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Perplexity and Language Model Evaluation
Perplexity and Language Model Evaluation
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conceptual
Why is perplexity useful when evaluating a language model?
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A.
It is the number of parameters divided by the batch size
B.
It summarizes held-out next-token likelihood on a more interpretable scale
C.
It proves the model will answer every user question correctly
D.
It removes the need for a validation set during training
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