What does a lower perplexity value indicate?

A lower perplexity value indicates a better language model performance in predicting text sequences. Essentially, it signifies the model is less surprised or "perplexed" by new, unseen data.

Lower perplexity means the model assigns higher probabilities to the actual words in a test dataset. This typically results from the model effectively capturing the statistical patterns and regularities of the language during training. It allows for objective comparison between different models or versions of the same model evaluated on the same test set. However, perplexity must be interpreted cautiously as it directly measures prediction probability, not guaranteed task performance like translation or text generation fluency.

As a core intrinsic evaluation metric in NLP, perplexity provides a fast and standardized way to assess the quality of a language model's core predictive capability. It helps researchers and developers select better models during training and development before moving to expensive extrinsic evaluations. Lower perplexity often correlates with improved performance in downstream applications.