Conclusion

Language-model evaluation measures probability assigned to held-out text. Negative log likelihood gives token-level loss, cross-entropy averages that loss, and perplexity turns it into a branching-factor intuition.

The metric is useful only when the setup is clear. Tokenizer, dataset, split, and leakage all matter.

Next we return to model inputs and study embeddings in language.