How does perplexity differ from other evaluation metrics in NLP?

What sets perplexity apart from other evaluation metrics in natural language processing? Given the multitude of metrics available, it's crucial to understand how perplexity uniquely measures model performance. Does it truly provide a more nuanced understanding of language models, or is it just another statistic that lacks practical significance?

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