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A model processes the sentence 'The quick brown fox jumps'. The sentence is tokenized, and each token is converted into a corresponding vector. The tokens are indexed starting from 1, so 'The' corresponds to index 1, 'quick' to index 2, and so on. Using the standard notation for a sequence of these vectors, how would you represent the vectors for the subsequence 'quick brown fox'?
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Application in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
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A model processes the sentence 'The quick brown fox jumps'. The sentence is tokenized, and each token is converted into a corresponding vector. The tokens are indexed starting from 1, so 'The' corresponds to index 1, 'quick' to index 2, and so on. Using the standard notation for a sequence of these vectors, how would you represent the vectors for the subsequence 'quick brown fox'?
Consider the sentence 'Large language models are powerful tools', where the words are indexed starting from 1. The notation
e_3, e_4, e_5represents the sequence of embedding vectors for the words 'models are powerful'.Representing a Subsequence with Embedding Notation