mdok-style at SemEval-2026 Task 9: Finetuning LLMs for Multilingual Polarization Detection

• URL: http://arxiv.org/abs/2605.02695v1
• Date: Mon, 04 May 2026 15:08:24 GMT
• Title: mdok-style at SemEval-2026 Task 9: Finetuning LLMs for Multilingual Polarization Detection
• Abstract summary: SemEval-2026 Task 9 is focused on multilingual polarization detection.<n>It covers the identification of multilingual, multicultural and multievent polarization along three axes, namely detection, type, and manifestation.<n>We have coped with this SemEval task by finetuning mid-size LLMs for the sequence-classification task using the QLoRA parameter-efficient finetuning technique.<n>The training data augmented the multilingual (22 languages) training sets by anonymized, lower-cased, upper-cased, and homoglyphied counterparts, making the detection more robust.
• Score: 5.3677118215189585
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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