12 Facts About GPT-3

GPT-3, which was introduced in May 2020, and was in beta testing as of July 2020, is part of a trend in natural language processing systems of pre-trained language representations.

Quality of the text generated by GPT-3 is so high that it can be difficult to determine whether or not it was written by a human, which has both benefits and risks.

GPT-3's capacity is ten times larger than that of Microsoft's Turing NLG, the next largest NLP model.

Sixty percent of the weighted pre-training dataset for GPT-3 comes from a filtered version of Common Crawl consisting of 410 billion byte-pair-encoded tokens.

GPT-3 was trained on hundreds of billions of words and is capable of coding in CSS, JSX, and Python, among others.

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The training data contains occasional toxic language and GPT-3 occasionally generates toxic language as a result of mimicking its training data.

GPT-3 produced less toxic language compared to its predecessor model, GPT-1, although it produced both more generations and a higher toxicity of toxic language compared to CTRL Wiki, a language model trained entirely on Wikipedia data.

GPT-3 is capable of performing zero-shot, few-shot and one-shot learning.

In June 2022, Almira Osmanovic Thunstrom wrote that GPT-3 was the primary author on an article on itself, that they had submitted it for publication, and that it had been pre-published while waiting for completion of its review.

The agreement permits OpenAI to offer a public-facing API such that users can send text to GPT-3 to receive the model's output, but only Microsoft will have access to GPT-3's source code.

Large language models, such as GPT-3, have come under criticism from Google's AI ethics researchers for the environmental impact of training and storing the models, detailed in a paper co-authored by Timnit Gebru and Emily M Bender in 2021.

GPT-3 was criticized for its algorithmic bias; for example, it is more likely to associate Islam with terrorism and Black people with crime.