@inproceedings{sinha-etal-2026-sigjbs-lt,
    title = "{S}ig{JBS}@{LT}-{EDI} 2026: {QL}o{RA}-Tuned Homophobic and Transphobic Counter Narrative Generation",
    author = "Sinha, Gaurangi  and
      Palacharla, Rajarajeswari  and
      Jagadeeshan, Manoj Balaji",
    editor = "Chakravarthi, Bharathi Raja  and
      B, Bharathi  and
      Buitelaar, Paul  and
      Thenmozhi, Durairaj  and
      Garc{\'i}a Cumbreras, Miguel {\'A}ngel  and
      Jim{\'e}nez Zafra, Salud Mar{\'i}a",
    booktitle = "Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion",
    month = jul,
    year = "2026",
    address = "Virtual (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.ltedi-1.29/",
    pages = "234--238",
    ISBN = "979-8-89176-424-8",
    abstract = "We present our approach to LT-EDI@ACL 2026 on counter-narrative generation for homophobic and transphobic comments. Generating high-quality counter-narratives in multilingual and low-resource settings remains challenging, particularly when data imbalance and script variation affect model performance. To address these issues, we explore multiple modeling strategies built around Gemma 3 12B with QLoRA fine-tuning, including data rebalancing and alternative input strategies for Tamil. Our findings show that task-specific fine-tuning combined with native-script Tamil produces more stable and higher-quality outputs than large few-shot prompts or transliteration-basedinputs. On the official leaderboard, our system ranks second in English with an overall score of 86.35{\%} and sixth in Tamil with 63.77{\%},highlighting both the effectiveness of targeted fine-tuning and the challenges of low-resource counter-narrative generation."
}