@inproceedings{tan-etal-2026-mitigating,
    title = "Mitigating Cultural Bias in {LLM}s via Multi-Agent Cultural Debate",
    author = "Tan, Qian  and
      Jiang, Lei  and
      Zeng, Yuting  and
      Ding, Shuoyang  and
      Xu, Xiaohua",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.418/",
    pages = "8600--8612",
    ISBN = "979-8-89176-395-1",
    abstract = "Large language models (LLMs) exhibit systematic Western-centric bias, yet whether prompting in non-Western languages (e.g., Chinese) can mitigate this remains understudied. Answering this question requires rigorous evaluation and effective mitigation, but existing approaches fall short on both fronts: evaluation methods force outputs into predefined cultural categories without a neutral option, while mitigation relies on expensive multi-cultural corpora or agent frameworks that use functional roles (e.g., Planner{--}Critique) lacking explicit cultural representation. To address these gaps, we introduce CEBiasBench, a Chinese{--}English bilingual benchmark, and Multi-Agent Vote (MAV), which enables explicit ``no bias'' judgments. Using this framework, we find that Chinese prompting merely shifts bias toward East Asian perspectives rather than eliminating it. To mitigate such persistent bias, we propose Multi-Agent Cultural Debate (MACD), a training-free framework that assigns agents distinct cultural personas and orchestrates deliberation via a ``Seeking Common Ground while Reserving Differences'' strategy. Experiments demonstrate that MACD achieves 57.6{\%} average No Bias Rate evaluated by LLM-as-judge and 86.0{\%} evaluated by MAV (vs. 47.6{\%} and 69.0{\%} baseline using GPT-4o as backbone) on CEBiasBench and generalizes to the Arabic CAMeL benchmark, confirming that explicit cultural representation in agent frameworks is essential for cross-cultural fairness."
}