AINeutralarXiv – CS AI · 7h ago6/10
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PSK at SemEval-2026 Task 9: Multilingual Polarization Detection Using Ensemble Gemma Models with Synthetic Data Augmentation
Researchers achieved second place in SemEval-2026's multilingual polarization detection task by fine-tuning Gemma models with synthetic data augmentation across 22 languages. Their ensemble approach combining LoRA-adapted 12B and 27B parameter models with LLM-generated training data achieved a mean macro-F1 of 0.811, demonstrating the effectiveness of synthetic data strategies and per-language optimization for multilingual NLP tasks.
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