@inproceedings{ulli-kumari-2026-teamv,
    title = "{T}eam{V} at {LT}-{EDI} 2026: Multilingual Hate Speech Span Detection and Counter-Narrative Generation via Few-Shot In-Context Learning",
    author = "Ulli, Vinay Babu  and
      Kumari, Jyoti",
    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-1.30/",
    pages = "239--243",
    ISBN = "979-8-89176-424-8",
    abstract = "This paper describes the system developed byTeamV for the LT-EDI 2026 Shared Task onCounter-Narrative Generation on Homophobic Transphobic Comments. The shared taskcomprises two subtasks: (1) Hate Speech SpanDetection in English, Tamil, and Hindi, and (2)Counter-Narrative Generation in English andTamil. Our system leverages the reasoning andmultilingual capabilities of a large proprietarylanguage model (Qwen3-Max) through rigor-ous few-shot in-context learning (ICL) and ro-bust post-processing mechanisms. Our submit-ted system demonstrated state-of-the-art perfor-mance on the official CodaBench leaderboard.In Task 1, our approach achieved 1st Placeacross all three languages, securing macro F1scores of 0.5338 in English, 0.5272 in Tamil,and 0.5478 in Hindi. For Task 2, our generatedcounter-narratives ranked 1st globally in En-glish with an overall average score of 87.47{\%}and 5th in Tamil. We present our promptingmethodology, robust span-matching pipeline,detailed official results, and an analysis of themodel{'}s performance across diverse languages."
}