TeamV at LT-EDI 2026: Multilingual Hate Speech Span Detection and Counter-Narrative Generation via Few-Shot In-Context Learning
Year: 2026
Type: conference-paper
Abstract: This paper describes the system developed by TeamV for the LT-EDI 2026 Shared Task on Counter-Narrative Generation on Homophobic & Transphobic Comments.The shared task comprises two subtasks: (1) Hate Speech Span Detection in English, Tamil, and Hindi, and (2) Counter-Narrative Generation in English and Tamil.Our system leverages the reasoning and multilingual capabilities of a large proprietary language model (Qwen3-Max) through rigorous few-shot in-context learning (ICL) and robust post-processing mechanisms.Our submitted system demonstrated state-of-the-art performance on the official CodaBench leaderboard.In Task 1, our approach achieved 1st Place across all three languages, securing macro F1 scores of 0.5338 in English, 0.5272 in Tamil, and 0.5478 in Hindi.For Task 2, our generated counter-narratives ranked 1st globally in English with an overall average score of 87.47% and 5th in Tamil.We present our prompting methodology, robust span-matching pipeline, detailed official results, and an analysis of the model's performance across diverse languages.
Authors: Vinay Babu Ulli, Jyoti Kumari
Institution: Banaras Hindu University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: hybrid
OpenAlex ID: W7166887419