UNITYAI-GUARD: Pioneering Toxicity Detection Across Low-Resource Indian Languages
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 11 of its 29 references that are in the map. First posted on arXiv 29 Mar 2025.
Builds on
11 of 29 references.
References · 11
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2024The Llama 3 Herd of Modelscited by 14,715
- 2023IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languagescited by 152
- 2024Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontiercited by 129
- 2024IndicLLMSuite: A Blueprint for Creating Pre-training and Fine-Tuning Datasets for Indian Languagescited by 52
- 2022Toxicity in Multilingual Machine Translation at Scalecited by 26
- 2023Offensive Language Identification in Transliterated and Code-Mixed Banglacited by 12
- 2024STAND-Guard: A Small Task-Adaptive Content Moderation Modelcited by 12
- 2024ToxVidLM: A Multimodal Framework for Toxicity Detection in Code-Mixed Videoscited by 10
- 2021indicnlp@kgp at DravidianLangTech-EACL2021: Offensive Language Identification in Dravidian Languagescited by 4
- 2023Hate Speech and Offensive Content Detection in Indo-Aryan Languages: A Battle of LSTM and Transformerscited by 4
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Citing papers per year: 2026: 3.
3 papers.