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You are tasked with building a system that can generate captions for images. You want to use a transformer-based model. During inference, you notice that the model tends to generate repetitive captions. Which of the following decoding strategies could you use to mitigate this issue?
Correct Answer: C,E
Greedy decoding often leads to repetitive sequences because it always chooses the most likely next word. Beam search, especially with a high beam width, can also lead to repetition if the highest-probability sequences contain repeated phrases. Beam search with a length penalty encourages the model to generate longer and more diverse captions. Random sampling and Top-k sampling introduce randomness into the decoding process, which can help to break out of repetitive loops and create more varied captions.