S ynth D etox M : M odern LLM s are Few-Shot Parallel Detoxification Data Annotators (2025)

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.

## introduction the proliferation of social networks and text-based internet media has highlighted the issue of online toxicity and hate speech @xcite . this phenomenon not only creates an unpleasant environment for users but also deters advertisers, potentially impacting the economic viability of these platforms @xcite . consequently, there is an urgent need for effective mechanisms to measure and mitigate toxicity in online spaces. a promising approach to addressing this challenge is text detoxification of text through paraphrasing @xcite ). text detoxification is a subtask of text style transfer (tst), which involves rewriting text while preserving its original * equal contribution. ## methodology in this section, we describe the pipeline introduced for collecting the multilingual parallel text detoxification dataset, synthdetoxm. a general illustration of our approach is shown in figure 1 . ## results additionally, following prior work @xcite , we investigate whether a two-stage fine-tuning approach-first on synthdetoxm, then on mpd (denoted as sdm + mpd)-yields further improvements. the highest sim scores for german and russian are achieved by mt0-xl trained on multipa-radetox (0.954 and 0.886, respectively), with the two-stage training approach (sdm + mpd) yielding slightly higher similarity in russian but lower in german. however, for sta, models trained on syn-thdetoxm consistently outperform multiparadetox across all languages, both when trained on the full dataset and on a similarly sized subset. models trained on synthdetoxm exhibit slightly lower fl scores, likely due to the referencedependent nature of this metric. despite this, the aggregated j metric-strongly influenced by fl-is significantly higher for models trained on side-by-side evaluation results for all languages the notation is similar to the notation from paradetox into the training process (sdm + mpd) results in a drop in j scores. to further illustrate the advantages of training on synthdetoxm, additionally, finally, we provide a side-by-side (sbs) comparison of the fine-tuned mt0-xl models to evaluate their detoxification performance. following @xcite , we employ gpt-4o as an evaluator to select the preferred detoxified outputs. the results of this comparison across german, spanish, and russian languages are summarized in figure 4 and detailed in appendix f. our sbs evaluation shows a clear preference for detoxifications produced by sdm over multipa-radetox (mpd), with sdm winning in 59% of cases compared to 19% for mpd, and 22% resulting in ties. the subset of sdm also outperforms mpd in 58% of cases. when comparing sdm against its combination with mpd (sdm + mpd), sdm is preferred in 47% of cases, with 21% favoring sdm + mpd, and 33% tied. additionally, the full sdm dataset is slightly preferred over the batch processing version in 55% of cases, with 35% ties. see appendix f for more details. ## conclusions we present several contributions to multilingual text detoxification technology. firstly, we success-fully extend the concept of few-shot prompting for detoxification to a multilingual context, building upon previous monolingual approaches and propose a framework for generation of multilingual synthetic detoxification data. secondly, we introduce synthdetoxm, a large-scale multilingual synthetic parallel dataset designed to address the long-standing issue of data scarcity in text detoxification research. notably, our dataset, created using our selection criteria, demonstrates competitive quality to existing human-annotated datasets, surpassing them in both low resource and high resource settings. our comprehensive evaluation of synthdetoxm reveals its effectiveness in training high-performing models for text detoxification. specifically, our experiments show that models trained on our dataset outperform those, which were trained on a similar amount of human-annotated data. furthermore, training a detoxification encoder-decoder model on full synthdetoxm yields a model, which surpasses the performance of most large language models in few-shot generation setups. findings presented in our work, show usefulness of the generated data for the task of multilingual text detoxification and pave the way for future research and developments of related technologies.