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

Himanshu Beniwal, Reddybathuni Venkat, Rohit Kumar, Birudugadda Srivibhav, Daksh Jain +5 more
3/29/2025
cs.CLcs.AI

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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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Cite this paper

@article{beniwal2025unityaiguard,
  title  = {UNITYAI-GUARD: Pioneering Toxicity Detection Across Low-Resource Indian Languages},
  author = {Himanshu Beniwal and Reddybathuni Venkat and Rohit Kumar and Birudugadda Srivibhav and Daksh Jain and Pavan Doddi and Eshwar Dhande and Adithya Ananth and Kuldeep and Mayank Singh},
  year   = {2025},
  eprint = {2503.23088},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2503.23088v2}
}

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