UrduMMLU is a new native-source MCQ benchmark for Urdu that reveals top LLMs reach only ~90% accuracy with large gaps on region-specific humanities content.
KMMLU : Measuring Massive Multitask Language Understanding in K orean
4 Pith papers cite this work. Polarity classification is still indexing.
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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.
Presents a Korean harm taxonomy, culturally grounded safe-response guidelines, and DPO fine-tuning that raises cultural safe rates on six open-weight LLMs with little benchmark degradation.
K-BrowseComp is a new Korean web-browsing agent benchmark where frontier LLMs score 30-46% and Korean LLMs score 0-10% on the verified subset.
citing papers explorer
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UrduMMLU: A Massive Multitask Benchmark for Urdu Language Understanding
UrduMMLU is a new native-source MCQ benchmark for Urdu that reveals top LLMs reach only ~90% accuracy with large gaps on region-specific humanities content.
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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.
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Korean Culture into LLM Alignment: Toward Cultural Coherence
Presents a Korean harm taxonomy, culturally grounded safe-response guidelines, and DPO fine-tuning that raises cultural safe rates on six open-weight LLMs with little benchmark degradation.
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K-BrowseComp: A Web Browsing Agent Benchmark Grounded in Korean Contexts
K-BrowseComp is a new Korean web-browsing agent benchmark where frontier LLMs score 30-46% and Korean LLMs score 0-10% on the verified subset.