The Elephant in the Room: Exploring the Role of Neutral Words in Language Model Group-Agnostic Debiasing (2025)

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.

## introduction from bert to gpt and deepseek @xcite @xcite , large language models (llms) have become increasingly intelligent and cost-effective, seamlessly integrating into daily life. however, their widespread use has also intensified ethical concerns, particularly regarding interactions between llms and users. as llms expand the application scenarios of language models, the risk of amplifying and propagating stereotypes by their responses grows. ## role 2: neutral words contribute to the group-agnostic nature of scm-based debiasing to further investigate why scm-based debiasing can eliminate group-agnostic stereotype, we divide the question into two sub-questions: ❶ how does scm-based debiasing eliminate stereotypes? ❷ how does scm-based debiasing achieve groupagnostic stereotype elimination? exploring the answers to the above questions is helpful in improving the interpretability of the scm debiasing method and provides better guidance for its application in stereotype elimination practice. ## related work there are several existing methods for debiasing language models, including counterfactual data augmentation (cda; @xcite , dropout @xcite , self-debias @xcite , and word embedding debiasing methods @xcite . our work closely follows the word embedding method dpce @xcite , which is more plain and more interpretable for subsequent analysis. @xcite was the first to propose scm-based group-agnostic debiasing method. originating from social psychology, stereotype content model (scm; @xcite offers valuable insights for group-agnostic debiasing. compared with dpce proposed by @xcite , we have developed the ϵ-dpce, which bridges the gap between the expected and the actual performance of debiased model. on the other hand, compared with scm-based debiasing method proposed by @xcite , we explore further by explaining both why and how scm-based debiasing achieve group-agnostic debiasing. ## conclusion in this work, we explored the role of neutral words in group-agnostic debiasing of language models from two perspectives. first, we demonstrated that incorporating a loss function grounded in neutral word semantics into dpce effectively preserved potential semantic modeling, which led to the development of ϵ-dpce. second, through our pro-posed scm-projection method, we revealed that the underlying mechanism of scm-based debiasing is repositioning the neutral words in the scm space. our analysis further showed that multigroup stereotypical objects embedded within neutral words contributed significantly to the groupagnostic properties of scm-based debiasing. overall, our findings highlight the essential role of neutral words in group-agnostic debiasing and offer valuable insights into their effective integration into bias mitigation methods.