@inproceedings{zhang-etal-2025-genderalign,
    title = "{G}ender{A}lign: An Alignment Dataset for Mitigating Gender Bias in Large Language Models",
    author = "Zhang, Tao  and
      Zeng, Ziqian  and
      YuxiangXiao, YuxiangXiao  and
      Zhuang, Huiping  and
      Chen, Cen  and
      Foulds, James R.  and
      Pan, Shimei",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.553/",
    pages = "11293--11311",
    ISBN = "979-8-89176-251-0",
    abstract = "Large Language Models (LLMs) are prone to generating content that exhibits gender biases, raising significant ethical concerns. Alignment, the process of fine-tuning LLMs to better align with desired behaviors, is recognized as an effective approach to mitigate gender biases. Although proprietary LLMs have made significant strides in mitigating gender bias, their alignment datasets are not publicly available. The commonly used and publicly available alignment dataset, HH-RLHF, still exhibits gender bias to some extent. There is a lack of publicly available alignment datasets specifically designed to address gender bias. Hence, we developed a new dataset named GenderAlign, aiming at mitigating a comprehensive set of gender biases in LLMs. This dataset comprises 8k single-turn dialogues, each paired with a ``chosen'' and a ``rejected'' response. Compared to the ``rejected'' responses, the ``chosen'' responses demonstrate lower levels of gender bias and higher quality. Furthermore, we categorized the gender biases in the ``rejected'' responses of GenderAlign into 4 principal categories. The experimental results show the effectiveness of GenderAlign in reducing gender bias in LLMs."
}