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A language model is pre-trained using a method where 15% of the words in an input sentence are selected for prediction. Of these selected words, a small fraction (10%) are intentionally left in their original form, while the model is still tasked with predicting them based on the surrounding context. What is the most significant reason for this strategy of leaving some target words unchanged?
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Ch.1 Pre-training - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
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Analysis in Bloom's Taxonomy
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Example of an Unchanged Token in a BERT Input Sequence
A language model is pre-trained using a method where 15% of the words in an input sentence are selected for prediction. Of these selected words, a small fraction (10%) are intentionally left in their original form, while the model is still tasked with predicting them based on the surrounding context. What is the most significant reason for this strategy of leaving some target words unchanged?
Calculating Token Modifications in Pre-training
Critique of a Modified Pre-training Strategy
Purpose of Unchanged Tokens in BERT's MLM Strategy