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Example of Permuted Language Modeling
In permuted language modeling, the input sequence is provided in its natural order, but the model learns to predict the tokens using an arbitrarily permuted generation order. For example, given the full input sentence [C] The kitten is chasing the ball ., the model might be tasked with generating the output tokens in a non-sequential order such as: The kitten is chasing the ball .. Here, the superscripts verify that chasing is generated first, ball is generated second, and so on, simulating an autoregressive process over a shuffled target ordering.
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Foundations of Large Language Models
Ch.1 Pre-training - 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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Example of Permuted Language Modeling