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This study exposes a critical vulnerability in large language models where surface-level phonetic similarities between Arabic and Hebrew cause them to fail at distinguishing true cognates from misleading false friends or loanwords. The findings demonstrate that current architectures rely too heavily on form-based signals, as contextual information alone proves insufficient for resolving these cross-lingual semantic conflicts. By introducing SemCog Bench, the authors provide a necessary benchmark to drive future research into robust multilingual reasoning capabilities beyond simple pattern matching.
When Similar Means Different: Evaluating LLMs on Arabic--Hebrew Cognates
arXiv:2606.13218v1 Announce Type: new Abstract: Arabic and Hebrew, as closely related Semitic languages, share a substantial lexicon of true cognates, misleading false friends, and modern loanwords. This overlap poses a challenge for cross-lingual semantic understanding in large language models (LLMs). To evaluate this capability, we introduce SemCog Bench, a curated benchmark of 1,858 Arabic--Hebrew word pairs with sentence-level annotations for cognate identification and semantic disambiguation. We evaluate open-source and commercial LLMs across multiple input representations (raw, diacritized, Romanized, and phonetic) and reveal a critical gap in cross-lingual reasoning. While models achieve high accuracy on true cognates, performance drops sharply on false friends and loanwords, reflecting a strong reliance on surface-form similarity. Furthermore, sentence-level context yields only modest improvements, suggesting that contextual cues alone are insufficient to overcome misleading form-based signals. These findings reveal a fundamental limitation of current LLMs in resolving cross-lingual form--meaning conflicts and establish SemCog Bench as a rigorous benchmark for multilingual semantic reasoning. Our code and data are publicly available.
Abstract:Arabic and Hebrew, as closely related Semitic languages, share a substantial lexicon of true cognates, misleading false friends, and modern loanwords. This overlap poses a challenge for cross-lingual semantic understanding in large language models (LLMs). To evaluate this capability, we introduce SemCog Bench, a curated benchmark of 1,858 Arabic--Hebrew word pairs with sentence-level annotations for cognate identification and semantic disambiguation. We evaluate open-source and commercial LLMs across multiple input representations (raw, diacritized, Romanized, and phonetic) and reveal a critical gap in cross-lingual reasoning. While models achieve high accuracy on true cognates, performance drops sharply on false friends and loanwords, reflecting a strong reliance on surface-form similarity. Furthermore, sentence-level context yields only modest improvements, suggesting that contextual cues alone are insufficient to overcome misleading form-based signals. These findings reveal a fundamental limitation of current LLMs in resolving cross-lingual form--meaning conflicts and establish SemCog Bench as a rigorous benchmark for multilingual semantic reasoning. Our code and data are publicly available.
Computation and Language (cs.CL)
arXiv:2606.13218 [cs.CL]
(or arXiv:2606.13218v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.13218 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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