@inproceedings{guo-etal-2025-elephant,
    title = "The Elephant in the Room: Exploring the Role of Neutral Words in Language Model Group-Agnostic Debiasing",
    author = "Guo, Xinwei  and
      Gao, Jiashi  and
      Zhou, Junlei  and
      Zhang, Jiaxin  and
      Chen, Guanhua  and
      Zhao, Xiangyu  and
      Liu, Quanying  and
      Wu, Haiyan  and
      Yao, Xin  and
      Wei, Xuetao",
    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/ingestion-acl-25/2025.findings-acl.1044/",
    pages = "20360--20371",
    ISBN = "979-8-89176-256-5",
    abstract = "Large Language Models (LLMs) are increasingly integrated into our daily lives, raising significant ethical concerns, especially about perpetuating stereotypes.While group-specific debiasing methods have made progress, they often fail to address multiple biases simultaneously. In contrast, group-agnostic debiasing has the potential to mitigate a variety of biases at once, but remains underexplored.In this work, we investigate the role of neutral words{---}the group-agnostic component{---}in enhancing the group-agnostic debiasing process. We first reveal that neutral words are essential for preserving semantic modeling, and we propose $\epsilon$-DPCE, a method that incorporates a neutral word semantics-based loss function to effectively alleviate the deterioration of the Language Modeling Score (LMS) during the debiasing process. Furthermore, by introducing the SCM-Projection method, we demonstrate that SCM-based debiasing eliminates stereotypes by indirectly disrupting the association between attribute and neutral words in the Stereotype Content Model (SCM) space. Our experiments show that neutral words, which often embed multi-group stereotypical objects, play a key role in contributing to the group-agnostic nature of SCM-based debiasing."
}