ARXIV:2605.31142 · LLM EVALUATION · SUBMITTED 01 JUN · 20:27 UTC · FRESHNESS STALE

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On the Robustness of Multilingual Text Embedding Rankings Across Learning Tasks, Languages, and Benchmark Datasets

Ana Gjorgjevikj · Barbara Koroušić Seljak · Tome Eftimov · arXiv

A meta-study of multilingual text embedding models reveals robustness indicators for evaluating performance across diverse tasks and languages, with implications for model selection in industry.

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Pain A meta-study of multilingual text embedding models reveals robustness indicators for evaluating performance across diverse tasks and languages, with implications for model selection in industry.

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PROBLEM

A meta-study of multilingual text embedding models reveals robustness indicators for evaluating performance across diverse tasks and languages, with implications for model selection in industry. Although benchmarking platforms such as MTEB report results across…

METHOD

Full abstract

Large-scale multilingual text embedding models play crucial role in both research and industry, yet their behavior in language-specific, multi-task settings remains insufficiently understood. Although benchmarking platforms such as MTEB report results across more than 250 languages, conclusions about model superiority often depend on implicit choices of dataset compositions and performance aggregation methods. To address this gap, we present a meta-study of multilingual model performance robustness in MTEB, applying a diverse set of multi-criteria decision-making ranking schemes and introducing two robustness indicators: dataset-composition robustness (sensitivity of rankings to changing dataset compositions) and ranking-scheme robustness (sensitivity to aggregation method change). They enable systematic sensitivity analysis of whether benchmarking conclusions remain stable under different evaluation designs. We conduct an in-depth analysis on five languages (English, French, German, Hindi, and Spanish) across nine tasks (e.g., classification, clustering, retrieval) and release results for approximately 230 additional languages. The task-specific analyses show that large-scale LLM-based models are often robust top performers, though not uniformly (e.g., in retrieval task), while task-agnostic results reveal that only a small subset of models remains consistently strong across tasks, ranking schemes, and data subsamples.

RESULT

ScienceToStartup currently rates this 4.0/10 on the public viability pass. Although benchmarking platforms such as MTEB report results across more than 250 languages, conclusions about model superiority often depend on implicit choices of dataset…

WHY NOW

LLM Evaluation moved forward this cycle; last verified June 2026. Public score 4.0/10. Production flags indicate code availability.

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PainA meta-study of multilingual text embedding models reveals robustness indicators for evaluating performance across diverse tasks and languages, with implications for model selection in industry.

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Analysis summary

A meta-study of multilingual text embedding models reveals robustness indicators for evaluating performance across diverse tasks and languages, with implications for model selection in industry.

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A meta-study of multilingual text embedding models reveals robustness indicators for evaluating performance across diverse tasks and languages, with implications for model selection in industry.

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4.0/10 public viability

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Competitive landscape

A meta-study of multilingual text embedding models reveals robustness indicators for evaluating performance across diverse tasks and languages, with implications for model selection in industry.

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LLM Evaluation

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No public code link in the paper record yet

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4.0/10 public viability

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On the Robustness of Multilingual Text Embedding Rankings Across Learning Tasks, Languages, and Benchmark Datasets

On the Robustness of Multilingual Text Embedding Rankings Across Learning Tasks, Languages, and Benchmark Datasets

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