mdok-style at SemEval-2026 Task 9: Finetuning LLMs for Multilingual Polarization Detection
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914528126566400 |
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| author | Macko, Dominik Debnath, Alok Simko, Jakub |
| author_facet | Macko, Dominik Debnath, Alok Simko, Jakub |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_02695 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | mdok-style at SemEval-2026 Task 9: Finetuning LLMs for Multilingual Polarization Detection Macko, Dominik Debnath, Alok Simko, Jakub Computation and Language Artificial Intelligence 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. |
| title | mdok-style at SemEval-2026 Task 9: Finetuning LLMs for Multilingual Polarization Detection |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2605.02695 |