Year: 2026
Type: conference-paper
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-based inputs.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.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: hybrid
OpenAlex ID: W7166875770