SynthDetoxM: Modern LLMs are Few-Shot Parallel Detoxification Data
  Annotators
Authors: name: Daniil Moskovskiy, name: Nikita Sushko, name: Sergey Pletenev, name: Elena Tutubalina, name: Alexander Panchenko

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.