# The Tatoxa System for Text Detoxification in Low-Resource Languages: The Case of Tatar

arXiv: https://arxiv.org/abs/2606.26015 · HTML: https://mlsanity.com/paper/2606.26015/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Safety alignment, refusal and guardrails, one of 12 directions in LLM safety and security.
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 24 Jun 2026.

## Placed in

- LLM safety and security / [Safety alignment, refusal and guardrails](https://mlsanity.com/safety/safety-alignment-refusal-and-guardrails/)

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [xCOMET: Transparent Machine Translation Evaluation through Fine-grained Error Detection](https://mlsanity.com/paper/2310.10482/) (2023)
- [MultiParaDetox: Extending Text Detoxification with Parallel Data to New Languages](https://mlsanity.com/paper/2404.02037/) (2024)
- [SynthDetoxM: Modern LLMs are Few-Shot Parallel Detoxification Data Annotators](https://mlsanity.com/paper/2502.06394/) (2025)
- [Evaluating Text Style Transfer: A Nine-Language Benchmark for Text Detoxification](https://mlsanity.com/paper/2507.15557/) (2025)
- [SmurfCat at PAN 2024 TextDetox: Alignment of Multilingual Transformers for Text Detoxification](https://mlsanity.com/paper/2407.05449/) (2024)
- [BanglaNirTox: A Large-scale Parallel Corpus for Explainable AI in Bengali Text Detoxification](https://mlsanity.com/paper/2511.01512/) (2025)

## Abstract (arXiv)

Text detoxification, the automated detection and mitigation of abusive and harmful content, is essential for ensuring the safety of online communities and protecting users. However, low resource languages such as Tatar have received little research attention. In this paper we present Tatoxa, a novel state-of-the-art system for text detoxification in the Tatar language. Comparative experiments show that the proposed approach outperforms existing open source and proprietary commercial LLMs on key quality metrics. We also introduce a new dataset for text detoxification in Tatar, designed for fine tuning and evaluation in low resource settings. Finally, cross lingual transfer experiments indicate that transfer from other languages, including the culturally close Russian, performs significantly worse than training on native Tatar data even when a large Russian corpus is available.