Part 3/10:
To address this limitation, Suver proposes a framework for scaling the training dataset from one trillion tokens to ten trillion tokens. This shift necessitates a more exhaustive and representative multilingual approach, moving beyond the predominately English-focused model training to include substantial spans of other languages such as Chinese, Hindi, French, and Spanish. The need for improved curation tools—such as automated data cleaning processes and advanced tokenizers—is paramount to manage the morphed nature of diverse languages and formats.