As of my last knowledge update in September 2021, the specific details of the training data used to pre-train ChatGPT may not be publicly disclosed by OpenAI, and the information available might be proprietary. However, I can provide a general overview of the kind of training data that is typically used to pre-train models like ChatGPT.

Pre-training a model like ChatGPT involves exposing it to a vast and diverse corpus of text data from the internet.

The training data often includes but is not limited to :

* Books : Large collections of books covering various topics and genres. Books provide structured and well-written content, making them valuable for language modeling.

* Websites : Text extracted from web pages, forums, blogs, news articles, and other online sources. This data helps the model understand informal language, current events, and a wide range of subjects.

* Wikipedia : Wikipedia articles are a valuable source of structured and factual information, helping the model acquire general knowledge.

* News Sources : Data from news websites and articles to keep the model updated with current events and news-related language.
* Common Crawl : This is a repository of web pages from across the internet, providing a diverse set of text data.

* Chat Logs : Chat logs from various sources can be used to expose the model to conversational language and dialogue patterns.

* Scientific Papers : Text extracted from scientific journals and publications, helping the model understand technical and scientific language.

* Encyclopedias : Similar to Wikipedia, encyclopedias provide structured information on a wide range of topics.

* User-Generated Content : Text from social media platforms, user reviews, and other forms of user-generated content to expose the model to informal and colloquial language.

* Multilingual Data : Text in multiple languages to enable the model to understand and generate text in different languages.