OpenAI’s data, particularly for training large models like GPT-3 and GPT-4, comes from a variety of publicly available and licensed sources. Here’s a breakdown of the key sources OpenAI uses for training its language models:
1. Public Web Data
- Web Crawls: OpenAI uses a wide range of publicly available web pages, which include websites, blogs, forums, news articles, and other textual content freely available on the internet.
- Books and Articles: Content from publicly available books, research papers, articles, and other publications.
- Wikipedia: Wikipedia’s vast amount of knowledge across different topics is often a key resource.
- Forums and Social Media: While OpenAI may use data from platforms like Reddit or StackExchange (among others), it's important to note that any data derived from these platforms is typically aggregated and anonymized.
2. Licensed Data
OpenAI may also have access to proprietary data through licensing agreements with certain organizations, such as:
- News sources: Subscription-based news websites or archives, which provide high-quality content for training.
- Research Papers: Databases like arXiv or academic publishers where papers are publicly available or licensed for use.
3. Books and Academic Journals
- OpenAI uses a large corpus of books and academic papers across various domains to give the model a broad knowledge base, particularly in specialized fields like science, technology, literature, history, and more.
4. Code and Programming Resources
- Models like GPT-4 have been trained on a large corpus of code from open-source platforms like GitHub to better understand and generate code across a variety of programming languages.
5. Other Datasets
OpenAI uses a range of curated datasets, such as:
- Common Crawl: A massive dataset of web data scraped regularly.
- Project Gutenberg: A collection of free eBooks, especially classic literature.
- Open Subtitles: Text data from movie subtitles, which help improve conversational understanding.
