aiqbench analysis
KMMMU introduces a native Korean benchmark comprising over 3,400 questions across nine disciplines to evaluate multimodal models on local conventions and expert-level reasoning rather than just translation accuracy. The study reveals significant performance gaps between open-weight and proprietary models, highlighting specific failure modes in domain-specific standards and symbolic induction that current evaluations often miss. This work is essential for advancing fair and linguistically diverse assessment of AI capabilities beyond English-centric datasets.
KMMMU: Evaluation of Massive Multi-discipline Multimodal Understanding in Korean Language and Context
arXiv:2604.13058v3 Announce Type: replace Abstract: Multimodal evaluation is strengthened when benchmarks reflect the languages, institutions, and disciplinary conventions that shape expert visual reasoning. We introduce KMMMU, a native Korean benchmark built from Korean-native examination materials. KMMMU contains 3,466 questions spanning nine disciplines and nine visual modality categories. For targeted evaluation on challenging questions and Korean-specific knowledge, we additionally provide KMMMU-Adv and KMMMU-Local, respectively. Experiments show that the strongest evaluated open-weight model reaches 42.05% on the KMMMU, while the best evaluated proprietary model achieves 52.42% on the KMMMU-Adv. Performance varies substantially across disciplines and answer formats, with Korean-specific questions showing gaps of up to 13.43%. Error analysis suggests that inspected failures often involve convention-to-label mapping, few-shot symbolic induction, localized knowledge recall, and domain-specific standards, not reasoning depth alone. KMMMU provides a testbed for native Korean expert-level multimodal understanding beyond English-centric or translation-based evaluation.
Abstract:Multimodal evaluation is strengthened when benchmarks reflect the languages, institutions, and disciplinary conventions that shape expert visual reasoning. We introduce KMMMU, a native Korean benchmark built from Korean-native examination materials. KMMMU contains 3,466 questions spanning nine disciplines and nine visual modality categories. For targeted evaluation on challenging questions and Korean-specific knowledge, we additionally provide KMMMU-Adv and KMMMU-Local, respectively. Experiments show that the strongest evaluated open-weight model reaches 42.05% on the KMMMU, while the best evaluated proprietary model achieves 52.42% on the KMMMU-Adv. Performance varies substantially across disciplines and answer formats, with Korean-specific questions showing gaps of up to 13.43%. Error analysis suggests that inspected failures often involve convention-to-label mapping, few-shot symbolic induction, localized knowledge recall, and domain-specific standards, not reasoning depth alone. KMMMU provides a testbed for native Korean expert-level multimodal understanding beyond English-centric or translation-based evaluation.
Computation and Language (cs.CL); Machine Learning (cs.LG); Multimedia (cs.MM)
arXiv:2604.13058 [cs.CL]
(or arXiv:2604.13058v3 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2604.13058 Focus to learn more arXiv-issued DOI via DataCite
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