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This study introduces SemCog Bench, a new benchmark designed to test large language models on distinguishing between true cognates, false friends, and loanwords in Arabic-Hebrew pairs. The results highlight significant limitations in current LLMs regarding cross-lingual form-meaning reasoning, showing that models often rely too heavily on surface-form similarity rather than deep semantic understanding. This work establishes a critical baseline for improving multilingual capabilities and addressing ambiguity in low-resource or morphologically complex language pairs.
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When Similar Means Different: Evaluating LLMs on Arabic--Hebrew Cognates

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2606.13218v2 Announce Type: replace Abstract: Arabic and Hebrew, as closely related Semitic languages, share many words with similar surface forms, including true cognates, false friends, and modern loanwords. These lexical relationships create ambiguity for cross-lingual semantic interpretation, as similar forms may correspond to shared, divergent, or borrowed meanings. To evaluate whether LLMs can make these distinctions, 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 a diverse set of open-source and proprietary LLMs across multiple input representations and contextual settings. Our results show reliance on surface-form similarity, with weaker performance on false friends and wide variation on loanwords. We further find that context and scale yield model-dependent gains, while original-script inputs generally perform best. Our findings reveal limitations in cross-lingual form-meaning reasoning and establish SemCog Bench as a benchmark for cross-lingual lexical semantics. Our code and data are publicly available.

Abstract:Arabic and Hebrew, as closely related Semitic languages, share many words with similar surface forms, including true cognates, false friends, and modern loanwords. These lexical relationships create ambiguity for cross-lingual semantic interpretation, as similar forms may correspond to shared, divergent, or borrowed meanings. To evaluate whether LLMs can make these distinctions, 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 a diverse set of open-source and proprietary LLMs across multiple input representations and contextual settings. Our results show reliance on surface-form similarity, with weaker performance on false friends and wide variation on loanwords. We further find that context and scale yield model-dependent gains, while original-script inputs generally perform best. Our findings reveal limitations in cross-lingual form-meaning reasoning and establish SemCog Bench as a benchmark for cross-lingual lexical semantics. Our code and data are publicly available.

Computation and Language (cs.CL)

arXiv:2606.13218 [cs.CL]

(or arXiv:2606.13218v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.13218 Focus to learn more arXiv-issued DOI via DataCite

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