Building an LLM Stack Part 2: Pre-training Tips & Data Processing Tricks

Large language models are generally trained through broad pre-training followed by task-specific fine-tuning, analogous to general education followed by specialization. Pre-training most commonly uses causal language modeling, in which a model learns to predict the next token from preceding context, while masked language modeling, popularized by BERT, predicts deliberately hidden tokens using context from both directions. Although the basic training procedure is relatively standardized, dataset selection and processing are presented as the principal determinants of model capability. Large web corpora such as Common Crawl provide extensive and current internet data but require filtering because they contain low-quality, duplicate, and unsuitable material. GPT-2 addressed this by using WebText, a smaller dataset built from Reddit-linked pages, while GPT-3 combined filtered and deduplicated Common Crawl data with curated sources including WebText, books, and Wikipedia. The discussion notes that modern model developers increasingly withhold dataset details for competitive reasons, sometimes supplementing public web data with licensed or synthetic material, and argues that growing corporate control over high-quality data may limit access and participation in AI research.

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