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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]
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.
GPTZero uses qualities it terms perplexity and burstiness to attempt determining if a passage was written by a AI. [14] According to the company, perplexity is how random the text in the sentence is, and whether the way the sentence is constructed is unusual or "surprising" for the application.
Credit - Getty Images/fStop. D espite their expertise, AI developers don't always know what their most advanced systems are capable of—at least, not at first. To find out, systems are subjected ...
The decoder is a standard Transformer decoder. It has the same width and Transformer blocks as the encoder. It uses learned positional embeddings and tied input-output token representations (using the same weight matrix for both the input and output embeddings). It uses a byte-pair encoding tokenizer, of the same kind as used in GPT-2. English ...
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.
GPT-3 trying to write an encyclopedic paragraph about water scarcity in Yemen With the rise of machine learning , discussions about Wikipedia and AI models are becoming more and more heated. As of December 2022, with the release of ChatGPT for free to the public, AI has shown its potential to either massively improve or disrupt Wikipedia.
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