What datasets are used to train GPT models?

One of the most common datasets used to train GPT models is the OpenAI GPT-2 dataset. This dataset consists of over 8 million web pages from a wide variety of sources such as news websites, blogs, e-commerce sites, and discussion forums. The data is pre-processed and tokenized, which makes it easier for the model to learn.

Another dataset that is commonly used to train GPT models is the OpenAI GPT-3 dataset. GPT-3 is a large-scale language model with over 175 billion parameters. This dataset is composed of text from over 45,000 sources, primarily consisting of books, forums, and news articles. GPT-3 is used to train the model to understand the structure of language, enabling it to generate more natural-sounding responses.

The third dataset frequently used to train GPT models is the WebText dataset. This dataset contains over 4 million webpages, with a focus on discussion forums, blogs, and online articles. The data is pre-processed and tokenized to make it easier for the model to learn. This dataset can be used to fine-tune GPT models, as it provides a more diverse range of language than the OpenAI datasets.