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Transformers in contextual generation and summarization
The transformer model can also be used for the contextual generation task and text summarization task.
During the contextual generation, the model is given some prefix text and will output a possible completion to it. The transformer model can have direct access to all the prefix text and the subsequently generated output of its own.
As for the text summarization task, the training set contains multiple full-length articles accompanied by their summaries with a unique marker separating these two parts, where one training unit is like . Teacher-forcing also applies during the training.
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Transformers in contextual generation and summarization
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- Approach 1: Processes the input text sequentially, token by token, updating an internal state that is passed from one step to the next.
- Approach 2: Processes all input tokens simultaneously, using a mechanism that directly relates every token to every other token in the input to determine context.
Which of the following statements best analyzes the primary trade-off between these two approaches for this specific task?
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