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

Daniil Moskovskiy; Nikita Sushko; Sergey Pletenev; Elena Tutubalina; Alexander Panchenko

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Paper · arXiv 2502.06394

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

Daniil Moskovskiy, Nikita Sushko, Sergey Pletenev, Elena Tutubalina, Alexander Panchenko

88 upvotesFebruary 10, 2025arXiv 预印本

AI 摘要

A pipeline generates multilingual synthetic detoxification data using LLMs, outperforming models trained on human-annotated datasets in few-shot settings.

multilingual text detoxificationparallel multilingual datasetstext detoxification datasetLLMsfew-shot settingMultiParaDetox

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.