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arXiv · 2406.17764

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

Ercong Nie·Bo Shao·Zifeng Ding·Mingyang Wang·Helmut Schmid·Hinrich Schütze
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Abstract

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. Code and data are publicly available at: https://github.com/ercong21/MultiKnow/.

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Ercong Nie, Bo Shao, Zifeng Ding, Mingyang Wang, Helmut Schmid, Hinrich Schütze. 2024-06-25. BMIKE-53: Investigating Cross-Lingual Knowledge Editing with In-Context Learning. https://arxiv.org/abs/2406.17764

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