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The video tutorial discusses word and sentence embeddings, essential components of large language models. These embeddings enable computers to process language by converting words and sentences into numerical representations, as computers work with numbers rather than words. This conversion is not done manually; instead, a neural network analyzes the context in which words and sentences appear together. As a result, words appearing in similar contexts are associated more closely. The method highlights the capability of embeddings to effectively capture linguistic relationships, ultimately producing impressive outcomes in language understanding.
