We present our system for SemEval-2026 Task 9: Multilingual Polarization Detection, a binary classification task spanning 22 languages. Our approach fine-tunes separate Gemma~3 models (12B and 27B parameters) per language using Low-Rank Adaptation (LoRA), augmented with synthetic data generated by a large language model (LLM). We employ three synthetic data strategies (direct generation, paraphrasing, and contrastive pair creation) using GPT-4o-mini, with a multi-stage quality filtering pipeline including embedding-based deduplication. We find that per-language threshold tuning on the development set yields 2 to 4\% F1 improvements without retraining. We also use weighted ensembles of 12B and 27B model predictions with per-language strategy selection. Our final system achieves a mean macro-F1 of 0.811 across all 22 languages, ranking 2nd overall of the participating teams, with 1st place finishes in 3 languages and top-3 in 8 languages. We also find that alternative architectures (XLM-RoBERTa, Qwen3) that showed strong development set performance suffered 30 to 50\% F1 drops on the test set, highlighting the importance of generalization.

翻译：我们提出了参与SemEval-2026任务9（多语言极化检测，一项涵盖22种语言的二分类任务）的系统方案。该方法采用低秩适配（LoRA）对每个语言单独微调Gemma~3模型（参数量分别为12B和27B），并结合大型语言模型（LLM）生成的合成数据进行增强。我们利用GPT-4o-mini实施了三种合成数据策略（直接生成、释义改写和对比对构建），并设计了包含基于嵌入的去重操作的多阶段质量过滤流程。实验表明，在开发集上针对每种语言进行阈值调优可在不重新训练的情况下提升F1值2%至4%。此外，我们采用加权集成方法融合12B与27B模型的预测结果，并为每种语言选择最优策略。最终系统在全部22种语言上实现了平均宏F1值为0.811的成绩，位列参赛队伍第二名，其中在3种语言中获得第一名，在8种语言中进入前三名。我们还发现，备选架构（XLM-RoBERTa、Qwen3）虽然在开发集上表现优异，但在测试集上F1值骤降30%至50%，凸显了泛化能力的重要性。