What happens when a model is overfit according to perplexity measurements?

When a model is overfit, it means that it has learned the training data too well, capturing noise and fluctuations that do not generalize to unseen data. In the context of perplexity measurements, an overfit model usually yields too low perplexity on the training dataset. This indicates that the model predicts the training data exceptionally well, often highlighting its inability to generalize to new, unseen examples.

Perplexity is a measure of how well a probability distribution predicts a sample, typically used in language models to assess performance. A low perplexity indicates that the model assigns high probabilities to the training data sequences, suggesting it fits those specific sequences very closely. However, this low perplexity does not mean the model will perform well on new input, which is a key sign of overfitting.

Thus, when perplexity is too low, it is often an indication of overfitting, as the model may have memorized the training data rather than learned its underlying distributions.