@inproceedings{xiao-etal-2026-jiraibench,
    title = "{J}irai{B}ench: A Bilingual Benchmark for Evaluating Large Language Models' Detection of Human risky health behavior Content in Jirai Community",
    author = "Xiao, Yunze  and
      He, Tingyu  and
      Wang, Lionel Z.  and
      Ma, Yiming  and
      Song, Xingyu  and
      Xu, Xiaohang  and
      Diab, Mona T.  and
      Li, Irene  and
      Ng, Ka Chung",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.23/",
    pages = "501--517",
    ISBN = "979-8-89176-380-7",
    abstract = "In this paper, we present the first cross-lingual dataset that captures a transnational cultural phenomenon, focusing on the Chinese and Japanese ``Jirai'' subculture and its association with risky health behaviors. Our dataset of more than 15,000 annotated social media posts forms the core of JiraiBench, a benchmark designed to evaluate LLMs on culturally specific content. This unique resource allowed us to uncover an unexpected cross-cultural transfer in which Japanese prompts better handle Chinese content, indicating that cultural context can be more influential than linguistic similarity. Further evidence suggests potential cross-lingual knowledge transfer in fine-tuned models. This work proves the indispensable role of developing culturally informed, cross-lingual datasets for creating effective content moderation tools that can protect vulnerable communities across linguistic borders."
}