@inproceedings{zhao-etal-2025-detoxifying,
    title = "Detoxifying Large Language Models via the Diversity of Toxic Samples",
    author = "Zhao, Ying  and
      Guo, Yuanzhao  and
      Weng, Xuemeng  and
      Tian, Yuan  and
      Wang, Wei  and
      Chang, Yi",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.298/",
    pages = "5869--5882",
    ISBN = "979-8-89176-332-6",
    abstract = "Eliminating toxicity from Large Language Models (LLMs) is crucial for ensuring user safety. However, current methods have limitations in the analysis and utilization of toxic samples, failing to fully harness their potential. Through comparative analysis of toxic and safe samples, we discover that toxic samples exhibit diversity and, within this diversity, there lies specificity. These findings suggest that leveraging these characteristics of toxic samples could enhance the performance of algorithms in detoxifying LLMs. To this end, we propose a novel diverse detoxification framework, DivDetox, which comprises two innovative components: a Multi-Category-Induced Personalized Sample Generation (MPSG) strategy and a Scaled Contrastive DPO (SC-DPO) approach. The former is designed to elicit a variety of personalized toxic responses from the LLM, while the latter is constructed to precisely and fully utilize these toxic responses. Experiments on benchmark datasets across different model scales and different detoxification tasks verify the effectiveness of our architecture."
}