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How does a deeper understanding of perplexity contribute to enhancing the performance of language models? In what ways can this metric be utilized to refine model training and evaluation processes, ultimately leading to improved outcomes in natural language processing tasks? What are the implications for model accuracy and efficiency?

How does understanding perplexity improve model performance?

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How does understanding perplexity improve model performance?

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