标题： 文本净化：数据效率、语义保留和模型泛化 显示英文标题

标题： Text Detoxification: Data Efficiency, Semantic Preservation and Model Generalization

摘要： 社交媒体上有毒内容的广泛传播对在线环境和公共讨论构成了严重威胁，凸显了迫切需要有效的净化方法，在去除毒性的同时保留原始语义。 然而，现有方法通常难以同时实现强大的净化性能、语义保留和对分布外数据的鲁棒性。 此外，它们通常依赖于成本高昂、手动标注的平行语料库，同时表现出较差的数据效率。 为了解决这些挑战，我们提出了一种两阶段训练框架，联合优化数据效率、语义保留和模型泛化能力。 我们首先在一小部分高质量、过滤后的平行数据上进行监督微调，以建立强大的初始化。 然后，我们利用未标记的有毒输入和自定义设计的奖励模型，使用组相对策略优化来训练大语言模型。 实验结果表明，我们的方法有效缓解了之前工作面临的权衡，实现了最先进的性能，提高了泛化能力，并显著降低了对标注数据的依赖。 我们的代码可在以下地址获取：https://github.com/allacnobug/Detoxification-of-Text. 显示英文摘要

摘要： The widespread dissemination of toxic content on social media poses a serious threat to both online environments and public discourse, highlighting the urgent need for detoxification methods that effectively remove toxicity while preserving the original semantics. However, existing approaches often struggle to simultaneously achieve strong detoxification performance, semantic preservation, and robustness to out-of-distribution data. Moreover, they typically rely on costly, manually annotated parallel corpora while showing poor data efficiency. To address these challenges, we propose a two-stage training framework that jointly optimizes for data efficiency, semantic preservation, and model generalization. We first perform supervised fine-tuning on a small set of high-quality, filtered parallel data to establish a strong initialization. Then, we leverage unlabeled toxic inputs and a custom-designed reward model to train the LLM using Group Relative Policy Optimization. Experimental results demonstrate that our method effectively mitigates the trade-offs faced by previous work, achieving state-of-the-art performance with improved generalization and significantly reduced dependence on annotated data. Our code is available at: https://github.com/allacnobug/Detoxification-of-Text.