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\title{NYCU-NLP at SemEval-2026 Task 9: Stacking Small Language Models\\
for Multilingual, Multicultural and Multievent Polarization Detection}
  
\author{
\textbf{Ding-Xiang Lin}, \textbf{Po-Chun Chu}, \textbf{Lung-Hao Lee}$^{*}$
\vspace{+0.5em}\\
Institute of Artificial Intelligence Innovation\\
National Yang Ming Chiao Tung University
\vspace{+0.5em}\\
\textbf{*Contact:} \texttt{lhlee@nycu.edu.tw}
}
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\begin{abstract}
This paper presents the NYCU-NLP system for SemEval-2026 Task 9 on online polarization analysis. Our approach explores the effectiveness of instruction-tuned small language models (SLMs), including Phi-4 (14B), Mistral-small-3.2 (24B), and Gemma-3 (27B), with task-specific prompting strategies and combined them via a stacking ensemble to leverage complementary modeling capacities. Evaluated across 22 languages and three subtasks, our system achieved macro-averaged F1 scores of 0.8071 for Polarization Detection (Subtask 1), 0.6108 for Polarization Type Classification (Subtask 2), and 0.5111 for Polarization Manifestation Identification (Subtask 3). Notably, our approach ranked first in 15, second in 12, and third in 10 of the 62 language-specific leaderboards, demonstrating the robustness and competitiveness of stacking-based SLM ensembles in multilingual settings.

\end{abstract}

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\section*{Limitations}
This work does not propose a new model to address this task for online polarization analysis. Experiments were conducted with basic settings without other advanced explorations due to computational resource limitations. 

We do 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 relies solely on the original training data; incorporating data augmentation strategies could potentially yield additional gains in model performance.

\section*{Use of AI Assistants}
We used ChatGPT and Grammarly to correct grammatical errors, enhancing the coherence of the final manuscript. While these tools have augmented our capabilities and contributed to our findings, it’s pertinent to note that they have inherent limitations. We have made every effort to use AI in a transparent and responsible manner. Any conclusions drawn are a result of combined human and machine insights.

\section*{Acknowledgments}
This study was partially supported by the National Science and Technology Council, Taiwan, under the grant NSTC 114-2221-E-A49-059-MY3. This work was also financially supported by the Co-creation Platform of the Industry Academia Innovation School, NYCU.

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