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The video tutorial discusses word and sentence embeddings, which are essential components of large language models. Language models aim to enable computers to understand and process language, transitioning from words to numerical representations, as computers work with numbers. Word embeddings associate each word with a list of numbers, while sentence embeddings do the same for sentences, reflecting their meanings. This process is achieved not by humans but by complex neural networks that analyze context; when two words frequently appear together, the model adjusts their numerical representations to be closer. The result is an effective way for computers to process and understand language.
