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Generative Pre-trained Transformer 2 (GPT-2) is a large language model by OpenAI and the second in their foundational series of GPT models. GPT-2 was pre-trained on a dataset of 8 million web pages. [2] It was partially released in February 2019, followed by full release of the 1.5-billion-parameter model on November 5, 2019. [3] [4] [5]
GPT-4 is a multi-modal LLM that is capable of processing text and image input (though its output is limited to text). [49] Regarding multimodal output , some generative transformer-based models are used for text-to-image technologies such as diffusion [ 50 ] and parallel decoding. [ 51 ]
These deep generative models were the first to output not only class labels for images but also entire images. In 2017, the Transformer network enabled advancements in generative models compared to older Long-Short Term Memory models, [ 38 ] leading to the first generative pre-trained transformer (GPT), known as GPT-1 , in 2018. [ 39 ]
GPT-2, a text generating model developed by OpenAI This page was last edited on 4 June 2020, at 12:21 (UTC). Text is available under the Creative Commons Attribution ...
An instance of GPT-2 writing a paragraph based on a prompt from its own Wikipedia article in February 2021. Generative Pre-trained Transformer 2 ("GPT-2") is an unsupervised transformer language model and the successor to OpenAI's original GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only limited demonstrative versions ...
For example, GPT-3, and its precursor GPT-2, [11] are auto-regressive neural language models that contain billions of parameters, BigGAN [12] and VQ-VAE [13] which are used for image generation that can have hundreds of millions of parameters, and Jukebox is a very large generative model for musical audio that contains billions of parameters. [14]
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The model may output text that appears confident, though the underlying token predictions have low likelihood scores. Large language models like GPT-4 can have accurately calibrated likelihood scores in their token predictions, [43] and so the model output uncertainty can be directly estimated by reading out the token prediction likelihood scores.
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