Existing approaches to multilingual text detoxification are hampered by the scarcity of parallel multilingual datasets. In this work, we introduce a pipeline for the generation of multilingual parallel detoxification data. We also introduce SynthDetoxM, a manually collected and synthetically generated multilingual parallel text detoxification dataset comprising 16,000 high-quality detoxification sentence pairs across German, French, Spanish and Russian. The data was sourced from different toxicity evaluation datasets and then rewritten with nine modern open-source LLMs in few-shot setting. Our experiments demonstrate that models trained on the produced synthetic datasets have superior performance to those trained on the human-annotated MultiParaDetox dataset even in data limited setting. Models trained on SynthDetoxM outperform all evaluated LLMs in few-shot setting. We release our dataset and code to help further research in multilingual text detoxification.
@article{arxiv.2502.06394,
title = {SynthDetoxM: Modern LLMs are Few-Shot Parallel Detoxification Data Annotators},
author = {Daniil Moskovskiy and Nikita Sushko and Sergey Pletenev and Elena Tutubalina and Alexander Panchenko},
journal= {arXiv preprint arXiv:2502.06394},
year = {2025}
}