Why is perplexity important in NLP?

Why is perplexity considered a crucial metric in natural language processing? What specific role does it play in evaluating language models, and how does it impact the overall effectiveness of NLP applications? Is there a risk of over-relying on this measure, potentially overlooking other important factors in model performance?

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