@inproceedings{nie-etal-2025-bmike,
    title = "{BMIKE}-53: Investigating Cross-Lingual Knowledge Editing with In-Context Learning",
    author = "Nie, Ercong  and
      Shao, Bo  and
      Wang, Mingyang  and
      Ding, Zifeng  and
      Schmid, Helmut  and
      Schuetze, Hinrich",
    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.798/",
    pages = "16357--16374",
    ISBN = "979-8-89176-251-0",
    abstract = "This paper introduces BMIKE-53, a comprehensive benchmark for cross-lingual in-context knowledge editing (IKE), spanning 53 languages and three KE datasets: zsRE, CounterFact, and WikiFactDiff. Cross-lingual KE, which requires knowledge edited in one language to generalize across diverse languages while preserving unrelated knowledge, remains underexplored. To address this, we systematically evaluate IKE under zero-shot, one-shot, and few-shot setups, including tailored metric-specific demonstrations. Our findings reveal that model scale and demonstration alignment critically govern cross-lingual editing efficacy, with larger models and tailored demonstrations significantly improving performance. Linguistic properties, particularly script type, strongly influence outcomes, with non-Latin languages underperforming due to issues like language confusion."
}