The article discusses the rapid evolution of Large Language Models (LLMs) since the release of OpenAI's GPT-3, highlighting their tokenization process. Inspired by a video from Andrej Karpathy, it explains how LLMs, built on transformer neural networks, convert textual input into numerical representations through tokens. These tokens serve as fundamental data units that neural networks then transform to make predictions. The article emphasizes the importance of understanding tokenization for comprehending the language processing capabilities of models like ChatGPT, which rely on intricate mathematical structures to generate text.
Tokens serve as the foundational units of input data for LLMs, which are processed mathematically through transformer neural networks, ultimately enabling effective text generation.
Understanding the intricacies of tokenization is essential for grasping how LLMs like ChatGPT process language, transforming input into meaningful outputs.
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