RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark
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Abstract (arXiv)
In this paper, we introduce an advanced Russian general language understanding evaluation benchmark -- RussianGLUE. Recent advances in the field of universal language models and transformers require the development of a methodology for their broad diagnostics and testing for general intellectual skills - detection of natural language inference, commonsense reasoning, ability to perform simple logical operations regardless of text subject or lexicon. For the first time, a benchmark of nine tasks, collected and organized analogically to the SuperGLUE methodology, was developed from scratch for the Russian language. We provide baselines, human level evaluation, an open-source framework for evaluating models (https://github.com/RussianNLP/RussianSuperGLUE), and an overall leaderboard of transformer models for the Russian language. Besides, we present the first results of comparing multilingual models in the adapted diagnostic test set and offer the first steps to further expanding or assessing state-of-the-art models independently of language.
Key facts. Cited by 53 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Oct 2020.
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Citing papers per year: 2021: 11; 2022: 14; 2023: 9; 2024: 10; 2025: 6; 2026: 3.
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- 2023Impact of Tokenization on LLaMa Russian Adaptation5 Dec 2023
- 2023A Family of Pretrained Transformer Language Models for Russian19 Sep 2023
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- 2022The Defeat of the Winograd Schema Challengecited by 25
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- 2025skLEP: A Slovak General Language Understanding Benchmark26 Jun 2025
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- 2025REPA: Russian Error Types Annotation for Evaluating Text Generation and Judgment Capabilities17 Mar 2025
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- 2024Long Input Benchmark for Russian Analysis5 Aug 2024
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