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Example of an Indexed Sentence with Non-Sequential Order
This example demonstrates how a sentence can be represented with non-sequential numerical indices for a language modeling task. In the sentence The8 ball9 rolls10 away swiftly12 ., each word is paired with a number that indicates its position in a permuted sequence, which is not necessarily the standard grammatical order.
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Ch.1 Pre-training - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Example of an Indexed Sentence with Non-Sequential Order
Example of a Sequentially Indexed Sentence
Example of a Permuted Sentence with Non-Sequential Indexing
Example of Permuted Language Modeling with a Shuffled Sentence
Consider two different training objectives for a language model. In Objective 1, the model learns by predicting a few randomly obscured words in a sentence, using all the other visible words as context. In Objective 2, the model is given a sentence's words in a randomly shuffled order and must predict them one by one according to that shuffled sequence, only using the words that have already appeared in that sequence as context. Which of the following statements best analyzes the key advantage of Objective 2?
A language model is trained using an objective where it predicts words from an input sentence one by one, but in a randomly shuffled order. For the sentence 'The quick brown fox', the model is given the prediction order [3, 1, 4, 2], corresponding to the original word positions. Arrange the following prediction tasks in the correct sequence that the model would perform.
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Encoding Process in Permuted Language Modeling
Example of Permuted Language Modeling
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A language model receives the following input, where each word is paired with a number indicating its position in a shuffled sequence:
quick4 The1 brown2 fox5 jumps3. Based on these numerical positions, what is the actual sequence of words the model processes?Analyzing a Permuted Prediction Task
A language model is given the following tokens from a sentence, where each word is paired with a number. This number indicates the order in which the model should process the token. Arrange the tokens below into the correct processing sequence, from the first to be processed to the last.