The process of assembling and cleaning massive text corpora.
The performance of a Large Language Model is profoundly dependent on the quality and scale of its training data. The process of creating a pre-training dataset is a massive data engineering challenge involving several stages. The first stage is Data Collection. The goal is to gather a diverse and comprehensive corpus of text that reflects the breadth of human knowledge and language use. Common sources include massive web scrapes like the Common Crawl dataset, which contains petabytes of data from billions of web pages. Other sources include digitized books (like Google Books), scientific papers (like arXiv), encyclopedias (like Wikipedia), and code repositories (like GitHub). The aim is to create a mixture that provides the model with conversational text, formal writing, technical information, and structured data like code. The second, and arguably most critical, stage is Data Curation and Cleaning. Raw data from the web is extremely noisy. It contains boilerplate HTML, advertisements, spam, low-quality content, and toxic language. This raw data must be heavily filtered and cleaned. Curation involves several steps: quality filtering to remove machine-translated text, gibberish, or overly short documents; deduplication at both the document and sentence level to prevent the model from overfitting to repeated text; and removal of personally identifiable information (PII) to protect privacy. Many organizations also apply toxicity filters to reduce the amount of harmful language the model is exposed to, although this is a complex and imperfect process. The final dataset, often measured in trillions of tokens, is the result of this extensive pipeline. The composition and quality of this dataset directly shape the model's capabilities, knowledge, and inherent biases.