ACL Findings2026
PL-MTEB: Polish Massive Text Embedding Benchmark
Rafal Poswiata, Slawomir Dadas, Michal Wiktor Perelkiewicz
摘要
In this paper, we introduce the Polish Massive Text Embedding Benchmark (PL-MTEB), a comprehensive benchmark for text embeddings in the Polish language. PL-MTEB comprises 30 diverse NLP tasks across five categories: classification, clustering, pair classification, information retrieval, and semantic text similarity. Within the scope of this work, we added 12 new Polish-language tasks to MTEB based on existing datasets and prepared two new datasets used to create four clustering tasks. We evaluated 30 publicly available text embedding models, including Polish and multilingual models. We analyzed the results in detail for specific task types and model sizes. We made the prepared datasets, the source code for evaluation, and the obtained results available to the public at https://github.com/rafalposwiata/ pl-mteb. MassiveScenario CBD PolEmo2.0-IN PolEmo2.
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引用它的顶会 Paper4
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- HUME: Measuring the Human-Model Performance Gap in Text Embedding TasksAdnan El Assadi, Isaac Chung, Roman Solomatin, Niklas Muennighoff 等ICLR 2026 · 8 次引用
- ML-Embed: Inclusive and Efficient Embeddings for a Multilingual WorldZiyin Zhang, Zihan Liao, Hang Yu, Peng Di 等ICML 2026
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