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

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arxiv 2406.02396 v1 pith:2PI23XDG submitted 2024-06-04 cs.CL cs.AI

classification cs.CLcs.AI
keywords embeddingtextscandinavianbenchmarksevaluationmtebacrosscomprehensive
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Benchmarking pre-trained text embedding models in aligning built asset information

    cs.CL 2024-11 conditional novelty 6.0 of 10

    A new public benchmark of 24 text embedding models on six built asset datasets shows uneven performance and that general-purpose benchmarks fail to predict domain-specific results.

  2. BEIR-NL: Zero-shot Information Retrieval Benchmark for the Dutch Language

    cs.CL 2024-12 conditional novelty 4.0 of 10

    BEIR-NL is a Dutch-translated version of the BEIR benchmark with evaluations showing BM25 remains competitive against multilingual dense models.

  3. A Comparative Study of Text Retrieval Models on DaReCzech

    cs.IR 2024-11 conditional novelty 4.0 of 10

    A benchmark on the Czech DaReCzech dataset finds Gemma2 most accurate, Contriever least accurate, and SPLADE/PLAID the best efficiency-quality trade-off.

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