Counter Narrative (CN) generation via Large Language Models (LLMs) offers a scalable approach to combating hate speech by producing targeted responses that challenge harmful content. However, existing methods typically require costly post-training or fine-tuning to improve narrative diversity and quality. We introduce a fine-tuning-free prompt optimization technique that enhances Counter Narrative effectiveness without additional model training, making it both resource-efficient and readily deployable. We conduct extensive evaluation on hate speech datasets spanning English and Tamil, employing both reference-based metrics and rubric-based LLM-as-a-judge scoring to capture multiple dimensions of narrative quality. Experiments across multiple LLMs demonstrate that our approach consistently outperforms vanilla prompting baselines, exhibits strong transferability across models, and adapts seamlessly to new evaluation metrics—requiring no architectural or procedural changes. Our findings suggest that carefully optimized prompting strategies can match or exceed the performance of more resource-intensive approaches, offering a practical path toward scalable hate speech intervention.

The automatic generation of counter-speech (CS) is a critical strategy for addressing hate speech by providing constructive and informed responses. However, existing methods often fail to generate high-quality, impactful, and scalable CS, particularly across diverse lin- guistic contexts. In this paper, we propose a novel methodology to enhance CS generation by aligning Large Language Models (LLMs) using Supervised Fine-Tuning (SFT) and Di- rect Preference Optimization (DPO). Our ap- proach leverages DPO to align LLM outputs with human preferences, ensuring contextu- ally appropriate and linguistically adaptable responses. Additionally, we incorporate knowl- edge grounding to enhance the factual accuracy and relevance of generated CS. Experimental results demonstrate that DPO-aligned models significantly outperform SFT baselines on CS benchmarks while scaling effectively to mul- tiple languages. These findings highlight the potential of preference-based alignment tech- niques to advance CS generation across var- ied linguistic settings. The model supervision and alignment is done in English and the same model is used for reporting metrics across other languages like Basque, Italian, and Spanish.