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arXiv · 2503.23088

UNITYAI-GUARD: Pioneering Toxicity Detection Across Low-Resource Indian Languages

Himanshu Beniwal·Reddybathuni Venkat·Rohit Kumar·Birudugadda Srivibhav·Daksh Jain·Pavan Doddi·Eshwar Dhande·Adithya Ananth·Kuldeep·Mayank Singh
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Abstract

This work introduces UnityAI-Guard, a framework for binary toxicity classification targeting low-resource Indian languages. While existing systems predominantly cater to high-resource languages, UnityAI-Guard addresses this critical gap by developing state-of-the-art models for identifying toxic content across diverse Brahmic/Indic scripts. Our approach achieves an impressive average F1-score of 84.23% across seven languages, leveraging a dataset of 567k training instances and 30k manually verified test instances. By advancing multilingual content moderation for linguistically diverse regions, UnityAI-Guard also provides public API access to foster broader adoption and application.

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Himanshu Beniwal, Reddybathuni Venkat, Rohit Kumar, Birudugadda Srivibhav, Daksh Jain, Pavan Doddi, Eshwar Dhande, Adithya Ananth, Kuldeep, Mayank Singh. 2025-03-29. UNITYAI-GUARD: Pioneering Toxicity Detection Across Low-Resource Indian Languages. https://arxiv.org/abs/2503.23088

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