Training Data Volume

Training data volume refers to the totality of structured or unstructured information used in the development of machine learning models, which fundamentally determines the algorithm's accuracy and generalization capability. For modern large language models (LLMs), this amounts to a petabyte-scale text corpus containing multiple trillions of tokens, sourced from books, websites, and codebases. Ensuring adequate quantity and quality of data is critical for model performance, as it enables the acquisition of rare linguistic patterns and complex correlations, while also raising legal and ethical questions about data collection.