In machine learning, queries, keys, and values are core components of attention mechanisms, enabling models to weigh input importance. Queries search for relevant information, keys act as identifiers, and values carry the content. This structure powers transformers in tasks like language translation and text generation, benefiting AI researchers, data scientists, and developers building advanced NLP systems.
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