@inproceedings{sabhahit-etal-2026-nasimlab,
    title = "{NASIML}ab at {S}em{E}val-2026 Task 9: A Comparative Analysis of Fine-Tuned Small Language Models vs. Generative Large Language Models for Multilingual Polarization Type Detection",
    author = "Sabhahit, Neel  and
      Selvaganapathy, Sanjeevan  and
      Nasim, Mehwish",
    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-1.413/",
    pages = "3316--3327",
    ISBN = "979-8-89176-414-9",
    abstract = "The POLAR dataset contains various social media texts that might be polarized (conflict-inducing or dangerously divisive). The task at hand is to identify whether any of the following types of polarization are present: political, racial/ethnic, religious, gender/sexual, and other types across 22 languages. In this paper, we propose a system of fine-tuned language-specific small language models and compare our approach with state-of-the-art large language models on the POLAR dataset. By fine-tuning models for each language, we demonstrate that fine-tuned small encoder-only models consistently outperform large language models, especially for low-resource languages. Our system performs well on this task for most low-resource languages, notably taking the top spot on the leaderboard in Burmese (mya), appearing within the top 10 for 12 languages, and within the top 20 for all remaining languages."
}