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An example of a denoising autoencoder task involving consecutive token masking is training a model to reconstruct a full sentence from a corrupted input. The model receives an input where adjacent tokens have been replaced by `[MASK]`, and its objective is to generate the original, complete sequence. For instance, the input `[CLS] The puppies are [MASK] outside [MASK] [MASK] .` would be used to train the model to produce the target output: `⟨s⟩ The puppies are frolicking outside the house .`.

Example of Denoising Task with Consecutive Token Masking

Based on the provided case study, explain why the team's chosen training task is a logical approach to address the specific problem they identified with their model.

Improving Phrase Generation in a Language Model

A language model is trained using a method where it receives a sentence with a sequence of several adjacent words replaced by a special marker (e.g., `The team celebrated their [MASK] [MASK] [MASK] .`). The model's task is to generate the original, complete sentence (e.g., `The team celebrated their hard-earned victory .`). Beyond simply learning vocabulary, what specific capability related to language structure does this training method help the model develop?

Benefit of Consecutive Token Masking

A language model is being trained with pairs of text sequences. Consider the following training example: Input: `The committee reviewed the [MASK] [MASK] [MASK] and approved it.` Target Output: `The committee reviewed the detailed project proposal and approved it.` Based on this training example, what is the primary learning objective for the model?

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