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        <title>When Similar Means Different: Evaluating LLMs on Arabic–Hebrew Cognates</title>
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    <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>
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%0 Conference Proceedings
%T When Similar Means Different: Evaluating LLMs on Arabic–Hebrew Cognates
%A Liang, Junhong
%A Abo Mokh, Noor
%A Alhafni, Bashar
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F liang-etal-2026-similar
%X 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.
%U https://aclanthology.org/2026.emnlp-main.424/
%P 9709-9741

Markdown (Informal)

[When Similar Means Different: Evaluating LLMs on Arabic–Hebrew Cognates](https://aclanthology.org/2026.emnlp-main.424/) (Liang et al., EMNLP 2026)

• When Similar Means Different: Evaluating LLMs on Arabic–Hebrew Cognates (Liang et al., EMNLP 2026)

ACL

• Junhong Liang, Noor Abo Mokh, and Bashar Alhafni. 2026. When Similar Means Different: Evaluating LLMs on Arabic–Hebrew Cognates. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 9709–9741, Budapest, Hungary. Association for Computational Linguistics.