What does perplexity mean in AI?
Perplexity measures how uncertain a language model is when predicting the next token in a sequence. It quantifies a model's prediction confidence on unseen text, where lower values indicate better performance.
It is mathematically the inverse probability of the test set normalized by the number of words. Lower perplexity means the model assigns higher likelihood to the test data. It primarily compares different models on the same dataset or tracks training progress. Note that lower perplexity doesn't guarantee better real-world task performance or language fluency.
Its key value lies in evaluating language model quality, especially comparing architectures or training approaches. Developers use it extensively during model development, fine-tuning, and selection phases to gauge intrinsic predictive capability efficiently before costly real-world task evaluation. It is a fundamental metric in NLP research and application development.
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