RspectNLP@LT-EDI 2026: Rubric-Driven Prompting for Safe Multilingual Counter Narrative Generation

The problem of harmful online discourse against the LGBTQ+ community is still a con cern on social media platforms. Although hate speech detection is a well-explored area, the task of constructive counter-narrative genera tion is still an emerging field of research, es pecially in the multilingual and low-resource settings. Counter-narratives are designed to counter harmful discourse with respectful and empathetic responses, as opposed to mere content deletion. In this paper, the model proposes a zero-shot multilingual system for counter-narrative generation in English and Tamil. The proposed system employs the pretrained google/flan-t5-base transformer model guided by rubric-aligned prompts to en courage politeness, contextual relevance, and non-toxic response generation. The system operates in a zero-shot setting without task specific fine-tuning and uses beam search de coding for controlled response generation. On the English test data, the system scored an over all score of 70.33 per cent with a contextual coherence score of 81.82 per cent. On the Tamil test data, the system scored an overall score of 33.57 per cent with significantly lower scores on coherence and quality. These find ings indicate that structured prompting can fa cilitate safe and coherent generation in English, but also underscore the challenges of zero-shot multilingual models in low-resource language scenarios.