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Is it reasonable to assert that perplexity can be applied across various types of language models? Given the diverse architectures and training methodologies in natural language processing, one must critically evaluate whether a single metric like perplexity truly captures the performance nuances of different models or if it's overly simplistic.

Can perplexity be used for different types of language models?

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Can perplexity be used for different types of language models?

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