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How LLMs Learn: The Pre-Training Phase Explained
- Large language models (LLMs) learn during the pre-training phase by being fed a huge amount of text to understand language rules and context.
- Common Crawl provides data from 250 billion web pages for pre-training, but preprocessing to remove noise is crucial.
- Tokenization breaks text into manageable tokens for numerical processing, with methods like Byte Pair Encoding (BPE) being common.
- Models like GPT-4o use subword-based tokenization to handle large vocabularies more efficiently.
- Training involves Next Token Prediction and Masked Language Modeling to learn language structure and relationships between tokens.
- Base models learn to generate text one token at a time and serve as a starting point for further fine-tuning.
- Base models can memorize text patterns but may struggle with reasoning tasks due to limited structured understanding.
- In-context memory allows base models to adjust responses based on the provided context, demonstrating versatility without fine-tuning.
- Base models excel in replicating text based on memorized patterns but may lack originality and deep reasoning abilities.
- In the pre-training phase, LLMs develop foundational skills by learning from raw data before advanced techniques are applied for post-training.
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