![]() Importance: Tokenization is crucial for preparing the text for numerical processing. Example: “unhappiness” → Ĭharacter Tokenization: Splits the text into individual characters.These models are often built using neural networks, particularly the Transformer architecture, which enables them to handle long sequences of text effectively. ![]() It learns the statistical properties of the language, such as syntax, semantics, and context, to generate text that closely resembles human language. A Large Language Model is a machine learning model trained on a vast dataset comprising text from books, websites, and other sources. The details of how they predict the next word are often treated as a deep mystery. If you know anything about this subject, you’ve probably heard that LLMs are trained to “predict the next word” and that they require huge amounts of text to do this. But how do they work? This article aims to demystify the underlying mechanisms of Large Language Models, focusing on the concept of word-to-vector calculations and embeddings. ![]() ![]() Among these, Large Language Models stand out for their ability to understand and generate human-like text. In the age of information, language models have become an essential tool for various applications, from chatbots to recommendation systems. ![]()
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