BERTScore uses BERT embeddings to measure semantic similarity between generated and reference text. How it works: Encode candidate and reference sentences with BERT, compute pairwise cosine similarity between token embeddings, greedily match tokens, aggregate into precision, recall, F1. Advantages over BLEU/ROUGE: Captures semantic similarity not just n-gram overlap. Same meaning, different words gets credit. Calculation: For each candidate token, find most similar reference token (and vice versa). Precision = avg best match for candidate tokens. Recall = avg best match for reference tokens. IDF weighting: Optionally weight tokens by inverse document frequency (rare words matter more). Layer selection: Different BERT layers capture different features. Later layers often better for semantics. Use cases: Machine translation, summarization, text generation evaluation. Limitations: Still a proxy (not human judgment), can be fooled by adversarial examples, computationally heavier than BLEU. Variants: RoBERTa-based, multilingual versions available. Best practice: Use alongside other metrics, validate correlation with human judgment for your task.