\section{Evaluation}

\subsection{Datasets}
The datasets were provided by the task organizers \cite{naseem2026-POLAR} and partitioned into mutually exclusive training, development, and test sets. Table 1 summarizes the data statistics. Subtasks 1 and 2 shared identical textual instances across 22 languages. In contrast, Subtask 3 covers 18 of these languages, while retaining the same instances for the overlapping languages. Overall, the dataset comprises 73,681 instances for model training, 3,687 for validation, and 33,288 for final evaluation.

\subsection{Settings}
We obtained pre-trained models from HuggingFace\footnote{%
\begin{minipage}[t]{0.95\linewidth}\raggedright
\href{https://huggingface.co/microsoft/phi-4}{https://huggingface.co/microsoft/phi-4}\\
\href{https://huggingface.co/mistralai/Mistral-Small-3.2-24B-Instruct-2506}{https://huggingface.co/mistralai/Mistral-Small-3.2-24B}\\
\href{https://huggingface.co/google/gemma-3-27b-it}{https://huggingface.co/google/gemma-3-27b-it}
\end{minipage}%
} and further fine-tuned them using only the training data for each subtask in a multilingual setting. All experiments were conducted on a server with two Nvidia H100 GPUs (Total 160GB memory). The hyperparameters for the SLMs were configured as follows: training epochs of 3; batch size of 12; Paged AdamW (32 bit) optimizer; learning rate of 1e-4; LoRA rank (r) of 8; LoRA alpha of 32 and LoRA dropout of 0.05. 

Task organizers provided baseline results by fine-tuning LaBSE \cite{LaBSE} using the training set for each language for all three subtasks. Each participating team was permitted to submit up to five runs. For all three subtasks, model performance was evaluated using the macro-averaged F1 score. The unofficial rankings are based on the final submissions of all registered teams. The official rankings, to be reported in the task description paper, will include only those teams that submit a corresponding system description paper.

\subsection{Results}
Table 2 presents the macro-averaged F1 scores on the development set for each language. For Subtask 1 (binary classification task), neither the individual SLMs nor the proposed ensemble consistently outperformed the others across all languages. For Subtask 2, the stacking ensemble of the three SLMs slightly outperformed Gemma-3 (27B). For Subtask 3, the proposed stacking ensemble clearly surpassed the individual SLMs, demonstrating the effectiveness of ensemble learning in more challenging multi-label classification scenarios.

Table 3 presents the macro-averaged F1 scores on the test set for each language. While similar trends are observed, the proposed ensemble underperformed the baseline on several low-resource languages, including Odia, Persian, Hausa, Khmer, and Telugu. Overall, the stacking ensemble achieved macro-averaged F1 scores of 0.8071, 0.6108, and 0.5111 across all languages for Subtasks 1, 2, and 3, respectively. 

The official rankings released by the task organizers for each language-specific leaderboard are also summarized in Table 3. We submitted the stacking-based SLM ensemble as our final system for all subtasks. Our system ranked first in 15, second in 12, and third in 10 of 62 language-specific evaluations, demonstrating the robustness and competitiveness of stacking-based SLM ensembles in multilingual contexts.


\input{Table/Table3-Lang-Rank}



\subsection{Discussion}

We evaluated several SLMs that have shown strong performance on public leaderboards, including Vicuna-1.5 (13B) \cite{Vicuna-13b-v1.5}, Llama-2 (13B) \cite{Llama2}, Ling-mini-2.0 (16B) \cite{Ling-mini-beta}, Reka-Flash-3.1 (21B) \cite{reka-flash-3.1}, OLMo-2 (32B) \cite{olmo20252}, Yi-1.5 (34B) \cite{Yi2025}, and Qwen-3 (30B) \cite{Qwen3}. Based on this comparison, we identified Phi-4 (14B), Mistral-small-3.2 (24B), and Gemma-3 (27B) as the best-performing models and adopted them as our primary SLMs for polarization detection.

%We evaluated several SLMs that have shown strong performance on public leaderboards, including Vicuna-1.5 (13B) \footnote{\href{https://huggingface.co/lmsys/vicuna-13b-v1.5}{https://huggingface.co/lmsys/Vicuna-13b-v1.5}} \cite{Vicuna-13b-v1.5}, Llama-2 (13B)\footnote{\href{https://huggingface.co/meta-llama/Llama-2-13b-chat-hf}{https://huggingface.co/meta-llama/Llama-2-13b-chat}} \cite{Llama2}, Ling-mini-2.0 (16B)\footnote{\href{https://huggingface.co/inclusionAI/Ling-mini-2.0}{https://huggingface.co/inclusionAI/Ling-mini-2.0}} \cite{Ling-mini-beta}, Reka-Flash-3.1 (21B)\footnote{\href{https://huggingface.co/RekaAI/reka-flash-3.1}{https://huggingface.co/RekaAI/Reka-flash-3.1}} \cite{reka-flash-3.1}, OLMo-2 (32B)\footnote{\href{https://huggingface.co/allenai/OLMo-2-0325-32B-Instruct}{https://huggingface.co/allenai/OLMo-2-32B-Instruct}} \cite{olmo20252}, Yi-1.5 (34B)\footnote{\href{https://huggingface.co/01-ai/Yi-1.5-34B-Chat}{https://huggingface.co/01-ai/Yi-1.5-34B-Chat}} \cite{Yi2025}, and Qwen-3 (30B)\footnote{\href{https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507}{https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct}} \cite{Qwen3}. Based on this comparison, we identified Phi-4 (14B), Mistral-small-3.2 (24B), and Gemma-3 (27B) as the best-performing models and adopted them as our primary SLMs for polarization detection. 

We further compared several ensemble strategies. Ultimately, we adopted a stacking ensemble as our assembly approach, as it consistently outperformed both the Simple Majority Voting Ensemble (SMVE) and the Weighted Majority Voting Ensemble (WMVE) \cite{weighted-ensemble} baselines on the development set.

Existing SLMs are typically pretrained on multilingual corpora. By leveraging all available training instances across languages to fine-tune a unified SLM, we consistently achieved better performance than training separate language-specific models. These results indicate that unified fine-tuning more effectively harnesses the benefits of multilingual pretraining and promotes cross-lingual knowledge transfer.

%We did not apply additional prompt engineering techniques to further optimize the prompts. Consequently, the instruction fine-tuning prompts may benefit from further refinement to achieve improved performance. Moreover, our instruction tuning relied solely on the original training data; incorporating data augmentation strategies could potentially yield additional gains in model performance.