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Alongside the rise of the Transformer architecture, the concept of language modeling was generalized to encompass models that learn to predict words in various ways, rather than strictly predicting the next token in a sequence. Many powerful Transformer-based models were pre-trained using these diverse word prediction tasks and successfully applied to a wide variety of downstream tasks.

Generalization of the Language Modeling Concept

Which of the following scenarios best exemplifies the generalization of the language modeling concept beyond its traditional definition of strictly predicting the next word in a sequence?

A model designed solely to fill in a blank word in the middle of a sentence (e.g., 'The quick brown ___ jumps over the lazy dog') is performing the task of language modeling according to its original, traditional definition.

Contrast the traditional, sequential definition of a language model with its more generalized, modern interpretation. What fundamental change in how contextual information is processed characterizes this evolution?

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