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This study introduces LENS, a new benchmark designed to evaluate Large Language Models on low-resource sociolinguistic contexts, specifically focusing on the distinct linguistic and ideological divide between North and South Korea. The research reveals that current models struggle significantly with aligning to local norms and avoiding stereotypical projections, demonstrating that linguistic similarity does not equate to cultural understanding. This work highlights a critical gap in AI evaluation resources for underrepresented political and cultural environments.
North or South? Diagnosing LLMs' Understanding of North Korean Language, Society, and Knowledge
arXiv:2610.15282v1 Announce Type: cross Abstract: Large Language Models (LLMs) have made substantial progress in multilingual question answering, yet their ability to understand low-resource sociolinguistic contexts remains under-explored. We study North Korea as a unique testbed where decades of division have produced distinct differences from South Korea in vocabulary, orthography, norms, and ideological discourse. We introduce LENS (Language and sociocultural Evaluation for North Korean Society), a human-validated benchmark of 1,765 questions evaluating LLMs across three dimensions: linguistic robustness, sociocultural perspective alignment, and domain-specific knowledge. Evaluating diverse recent LLMs, we find a modest performance drop on North Korean language inputs, a larger gap in sociocultural perspective alignment, and severe limitations on domain-specific knowledge. Qualitative analysis reveals that models often conflate formal institutions with lived realities, project liberal-democratic assumptions, or rely on coarse stereotypes. Our results demonstrate that linguistic similarity alone does not guarantee sociocultural or domain-specific understanding, highlighting the critical need for grounded evaluation resources for underrepresented cultural and political contexts.
Title:North or South? Diagnosing LLMs' Understanding of North Korean Language, Society, and Knowledge
Abstract:Large Language Models (LLMs) have made substantial progress in multilingual question answering, yet their ability to understand low-resource sociolinguistic contexts remains under-explored. We study North Korea as a unique testbed where decades of division have produced distinct differences from South Korea in vocabulary, orthography, norms, and ideological discourse. We introduce LENS (Language and sociocultural Evaluation for North Korean Society), a human-validated benchmark of 1,765 questions evaluating LLMs across three dimensions: linguistic robustness, sociocultural perspective alignment, and domain-specific knowledge. Evaluating diverse recent LLMs, we find a modest performance drop on North Korean language inputs, a larger gap in sociocultural perspective alignment, and severe limitations on domain-specific knowledge. Qualitative analysis reveals that models often conflate formal institutions with lived realities, project liberal-democratic assumptions, or rely on coarse stereotypes. Our results demonstrate that linguistic similarity alone does not guarantee sociocultural or domain-specific understanding, highlighting the critical need for grounded evaluation resources for underrepresented cultural and political contexts.
Accepted to AACL-IJCNLP 2026 (Main Conference). 19 pages, 1 figure, 11 tables
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2610.15282 [cs.CL]
(or arXiv:2610.15282v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.15282 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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