@inproceedings{kadasi-etal-2026-lingoresearchgroup,
    title = "{L}ingo{R}esearch{G}roup at {S}em{E}val-2026 Task 9: Evaluating Prompt Variants for Polarization Detection",
    author = "Kadasi, Pritam  and
      Tiwari, Anuj  and
      Singh, Mayank",
    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-1.376/",
    pages = "2991--3001",
    ISBN = "979-8-89176-414-9",
    abstract = "Our submission presented in this paper is for SemEval-2026 Task 9: Multilingual Text Classification Challenge - Polarization Detection and it covers all three subtasks: (1) binary polarization detection, (2) polarization type classification and (3) polarization manifestation identification. We adopt a systematic approach of research on short designed prompts by considering twelve designed prompts that are different in terminology clarity, detail of the definition, guidance of reasoning and in-context examples use. The experiments are conducted using aya-101 and Gemma3-27B, with the latter chosen for the submission at the end of the development through performance considerations. Our system has an average macro level {\textbackslash}textbf{\{}F1-score of 0.762 on Subtask 1, 0.587 on Subtask 2 and 0.444 on Subtask 3{\}} with the average accuracy of 0.819, 0.678 and 0.498, respectively, on the official test set averaged among 22 languages, respectively. With cross-task and cross-lingual analysis, we demonstrate that prompt-based approaches can be used effectively to detect coarse-grained polarization but encounter more and more difficulties as far as fine-grained and multi-label sociolinguistic classification is concerned."
}