When Similar Means Different: Evaluating LLMs on Arabic--Hebrew Cognates

• URL: http://arxiv.org/abs/2606.13218v1
• Date: Thu, 11 Jun 2026 11:33:09 GMT
• Title: When Similar Means Different: Evaluating LLMs on Arabic--Hebrew Cognates
• Abstract summary: Arabic and Hebrew, as closely related Semitic languages, share a substantial lexicon of true cognates, misleading false friends, and modern loanwords.<n>This overlap poses a challenge for cross-lingual semantic understanding in large language models (LLMs)<n>We introduce SemCog Bench, a benchmark of 1,858 Arabic--Hebrew word pairs with sentence-level annotations for cognate identification and semantic reasoning.
• Score: 3.5005564083285123
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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