Training LLM - Dataset

Large Language Models (LLMs) like OpenAI's GPT-4 are trained on a diverse and extensive range of internet text sources. 

For explanation on GAI view this link:

The introduction to GAI and LLM can be viewed here :

The data used for training these models typically include:

1. Books: A wide variety of books, covering fiction, non-fiction, and textbooks, provide a rich source of diverse vocabulary and complex sentence structures.

2. Websites: Content from a broad array of websites, including news sites, blogs, and forums, contributes to the model's understanding of current events, colloquial language, and various viewpoints.

3. Scientific Papers: Academic and scientific literature helps in training the model to understand and generate text related to scientific, technical, and academic topics.

4. Wikis: Websites like Wikipedia offer structured and well-organized information on a vast array of topics, which is valuable for training models in factual knowledge and encyclopedic information.

5. Common Crawl Data: Common Crawl is an organization that crawls the web and freely provides its archives. This data is a massive, diverse dataset that helps in training the models on current and historical web content.

6. Online Forums and Discussion Boards: These sources provide insights into informal, conversational language and diverse viewpoints on a multitude of topics.

7. News Articles: Current and historical news articles provide the models with a wide range of reporting styles and coverage of events over time.

8. Instructional and Training Manuals: These texts help the model learn technical and instructional language.

9. Legal and Governmental Documents: To understand formal language and complex structures typical of legal and governmental texts.

The training process involves feeding these texts into the model, allowing it to learn patterns, language structures, and information. The model does not store specific texts or personal data but rather learns the underlying structure and patterns of the language.

It's important to note that while these models are trained on vast and diverse datasets, they are not immune to biases present in the training data. Efforts are continually made to mitigate these biases and improve the accuracy and fairness of the models.