Semantic Similarity Score

Also known as: semantic score, embedding similarity

A semantic similarity score measures how close two texts are in meaning by comparing their embedding vectors — typically with cosine similarity — rather than requiring exact string overlap. It captures paraphrases and synonym variations that exact match misses. In NLG evaluation, semantic similarity to a reference is used as a proxy for content quality, though it does not verify factual accuracy and can score fluent but incorrect paraphrases as high-quality responses.