Is a smaller perplexity better?

Perplexity measures how well a probability model predicts a test sample. A smaller perplexity signifies the model assigns higher probability to the actual test data, implying less uncertainty or "surprise". Crucially, it's only directly comparable between models using the same vocabulary and tokenization. While a vital intrinsic metric for model refinement and training progress, a lower perplexity doesn't automatically translate to superior performance in downstream tasks like question answering or reasoning. It also doesn't guarantee factual accuracy or usefulness.

Models optimized for lower perplexity are often preferred for tasks heavily reliant on predicting fluent and coherent text sequences, such as machine translation, text summarization, and continuation. Minimizing perplexity helps align the model's outputs more closely with the statistical patterns of the training data. However, extrinsic evaluation metrics and human judgment are essential complements to perplexity for assessing real-world applicability and quality.