SynthDetoxM: Modern LLMs are Few-Shot Parallel Detoxification Data Annotators (2025.naacl-long)
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| Challenge: | Existing approaches to multilingual text detoxification are hampered by the scarcity of parallel multilingual datasets. |
| Approach: | They propose a pipeline for the generation of multilingual parallel detoxification data and a dataset for SynthDetoxM which is manually generated and rewritten with open-source LLMs. |
| Outcome: | The proposed pipeline outperforms human-annotated datasets even in data limited setting. |
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| Challenge: | Text detoxification is a textual style transfer task where a toxic text is paraphrased to the neutral register. |
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| Challenge: | Manual annotation methods, such as crowdsourcing, are costly and require intricate task design skills. |
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ParaDetox: Detoxification with Parallel Data (2022.acl-long)
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Varvara Logacheva, Daryna Dementieva, Sergey Ustyantsev, Daniil Moskovskiy, David Dale, Irina Krotova, Nikita Semenov, Alexander Panchenko
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| Challenge: | Large language models can reproduce and amplify toxic content, including hate speech, harassment, and bias. |
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| Challenge: | Prior work has shown that finetuning on specialized datasets can mitigate this behavior, and doing so in English can transfer to other languages. |
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Exploring Cross-lingual Text Detoxification with Large Multilingual Language Models. (2022.acl-srw)
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| Challenge: | Existing methods of textual style transfer are monolingual i.e. designed to work in one exact language. |
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Multilingual and Explainable Text Detoxification with Parallel Corpora (2025.coling-main)
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Preference Tuning For Toxicity Mitigation Generalizes Across Languages (2024.findings-emnlp)
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| Challenge: | Detoxifying multilingual Large Language Models (LLMs) has become crucial due to their increasing global use. |
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DetoxLLM: A Framework for Detoxification with Explanations (2024.emnlp-main)
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| Challenge: | DetoxLLM is a comprehensive end-to-end detoxification framework for toxic language. |
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