What is the relationship between perplexity and entropic measures in NLP?
I'm curious about the connection between perplexity and entropic measures in natural language processing. How do these concepts relate to each other, and what insights can they provide into language models? Any explanations or examples would be greatly appreciated, as I'm eager to deepen my understanding of this topic!
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