Vivatham: a multi-agent debate framework for generating counter-narratives against homophobia and transphobia in Tamil

Abstract Social media platforms have increasingly become conduits for the dissemination of hate speech targeting the LGBTQ+ community, significantly undermining their mental health and overall well-being. This phenomenon presents a critical challenge within the field of Natural Language Processing (NLP). While hate speech detection is a well-established area of research, counter-narratives (CNs)-defined as polite, empathetic responses designed to neutralize harmful content-offer a promising alternative to traditional moderation. However, the availability of CN datasets remains severely limited for low-resource Dravidian languages, such as Tamil. In this paper, we introduce a newly curated dataset comprising 5,428 Tamil HS-CN pairs and propose VIVATHAM, a novel multi-agent persona debate framework for generating high quality Tamil counter-narratives. This dataset was developed through a human-in-the-loop (HITL) process, in which domain experts reviewed and refined initial CNs to ensure linguistic accuracy, politeness, and cultural sensitivity. Unlike conventional methods, our approach employs a system of agents with distinct personas to debate and iteratively refine responses from diverse perspectives. This debate-based mechanism facilitates the production of nuanced, non-aggressive, and contextually appropriate counter-narratives. Our results demonstrate that the multi-agent debate framework significantly outperforms standard CN generation approaches. This research contributes the first large-scale Tamil CN dataset and highlights the potential of multi-agent systems to enhance the quality and inclusiveness of online discourse. GitHub Link: https://github.com/Unit-for-Inclusive-AI/VIVATHAM .