SigJBS@LT-EDI 2026: QLoRA-Tuned Homophobic and Transphobic Counter Narrative Generation

We present our approach to LT-EDI@ACL 2026 on counter-narrative generation for ho mophobic and transphobic comments. Gener ating high-quality counter-narratives in multi lingual and low-resource settings remains chal lenging, particularly when data imbalance and script variation affect model performance. To address these issues, we explore multiple mod eling strategies built around Gemma 3 12B with QLoRA fine-tuning, including data rebalanc ing 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.