SynthDetoxM: Modern LLMs are Few-Shot Parallel Detoxification Data
  Annotators
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
claimed_verified
2025-02-11T07:54:17.448Z
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Daniil Moskovskiy
user
etomoscow
67aafead3711ca5b760f324d
Nikita Sushko
claimed_verified
2025-02-11T07:54:21.453Z
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Nikita Sushko
user
chameleon-lizard
67aafead3711ca5b760f324e
Sergey Pletenev
claimed_verified
2025-02-11T09:59:47.063Z
5dfa8e07da6d0311fd3d5430
https://cdn-avatars.huggingface.co/v1/production/uploads/1651090418656-5dfa8e07da6d0311fd3d5430.png
Sergey Pletenev
user
memyprokotow
67aafead3711ca5b760f324f
Elena Tutubalina
claimed_verified
2025-02-11T09:59:50.003Z
662f8d645c4db70c77a203b0
/avatars/72f9a3c39b3ba5114388d16a35524835.svg
Elena Tutubalina
user
tlenusik
67aafead3711ca5b760f3250
Alexander Panchenko
admin_assigned
2025-02-11T15:54:34.688Z
605473729d7c1d4d81b7e52b
https://cdn-avatars.huggingface.co/v1/production/uploads/1662046050710-605473729d7c1d4d81b7e52b.jpeg
Alexander Panchenko
user
apanc
SynthDetoxM: Modern LLMs are Few-Shot Parallel Detoxification Data
  Annotators
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.
A pipeline generates multilingual synthetic detoxification data using LLMs, outperforming models trained on human-annotated datasets in few-shot settings.
multilingual text detoxification parallel multilingual datasets text detoxification dataset LLMs few-shot setting MultiParaDetox