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        <title>mdok-style at SemEval-2026 Task 9: Finetuning LLMs for Multilingual Polarization Detection</title>
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        <namePart type="given">Dominik</namePart>
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        <namePart type="given">Alok</namePart>
        <namePart type="family">Debnath</namePart>
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            <title>Proceedings of the 20th International Workshop on Semantic Evaluation (2026)</title>
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            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Kochmar</namePart>
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            <namePart type="given">Debanjan</namePart>
            <namePart type="family">Ghosh</namePart>
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        <name type="personal">
            <namePart type="given">Kai</namePart>
            <namePart type="family">North</namePart>
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        <name type="personal">
            <namePart type="given">Mamoru</namePart>
            <namePart type="family">Komachi</namePart>
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    <abstract>SemEval-2026 Task 9 is focused on multilingual polarization detection. Specifically, it covers the identification of multilingual, multicultural and multievent polarization along three axes (in subtasks), namely detection, type, and manifestation. Online polarization presents a concern, because it is often followed by hate speech, offensive discourse, and social fragmentation. Therefore, its detection before it escalates is crucial for a safer and more inclusive online space. We have coped with this SemEval task by finetuning mid-size LLMs for the sequence-classification task using the QLoRA parameter-efficient finetuning technique. The training data augmented the multilingual (22 languages) training sets by anonymized, lower-cased, upper-cased, and homoglyphied counterparts, making the detection more robust.</abstract>
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%0 Conference Proceedings
%T mdok-style at SemEval-2026 Task 9: Finetuning LLMs for Multilingual Polarization Detection
%A Macko, Dominik
%A Debnath, Alok
%A Simko, Jakub
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F macko-etal-2026-mdok
%X SemEval-2026 Task 9 is focused on multilingual polarization detection. Specifically, it covers the identification of multilingual, multicultural and multievent polarization along three axes (in subtasks), namely detection, type, and manifestation. Online polarization presents a concern, because it is often followed by hate speech, offensive discourse, and social fragmentation. Therefore, its detection before it escalates is crucial for a safer and more inclusive online space. We have coped with this SemEval task by finetuning mid-size LLMs for the sequence-classification task using the QLoRA parameter-efficient finetuning technique. The training data augmented the multilingual (22 languages) training sets by anonymized, lower-cased, upper-cased, and homoglyphied counterparts, making the detection more robust.
%U https://aclanthology.org/2026.semeval-1.46/
%P 314-321

Markdown (Informal)

[mdok-style at SemEval-2026 Task 9: Finetuning LLMs for Multilingual Polarization Detection](https://aclanthology.org/2026.semeval-1.46/) (Macko et al., SemEval 2026)

• mdok-style at SemEval-2026 Task 9: Finetuning LLMs for Multilingual Polarization Detection (Macko et al., SemEval 2026)

ACL

• Dominik Macko, Alok Debnath, and Jakub Simko. 2026. mdok-style at SemEval-2026 Task 9: Finetuning LLMs for Multilingual Polarization Detection. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 314–321, San Diego, California, USA. Association for Computational Linguistics.