A language-adaptive combination of generalist, specialist, and ensemble transformer models achieves 0.796 macro F1 and 0.826 accuracy on multilingual polarization detection across 22 languages.
Better as Generators Than Classifiers: Leveraging LLM s and Synthetic Data for Low-Resource Multilingual Classification
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MKJ at SemEval-2026 Task 9: A Comparative Study of Generalist, Specialist, and Ensemble Strategies for Multilingual Polarization
A language-adaptive combination of generalist, specialist, and ensemble transformer models achieves 0.796 macro F1 and 0.826 accuracy on multilingual polarization detection across 22 languages.