In a nutshell, this means that we train the model (i) by taking text samples from the data set and (ii) training the model to predict the next word; see the illustration above. The GPT-3 is a language prediction model. This means that it has a machine learning model of neural networks that can take the input text and transform it into what it predicts will be the most useful result. This is achieved by training the system in the enormous amount of text on the Internet to detect patterns in a process called generative pre-training.
The GPT-3 was trained with several data sets, each with different weights, including Common Crawl, WebText2 and Wikipedia. GPT is an architecture based on Transformers and a training procedure for natural language processing tasks. First, a language modeling objective is used on unlabeled data to learn the initial parameters of a neural network model. These parameters are then adapted to a target task using the corresponding supervised objective.
GPT models are a collection of language models based on deep learning created by the OpenAI team. Without supervision, these models can perform various NLP tasks, such as answering questions, including texts, summarizing texts, etc. These language models require very few or no examples to understand the tasks. They perform equivalent to or even better than state-of-the-art models trained in a supervised manner.
The integration of GPT models into virtual assistants and chatbots increases their capabilities, which has led to an increase in demand for GPT models.