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What are the key limitations associated with using perplexity as a performance metric in natural language processing? Understanding these constraints is crucial for evaluating model effectiveness, as perplexity may not fully capture aspects such as contextual relevance, semantic coherence, or real-world applicability. What should researchers consider when interpreting this metric?

What are the limitations of using perplexity as a performance metric?

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