1. Dataset Size and Scope: Starting with a relatively small dataset of 2.5 billion tokens and then expanding to 11.5 billion tokens highlights the necessity of having a substantial amount of data for the model to learn effectively. Although 11.5 billion tokens may seem minimal compared to the datasets used by larger models, it underscores the challenge of building a high-quality corpus. The size of the dataset directly affects the model's ability to learn a wide range of language patterns and nuances. However, the emphasis on curation indicates that not just the size, but the quality of data is crucial for model training.
2. Bias Reduction: The deliberate avoidance of open internet data is a strategy to reduce the biases inherent in such data sources. By hand-picking content from Project Gutenberg, Wikipedia, and specific Hugging Face datasets, the curator aims to minimize the exposure of the model to contemporary biases, particularly those that are critical in current society. This decision reflects an understanding that while all textual data may contain biases, some sources can offer more controlled and historically contextualized biases than the relatively unfiltered content available on the open internet.
3. Data Quality and Format: The meticulous process of curating the dataset to include primarily paragraph-length content, while excluding titles, references, indexes, and short sentences, is designed to teach the model to generate coherent and continuous text. This approach addresses a common issue with language models, where they may learn to replicate non-informative structures like indexes or reference lists, detracting from the quality of generated text. By focusing on paragraphs, the model is better trained to understand context and produce meaningful, cohesive outputs.
4. Diverse Subject Matter: The selection of specific topics such as biology, history, and philosophy, in addition to reasoning datasets, aims to equip the model with a broad knowledge base and the ability to engage in more complex thought processes. This diversity not only enhances the model's linguistic capabilities but also its reasoning skills, indicating a strategic approach to dataset compilation that goes beyond mere language learning to include cognitive development.
5. Impact on Learning and Output Quality: Through careful dataset selection and curation, the model is more likely to learn a balanced and nuanced understanding of language, reduce the perpetuation of harmful biases, and improve its ability to generate coherent, contextually relevant, and thoughtfully reasoned outputs. This tailored approach to dataset compilation ensures that the model's learning process is aligned with the desired outcomes, emphasizing the importance of quality, diversity, and strategic content selection in training effective language models.

In summary, the selection and curation of a dataset fundamentally shape the learning experience and output quality of a language model by balancing quantity with quality, minimizing biases, ensuring content relevance and coherence, and fostering a broad and deep understanding of language and reasoning.