BloomBench reveals that state-of-the-art VLMs perform well on semantic understanding but struggle with factual recall and creative synthesis, while also showing large English-Arabic performance gaps.
arXiv preprint arXiv:2603.16397 , year=
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AraSEG is a genre-diverse Arabic sentence segmentation corpus showing lightweight encoders and dependency parsers outperform LLMs under challenging punctuation while improving downstream parsing.
Using a 1PL IRT model on real cultural questions across 13 locales, the study identifies a local-language knowledge-access advantage masked by lower proficiency in raw accuracy.
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Arabic Sentence Segmentation Across Genres and Punctuation Conditions
AraSEG is a genre-diverse Arabic sentence segmentation corpus showing lightweight encoders and dependency parsers outperform LLMs under challenging punctuation while improving downstream parsing.
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The Masked Advantage: Uncovering Local-Language Access to Cultural Knowledge in LLMs
Using a 1PL IRT model on real cultural questions across 13 locales, the study identifies a local-language knowledge-access advantage masked by lower proficiency in raw accuracy.