@inproceedings{gallegos-etal-2025-self,
    title = "Self-Debiasing Large Language Models: Zero-Shot Recognition and Reduction of Stereotypes",
    author = "Gallegos, Isabel O.  and
      Aponte, Ryan  and
      Rossi, Ryan A.  and
      Barrow, Joe  and
      Tanjim, Mehrab  and
      Yu, Tong  and
      Deilamsalehy, Hanieh  and
      Zhang, Ruiyi  and
      Kim, Sungchul  and
      Dernoncourt, Franck  and
      Lipka, Nedim  and
      Owens, Deonna  and
      Gu, Jiuxiang",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.naacl-short.74/",
    pages = "873--888",
    ISBN = "979-8-89176-190-2",
    abstract = "Large language models (LLMs) have shown remarkable advances in language generation and understanding but are also prone to exhibiting harmful social biases. While recognition of these behaviors has generated an abundance of bias mitigation techniques, most require modifications to the training data, model parameters, or decoding strategy, which may be infeasible without access to a trainable model. In this work, we leverage the zero-shot capabilities of LLMs to reduce stereotyping in a technique we introduce as zero-shot self-debiasing. With two approaches, self-debiasing via explanation and self-debiasing via reprompting, we show that self-debiasing can significantly reduce the degree of stereotyping across nine different social groups while relying only on the LLM itself and a simple prompt, with explanations correctly identifying invalid assumptions and reprompting delivering the greatest reductions in bias. We hope this work opens inquiry into other zero-shot techniques for bias mitigation."
}