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arXiv · 2502.06394

SynthDetoxM: Modern LLMs are Few-Shot Parallel Detoxification Data Annotators

Daniil Moskovskiy·Nikita Sushko·Sergey Pletenev·Elena Tutubalina·Alexander Panchenko
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Abstract

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.

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Daniil Moskovskiy, Nikita Sushko, Sergey Pletenev, Elena Tutubalina, Alexander Panchenko. 2025-02-10. SynthDetoxM: Modern LLMs are Few-Shot Parallel Detoxification Data Annotators. https://doi.org/10.18653/v1%2F2025.naacl-long.294

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