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ACL Findings2026

PL-MTEB: Polish Massive Text Embedding Benchmark

Rafal Poswiata, Slawomir Dadas, Michal Wiktor Perelkiewicz

Abstract

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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Cited by top-tier papers4

  1. MMTEB: Massive Multilingual Text Embedding BenchmarkKenneth C. Enevoldsen, Isaac Chung, Imene Kerboua, Márton Kardos et al.ICLR 2025 · 10 citations
  2. HUME: Measuring the Human-Model Performance Gap in Text Embedding TasksAdnan El Assadi, Isaac Chung, Roman Solomatin, Niklas Muennighoff et al.ICLR 2026 · 8 citations
  3. ML-Embed: Inclusive and Efficient Embeddings for a Multilingual WorldZiyin Zhang, Zihan Liao, Hang Yu, Peng Di et al.ICML 2026
  4. SkMTEB: Slovak Massive Text Embedding Benchmark and Model AdaptationMarek Suppa, Andrej Ridzik, Daniel Hládek, Natália Knazeková et al.ACL 2026

Builds on14

  1. SimCSE: Simple Contrastive Learning of Sentence EmbeddingsTianyu Gao, Xingcheng Yao, Danqi ChenEMNLP 2021 · 2,496 citations
  2. M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge DistillationJianlyu Chen, Shitao Xiao, Peitian Zhang, Kun Luo et al.ACL Findings 2024 · 762 citations
  3. Large Dual Encoders Are Generalizable RetrieversJianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai et al.EMNLP 2022 · 145 citations
  4. Dense Passage Retrieval for Open-Domain Question AnsweringVladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis et al.EMNLP 2020 · 142 citations
  5. TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding LearningKexin Wang, Nils Reimers, Iryna GurevychEMNLP Findings 2021 · 130 citations

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