In which tasks is perplexity suitable for use?

Perplexity is primarily suitable for evaluating language models and comparing their prediction quality on text tasks. It serves as a key intrinsic metric for assessing how well a model predicts unseen text sequences.

Lower perplexity indicates a model is more confident and accurate in its predictions. It is most meaningful when comparing models trained and evaluated on the same tokenization scheme and comparable datasets. Perplexity directly relates to cross-entropy, a fundamental loss measure. Its main limitation is that it doesn't directly measure specific application performance or task-specific quality like fluency or coherence.

Its primary application is in the development, selection, and tuning of language models themselves. Researchers and engineers use perplexity to track training progress, compare different model architectures or hyperparameter settings efficiently, and select the best-performing model before costly task-specific fine-tuning or extrinsic evaluation. It provides a valuable, quantifiable signal of core predictive capability.