Why is understanding perplexity important in natural language processing?

Why is it crucial to grasp the concept of perplexity in natural language processing? Understanding this metric seems essential for evaluating language models, yet many overlook its significance. How can we effectively assess model performance and ensure meaningful interpretations without a solid comprehension of perplexity's role in NLP?

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