@inproceedings{kaneko-etal-2026-multilingual,
    title = "A Multilingual Social Bias Benchmark Incorporating Thinking Processes",
    author = "Kaneko, Masahiro  and
      Bollegala, Danushka  and
      Baldwin, Timothy",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.2204/",
    pages = "47726--47741",
    ISBN = "979-8-89176-390-6",
    abstract = "Large Language Models (LLMs) can learn both useful knowledge and harmful stereotypes, making bias evaluation essential.Existing frameworks fall into two types: those considering reasoning steps (Thinking Process-Aware Evaluation, TPAE) and those focusing only on final outputs (Straight-to-the-Answer Evaluation, SAE).Prior TPAE studies showed effectiveness in assessing gender bias but relied on template-based, word-counting prompts, limiting generalization to other bias types, languages, and reasoning-based methods.In this study, we introduce MBTP, a multilingual social bias benchmark that incorporates human-generated pro- and anti-stereotype reasoning as part of the thinking process, and propose a few-shot meta-evaluation method that enables scalable bias assessment without model fine-tuning.From experiments evaluating 13 social bias categories across 8 languages, we find that human-generated thinking consistently yields higher-quality evaluations than LLM-generated or template-based approaches.Furthermore, TPAE demonstrates superior performance over SAE, highlighting the importance of considering reasoning processes in bias evaluation.We will release the MBTP dataset upon paper acceptance."
}