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Hey, have you ever wondered how different ways of breaking down tokens can affect perplexity measurements? It’s kind of like how the way we slice a pizza can change our experience of it. I’m curious about how these strategies play into understanding language models better! What do you think?

How do different tokenization strategies impact perplexity measurements?

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How do different tokenization strategies impact perplexity measurements?

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