This paper presents our system for SemEval-2026 Task~9 (POLAR), Subtask~2, which focuses on classifying polarization types in social media text. We investigate three paradigms: (i) fine-tuning mDeBERTa-v3 with domain-adaptive pre-training, (ii) parameter-efficient adaptation of Qwen2.5-32B using LoRA, and (iii) few-shot prompting with Llama-3.3-70B-Instruct. Experimental results show that few-shot prompting, despite requiring no task-specific training, outperforms both fine-tuning and parameter-efficient approaches. Notably, it achieves non-zero F1 scores across all polarization categories, which is critical under macro-averaged evaluation. Our system ranks 2nd out of 29 English submissions on the official leaderboard, achieving an F1 Macro of 0.5157. These findings highlight the effectiveness of large instruction-tuned models in low-resource, label-imbalanced classification settings.