Can perplexity reflect a model's language ability?

Perplexity is a quantitative measure that evaluates how well a language model predicts a given text sequence. While it correlates with basic language modeling competence, it cannot comprehensively reflect a model's overall language abilities.

Perplexity assesses the model's ability to predict the next token accurately based on its probability estimates. Lower perplexity generally indicates better prediction performance on the test data provided. However, it primarily measures lexical and syntactic prediction skills at the token level. Crucially, it does not directly evaluate higher-level abilities like semantic understanding, coherence in generation, reasoning, or factual accuracy. It also relies heavily on the specific training and test datasets used.

As an intrinsic evaluation metric, perplexity is valuable for benchmarking and comparing core language modeling capabilities during development and training phases. It serves as a useful, computationally efficient proxy for model convergence and performance on predicting unseen text. However, due to its limitations in assessing broader language understanding and generation quality, it must be complemented with extensive extrinsic evaluation using specific task metrics and human judgment.