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Categorizing pre-training tasks based purely on model architecture is suboptimal because a single training objective can be applied across varying architectures (e.g., masked language modeling works for both encoder-only and encoder-decoder setups). Therefore, a better approach is to classify these tasks based on their specific training objectives.

Rationale for Categorizing Pre-training Tasks by Objective

Based on the provided scenario, should the pre-training task be classified as one method or two? Justify your answer by explaining the most effective principle for categorizing such tasks.

Classifying a Novel Pre-training Method

Explain why classifying pre-training tasks for language models by their objective (e.g., predicting masked tokens) is considered a more robust and conceptually coherent approach than classifying them by model architecture (e.g., encoder-only). Provide a specific example to support your reasoning.

Justification for Pre-training Task Classification

A research team trains two different models: one with an encoder-only structure and another with an encoder-decoder structure. Both models are trained using the exact same objective: predicting randomly masked words within a text. A colleague argues that because the model structures are different, they should be classified under separate pre-training categories. Why is this argument fundamentally flawed from a conceptual standpoint?

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