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.

This paper presents our system for the LT-EDI@ACL 2026 workshop on meme classification of homophobia and transphobia in English, Hindi, and Chinese. Detecting harmful content in memes is challenging because meaning often emerges from the interaction between visual elements and short textual cues, particularly in multilingual settings. To address this, we build a multimodal pipeline using CLIP ViT-L/14 visual embeddings, EasyOCR text extraction, TF–IDF lexical features, and a multinomial logistic regression classifier. We further incorporate two optional expert modules, a LoRA-adapted Qwen2-VL model and a CLIP zero-shot classifier, and combine predictions using weighted majority voting. The system is intentionally lightweight and reproducible, demonstrating that strong pretrained transfer features paired with explicit OCR can provide robust multilingual meme moderation without extensive fine-tuning. On the official leaderboard, our submission ranks 1st in Hindi, 3rd in English, and 5th in Chinese.