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You're training a multimodal model for generating stories from images and audio. You use a Transformer architecture. During training, you notice that the model struggles to maintain long-range dependencies in the generated stories, leading to incoherent narratives. Which of the following techniques would be MOST effective in addressing this issue within the Transformer architecture?
Correct Answer: C
Positional encodings help the Transformer understand the order of words in the sequence, which is crucial for maintaining coherence. Increasing the attention window size allows the model to attend to a larger context when generating each word, enabling it to capture longer-range dependencies. Reducing layers or embedding dimension would likely worsen the problem. Removing self-attention would defeat the purpose of using a Transformer. Positional encodings and attention window size are key to transformer performance with respect to long range dependencies.