LLMs are trained on large and diverse datasets that include text from books, articles, websites, and other publicly available content. These datasets cover a wide range of topics, styles, and languages, enabling the model to understand various contexts and writing conventions. For instance, models like GPT are trained on datasets containing encyclopedias, coding forums, and creative writing.
Commonly used datasets include Wikipedia, Common Crawl (a web archive), and curated corpora like OpenWebText. Specialized datasets are sometimes included for domain-specific training, such as medical journals or legal documents. This helps LLMs perform better in specialized tasks when fine-tuned.
Ethical considerations play a role in dataset selection. Developers aim to minimize biases by including diverse sources and ensuring the data complies with copyright and privacy regulations. The quality and variety of the training data directly impact the model’s capabilities and generalization performance.