When Similar Means Different: Evaluating LLMs on Arabic--Hebrew Cognates
A new benchmark called SemCog Bench has been introduced to evaluate large language models (LLMs) on Arabic-Hebrew cognates, consisting of 1,858 word pairs with annotations for cognate identification and semantic disambiguation. The study reveals a significant performance gap in cross-lingual reasoning, particularly with false friends and loanwords, where models struggle despite high accuracy on true cognates.
WPN Brief
- What Happened
A new benchmark called SemCog Bench has been introduced to evaluate large language models (LLMs) on Arabic-Hebrew cognates, consisting of 1,858 word pairs with annotations for cognate identification and semantic disambiguation. The study reveals a significant performance gap in cross-lingual reasoning, particularly with false friends and loanwords, where models struggle despite high accuracy on true cognates.
- Why It Matters
This development is crucial as it highlights the limitations of current LLMs in understanding the complexities of closely related languages, which can impact applications in translation, education, and cross-cultural communication.
- The Bigger Picture
The findings reflect broader challenges in the field of artificial intelligence, particularly in multilingual contexts, where models often rely on surface-level similarities rather than deeper semantic understanding, raising questions about their effectiveness in diverse linguistic environments.
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