@inproceedings{hyeonsu-etal-2025-dapi,
    title = "{DAPI}: Domain Adaptive Toxicity Probe Vector Intervention, for Fine-Grained Detoxification",
    author = "Hyeonsu, Cho  and
      Kim, Dooyoung  and
      Ko, Youngjoong",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.findings-acl.779/",
    pages = "15059--15069",
    ISBN = "979-8-89176-256-5",
    abstract = "There have been attempts to utilize linear probe for detoxification, with existing studies relying on a single toxicity probe vector to reduce toxicity. However, toxicity can be fine-grained into various subcategories, making it difficult to remove certain types of toxicity by using a single toxicity probe vector. To address this limitation, we propose a category-specific toxicity probe vector approach. First, we train multiple toxicity probe vectors for different toxicity categories. During generation, we dynamically select the most relevant toxicity probe vector based on the current context. Finally, the selected vector is dynamically scaled and subtracted from model. Our method successfully mitigated toxicity from categories that the single probe vector approach failed to detoxify. Experiments demonstrate that our approach achieves up to a 78.52{\%} reduction in toxicity on the evaluation dataset, while fluency remains nearly unchanged, with only a 0.052{\%} drop compared to the unsteered model."
}