用大模型自动生成多语言去毒文本对，效果优于人工标注数据。
SynthDetoxM: Modern LLMs are Few-Shot Parallel Detoxification Data Annotators
- 用9个开源大模型在少样本下重写毒性文本生成新数据。
- 自动生成的1.6万组数据在小样本下训练效果超人工数据。
- 适合研究多语言内容安全与合成数据生成的学者使用。
现有多种语言文本去毒方法受限于平行多语言数据稀缺。本文提出一种生成多语言平行去毒数据的流程，并构建了SynthDetoxM数据集，包含德语、法语、西班牙语和俄语共16,000条高质量去毒句对。数据源自多个毒性评估数据集，经九个现代开源大模型在少样本设置下重写生成。实验表明，基于该合成数据训练的模型，在数据有限条件下性能优于基于人工标注的MultiParaDetox数据集训练的模型；且在少样本设置下，其表现超越所有对比的大模型。论文已公开数据集与代码，以推动多语言文本去毒研究。
原文摘要 · Abstract (English)
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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