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Large language models (LLMs) learn through a process called unsupervised learning, primarily using vast amounts of text data. They rely on transformer architectures, which enable them to understand context and relationships in language. During training, the model processes large datasets, learning to predict the next word in a sentence, which helps it understand grammar, facts, and nuances. This involves adjusting millions or even billions of parameters through techniques like backpropagation an
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Large language models (LLMs) learn through a process called unsupervised learning, primarily using vast amounts of text data. They rely on transformer architectures, which enable them to understand context and relationships in language. During training, the model processes large datasets, learning to predict the next word in a sentence, which helps it understand grammar, facts, and nuances. This involves adjusting millions or even billions of parameters through techniques like backpropagation an
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