Semantic Textual Similarity

Semantic similarity measures how close two pieces of text are in meaning — the foundation of duplicate detection, paraphrase mining, and retrieval. STS Benchmark scores climbed from 70 (GloVe averages) to 86+ with Sentence-BERT, and now exceed 92 with models like GTE-Qwen2 and E5-Mistral that leverage billion-parameter backbones. The real shift was from symmetric similarity (are these two sentences paraphrases?) to asymmetric retrieval (does this passage answer this query?), driven by the RAG revolution that made embedding quality a production-critical metric. Cross-lingual semantic similarity remains a hard frontier — models trained primarily on English still lose 5-10 points when comparing sentences across language families, despite multilingual pretraining.