title: SynthDetoxM: Modern LLMs are Few-Shot Parallel Detoxification Data
    Annotators
summary: 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.
paper_authors._id: 67aafead3711ca5b760f324c
paper_authors.name: Daniil Moskovskiy
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-11T07:54:17.448Z
paper_authors._id: 61ade264f602880813dbe10b
paper_authors.avatarUrl: /avatars/a92dea7d853bbabbf60b351c207b6875.svg
paper_authors.fullname: Daniil Moskovskiy
paper_authors.type: user
paper_authors.user: etomoscow
paper_authors._id: 67aafead3711ca5b760f324d
paper_authors.name: Nikita Sushko
paper_authors.status: claimed_verified
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paper_authors.fullname: Nikita Sushko
paper_authors.type: user
paper_authors.user: chameleon-lizard
paper_authors._id: 67aafead3711ca5b760f324e
paper_authors.name: Sergey Pletenev
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-11T09:59:47.063Z
paper_authors._id: 5dfa8e07da6d0311fd3d5430
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1651090418656-5dfa8e07da6d0311fd3d5430.png
paper_authors.fullname: Sergey Pletenev
paper_authors.type: user
paper_authors.user: memyprokotow
paper_authors._id: 67aafead3711ca5b760f324f
paper_authors.name: Elena Tutubalina
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-11T09:59:50.003Z
paper_authors._id: 662f8d645c4db70c77a203b0
paper_authors.avatarUrl: /avatars/72f9a3c39b3ba5114388d16a35524835.svg
paper_authors.fullname: Elena Tutubalina
paper_authors.type: user
paper_authors.user: tlenusik
paper_authors._id: 67aafead3711ca5b760f3250
paper_authors.name: Alexander Panchenko
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-11T15:54:34.688Z
paper_authors._id: 605473729d7c1d4d81b7e52b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1662046050710-605473729d7c1d4d81b7e52b.jpeg
paper_authors.fullname: Alexander Panchenko
paper_authors.type: user
paper_authors.user: apanc
paper_title: SynthDetoxM: Modern LLMs are Few-Shot Parallel Detoxification Data
    Annotators
paper_summary: 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.
paper_ai_summary: A pipeline generates multilingual synthetic detoxification data using LLMs, outperforming models trained on human-annotated datasets in few-shot settings.
paper_ai_keywords: multilingual text detoxification parallel multilingual datasets text detoxification dataset LLMs few-shot setting MultiParaDetox