The Scandinavian Embedding Benchmarks: Comprehensive Assessment of Multilingual and Monolingual Text Embedding

Kenneth Enevoldsen, Márton Kardos, Niklas Muennighoff, Kristoffer Nielbo

Keywords: sentence embeddings, rag, low-resource NLP, Danish, Norwegian, Swedish, Scandinavian

TL;DR: We introduce the Scandinavian Embedding Benchmark (SEB); a comprehensive framework for text embedding evaluation for Scandinavian languages across 24 tasks, 10 subtasks, and 4 task categories.

Abstract: The evaluation of English text embeddings has transitioned from evaluating a handful of datasets to broad coverage across many tasks through benchmarks such as MTEB. However, this is not the case for multilingual text embeddings due to a lack of available benchmarks. To address this problem, we introduce the Scandinavian Embedding Benchmark (SEB). SEB is a comprehensive framework that enables text embedding evaluation for Scandinavian languages across 24 tasks, 10 subtasks, and 4 task categories. Building on SEB, we evaluate more than 26 models, uncovering significant performance disparities between public and commercial solutions not previously captured by MTEB. We open-source SEB and integrate it with MTEB, thus bridging the text embedding evaluation gap for Scandinavian languages.

Submission Number: 611