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        <title>RusConText Benchmark: A Russian Language Evaluation Benchmark for Understanding Context</title>
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        <namePart type="given">Andrey</namePart>
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    <name type="personal">
        <namePart type="given">Svetlana</namePart>
        <namePart type="family">Kuznetsova</namePart>
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    <name type="personal">
        <namePart type="given">Maria</namePart>
        <namePart type="family">Volina</namePart>
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    <name type="personal">
        <namePart type="given">Anna</namePart>
        <namePart type="family">Dengina</namePart>
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            <title>Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)</title>
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        <name type="personal">
            <namePart type="given">Jin</namePart>
            <namePart type="family">Zhao</namePart>
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        <name type="personal">
            <namePart type="given">Mingyang</namePart>
            <namePart type="family">Wang</namePart>
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            <namePart type="given">Zhu</namePart>
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    <abstract>This paper represents an implementation of an approach rather similar to that of (Zhu et al., 2024), adapted for the Russian-language data. We introduce the RusConText Benchmark for evaluating short-context understanding in Russian, comprising four distinct yet interrelated tasks: coreference resolution, discourse understanding, idiom interpretation and ellipsis resolution. Each task targets a specific aspect of linguistic processing, challenging a large language model to recover omitted information, resolve referential dependencies, interpret idioms and discourse. The RusConText Benchmark is an additional resource beyond standard benchmarks, designed to assess model performance from a specific perspective. In addition, we present the results of scoring 4 models on our benchmark.</abstract>
    <identifier type="citekey">chirkin-etal-2025-ruscontext</identifier>
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        <date>2025-07</date>
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%0 Conference Proceedings
%T RusConText Benchmark: A Russian Language Evaluation Benchmark for Understanding Context
%A Chirkin, Andrey
%A Kuznetsova, Svetlana
%A Volina, Maria
%A Dengina, Anna
%Y Zhao, Jin
%Y Wang, Mingyang
%Y Liu, Zhu
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-254-1
%F chirkin-etal-2025-ruscontext
%X This paper represents an implementation of an approach rather similar to that of (Zhu et al., 2024), adapted for the Russian-language data. We introduce the RusConText Benchmark for evaluating short-context understanding in Russian, comprising four distinct yet interrelated tasks: coreference resolution, discourse understanding, idiom interpretation and ellipsis resolution. Each task targets a specific aspect of linguistic processing, challenging a large language model to recover omitted information, resolve referential dependencies, interpret idioms and discourse. The RusConText Benchmark is an additional resource beyond standard benchmarks, designed to assess model performance from a specific perspective. In addition, we present the results of scoring 4 models on our benchmark.
%R 10.18653/v1/2025.acl-srw.91
%U https://aclanthology.org/2025.acl-srw.91/
%U https://doi.org/10.18653/v1/2025.acl-srw.91
%P 1158-1170

Markdown (Informal)

[RusConText Benchmark: A Russian Language Evaluation Benchmark for Understanding Context](https://aclanthology.org/2025.acl-srw.91/) (Chirkin et al., ACL 2025)

• RusConText Benchmark: A Russian Language Evaluation Benchmark for Understanding Context (Chirkin et al., ACL 2025)

ACL

• Andrey Chirkin, Svetlana Kuznetsova, Maria Volina, and Anna Dengina. 2025. RusConText Benchmark: A Russian Language Evaluation Benchmark for Understanding Context. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 1158–1170, Vienna, Austria. Association for Computational Linguistics.