Text Embedding Evaluation
Overview
Text embedding evaluation studies whether vectors produced by language models preserve the distinctions needed for tasks such as semantic similarity, search, clustering, and classification. Researchers use broad suites such as MTEB and BEIR alongside domain- and language-specific benchmarks, controlled contrastive tests, and analyses of embedding geometry and robustness. Results show that model rankings often vary with task, language, prompts, data, and similarity metric, so performance on one benchmark may not predict performance elsewhere. A central challenge is ensuring that benchmark scores reflect meaningful distinctions and real deployment needs, including latency, cost, and the value of combining dense embeddings with lexical retrieval.
Latest papers
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