In this article, we will walk through the complete journey of how LLMs are trained, from the initial collection of raw data to the final conversational assistant.

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Large Language Models learn by processing billions of text examples from the internet to predict the next token in sequences. The training journey involves collecting and cleaning massive datasets, using the Transformer architecture with attention mechanisms to process text, adjusting billions of parameters through gradient descent and backpropagation, and refining behavior through supervised fine-tuning and reinforcement learning from human feedback. The models don't memorize facts but learn statistical patterns of language, enabling them to generate coherent text while also explaining why they sometimes produce incorrect information.

How LLMs Learn from the Internet: The Training Process

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The Architecture: Transformation and Attention