BMIKE-53: Investigating Cross-Lingual Knowledge Editing with In-Context Learning

This paper introduces BMIKE-53, a comprehensive benchmark for cross-lingual in-context knowledge editing (IKE) across 53 languages, unifying three knowledge editing (KE) datasets: zsRE, CounterFact, and WikiFactDiff. Cross-lingual KE, which requires knowledge edited in one language to generalize across others while preserving unrelated knowledge, remains underexplored. To address this gap, we systematically evaluate IKE under zero-shot, one-shot, and few-shot setups, incorporating tailored metric-specific demonstrations. Our findings reveal that model scale and demonstration alignment critically govern cross-lingual IKE efficacy, with larger models and tailored demonstrations significantly improving performance. Linguistic properties, particularly script type, strongly influence performance variation across languages, with non-Latin languages underperforming due to issues like language confusion.

@article{nie2025_2406.17764,
  title={ BMIKE-53: Investigating Cross-Lingual Knowledge Editing with In-Context Learning },
  author={ Ercong Nie and Bo Shao and Zifeng Ding and Mingyang Wang and Helmut Schmid and Hinrich Schütze },
  journal={arXiv preprint arXiv:2406.17764},
  year={ 2025 }
}