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This video from Cohere AI discusses word and sentence embeddings, which are integral to large language models. The purpose of language models is to enable computers to understand and process language, transitioning from words to numerical representations. Word embeddings map each word to a list of numbers, while sentence embeddings do the same for sentences in a meaningful way. This mapping is not done manually; instead, it is performed by complex neural networks that analyze context. When two words appear in similar contexts, the model brings their numerical representations closer together. This process ultimately yields impressive results in language understanding and processing.
