Can perplexity be used for different types of language models?
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.
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