Perplexity is a measurement used in language models to evaluate how well a probability distribution predicts a sample. It quantifies the uncertainty of the model when generating text, with lower perplexity indicating better performance. Essentially, it reflects how surprised the model is by the actual sequence of words it encounters.
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