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The effectiveness of pre-training an encoder-decoder model is significantly influenced by the choice of input corruption methods. Therefore, selecting the most suitable techniques, which in turn define the training objectives, is a critical step that generally requires careful empirical evaluation and experimentation.

Selecting Appropriate Input Corruption Methods

A research team is pre-training an encoder-decoder model that will later be fine-tuned for abstractive text summarization. The team must decide on the most effective input corruption strategy for this pre-training phase. They are considering two primary methods:

1.  **Token Masking:** Randomly replacing 15% of the input tokens with a special `[MASK]` token and training the model to predict the original tokens.
2.  **Sentence Shuffling:** Randomly reordering the sentences within a document and training the model to reconstruct the original sentence order.

Analyze these two options. Which strategy is likely to be more beneficial for preparing a model for abstractive summarization, and why? Justify your reasoning by connecting the skills learned during pre-training to the requirements of the final summarization task.

Selecting a Pre-training Strategy for a Summarization Model

Imagine you are leading a project to pre-train a new encoder-decoder model for the specific task of translating complex legal documents from English to German. You are considering three different input corruption strategies for the pre-training phase: random token masking, token deletion, and text infilling. Describe the experimental methodology you would design to empirically determine which of these three strategies is most effective for your specific downstream task. Your description should include the key steps of the experiment and the evaluation metrics you would use to compare the outcomes.

Designing an Experiment to Select a Pre-training Objective

A research team is pre-training an encoder-decoder model specifically for the task of correcting complex grammatical errors and improving sentence structure in user-generated text. The team wants to select a pre-training objective that will best prepare the model for this downstream task. Which of the following input corruption strategies is most likely to be effective, and why?

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