Summary

In this chapter, we laid out a comprehensive pathway for training LLMs, beginning with the imperative stage of data preparation and management. A robust corpus – varied, extensive, and balanced – is the bedrock upon which LLMs stand, requiring a diverse spectrum of text encompassing a broad scope of topics, cultural and linguistic representations, and temporal spans. To this end, we detailed the significance of collecting data that ensures a balanced representation and mitigates biases, hence fostering models that deliver a refined understanding of language.

Following the collection, rigorous processes of cleaning, tokenization, and annotation come into play to refine the quality and utility of data. These steps remove noise and standardize the text, breaking it into tokens that the model can efficiently process and annotate to provide contextual richness.

Data augmentation and preprocessing practices were emphasized as pivotal in expanding the scope of...