is a decoder-only [2] transformer model of deep neural network, which supersedes recurrence and convolution-based architectures with a technique known as "attention". [3]
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 AI systems trained on sets of images with text captions include Imagen, DALL-E, Midjourney, Adobe Firefly, FLUX.1, Stable Diffusion and others (see Artificial intelligence art, Generative art, and Synthetic media). They are commonly used for text-to-image generation and neural style transfer. [54]
Interest in natural-language generation increased in 2019 after OpenAI unveiled GPT2, an AI system that generates text matching its input in subject and tone. [81] GPT2 is a transformer, a deep machine learning model introduced in 2017 used primarily in the field of natural language processing (NLP).
BookCorpus (also sometimes referred to as the Toronto Book Corpus) is a dataset consisting of the text of around 7,000 self-published books scraped from the indie ebook distribution website Smashwords. [1]