@inproceedings{hira-etal-2026-mind,
    title = "Mind the Gap: Multilingual Divide in {LLM} Bias Detection and Reasoning",
    author = "Hira, Medha  and
      Goyal, Prachi  and
      Maheshwari, Raj  and
      Goel, Arnav",
    editor = "T.Y.S.S., Santosh  and
      Rodriguez, Juan Diego  and
      de Gibert, Ona",
    booktitle = "Proceedings of the 64th Annual Meeting 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.acl-srw.117/",
    pages = "1305--1316",
    ISBN = "979-8-89176-393-7",
    abstract = "Large Language Models (LLMs) are increasingly deployed in multilingual settings, yet most bias evaluation remains English-centric and overlooks how bias manifests within reasoning. We present a systematic study of social bias in both predictions and chain-of-thought reasoning across English, Dutch, Spanish, and Turkish using the MBBQ benchmark. We evaluate instruction-tuned, CoT-prompted, and reasoning-native models under supervised fine-tuning and preference optimization, using accuracy, F1, bias metrics, and a novel reasoning-level language drift measure. We find that (1) bias varies substantially across languages, with consistent degradation in non-English settings, (2) reasoning traces often introduce additional stereotype-driven signals beyond final outputs, and (3) English-trained debiasing methods fail to generalize reliably, with preference optimization introducing cross-lingual trade-offs. We further show that performance gains in multilingual settings are frequently driven by implicit reliance on English-centric reasoning, revealed through increased language drift. Together, our results demonstrate that multilingual fairness cannot be inferred from English performance and requires reasoning-aware, language-specific evaluation and alignment."
}