GPT-3

Generative Pre-trained Transformer 3 (GPT-3) is an autoregressive language model that uses deep learning to produce human-like text. It is trained on the Wikipedia corpus. GPT-3 has been shown to be able to generate coherent paragraphs and parse natural language questions into meaningful answers that can reason about their answers when necessary.

Overview[edit]

GPT-3 is a deep neural network that is pre-trained on a large corpus of text. It has its own internal representation of the text it was trained on and can be fed new text to process. The model can generate new text based on the text it is trained on. When it does, it is able to generate coherent sentences and produce results that appear to be written in a human language.1 The model is trained on a corpus of Wikipedia articles and has been shown to be able to generate coherent paragraphs and parse natural language questions into meaningful answers that can reason about their answers when necessary. GPT-3 is based on GPT-2 which has been shown to be able to answer questions and work with infinitive phrases. GPT-3 expands the capabilities of GPT-2 and has been shown to be able to generate coherent paragraphs and parse natural language questions into meaningful answers that can reason about their answers when necessary.

Applications[edit]

GPT-3 has been shown to be able to generate coherent paragraphs and parse natural language questions into meaningful answers that can reason about their answers when necessary.1 This shows potential use in creating chatbots that can have coherent conversations with humans. GPT-3 can also be used to create English subtitles for foreign language media. The model is able to generate an English subtitle that is uniform with the other subtitles in the video.2

See also[edit]

GPT-2, Google's previous text generation model.

References[edit]

1. https://arxiv.org/abs/1801.01758

2. https://arxiv.org/abs/1802.02587