GPT-3’s training data consists of a broad mix of publicly available text from books, websites, and other sources. The dataset includes a wide range of content, such as web pages (via Common Crawl, a large-scale web crawl), digitized books, Wikipedia articles, academic papers, and online forums. The data spans diverse topics, languages, and writing styles, collected up to October 2019. For example, Common Crawl alone contributed roughly 60% of the training data, while curated sources like books and Wikipedia made up smaller but significant portions. This combination allows GPT-3 to handle general-purpose language tasks by learning patterns from varied contexts.
The dataset was preprocessed to remove low-quality or redundant content. For instance, duplicates were filtered out to avoid overfitting, and documents were scored for quality to prioritize well-structured text. Tokenization—breaking text into manageable units—was done using byte-pair encoding (BPE), which balances rare and common words effectively. Developers should note that while the data is extensive, it isn’t evenly distributed. Technical content, like programming documentation, is less prevalent than general web text, which can affect performance in niche domains. Additionally, the data reflects biases and inaccuracies present in its sources, as no manual fact-checking was applied. For example, outdated information or controversial opinions in the training data may surface in model outputs.
A key limitation is the lack of transparency about specific sources. OpenAI hasn’t disclosed exact details, making it harder to audit biases or copyright issues. For developers, this means GPT-3 might inadvertently reproduce sensitive or copyrighted material from its training data. The cutoff date also means it lacks knowledge of events post-2019, like the COVID-19 pandemic. When integrating GPT-3 into applications, these factors require mitigation strategies—such as post-processing filters or combining the model with up-to-date databases—to ensure reliable and responsible outputs. Understanding these constraints helps developers set realistic expectations and design robust systems around them.