@inproceedings{mallik-dhar-2026-cuet,
    title = "{CUET}-823 at {S}em{E}val-2026 Task 9: {L}o{RA}-Based Instruction Fine-Tuning of {LLM}s vs. Transformer Models for {B}engali Polarization Detection",
    author = "Mallik, Arpita  and
      Dhar, Ratnajit",
    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-1.154/",
    pages = "1128--1135",
    ISBN = "979-8-89176-414-9",
    abstract = "The rapid growth of social media has gone hand in hand with a sharp increase in heated public discussions, where debates about elections, conflicts, protests, and identity often turn into divisive and polarized rhetoric. In this paper, we present our system for SemEval 2026 Task 9 {--} Subtask 1: Multilingual Text Classification Challenge-Polarization Detection, focusing specifically on the Bengali language. The task is a binary classification problem aimed at determining whether a social media post exhibits attitude polarization, such as intolerance, dehumanization, deindividuation, vilification, or stereotyping toward others' opinions, identities, or beliefs. Among 49 participating teams, our approach ranked 2nd, achieving a macro-F1 score of 0.8582. We experimented with both transformer-based models and large language models (LLMs), and observed that LoRA-based instruction fine-tuned LLM-based approaches delivered the strongest performance in detecting nuanced and context-dependent polarization in Bengali text."
}