@inproceedings{phu-thin-2026-stochastic,
    title = "Stochastic Gradient Descenders at {S}em{E}val-2026 Task 9: Few-Shot {LLM} Prompting for Polarization Type Classification",
    author = "Phu, Huynh  and
      Thin, Dang",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.129/",
    pages = "938--942",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper presents our system for SemEval-2026 Task{\textasciitilde}9 (POLAR), Subtask{\textasciitilde}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."
}