The Masked Advantage: Uncovering Local-Language Access to Cultural Knowledge in LLMs

Global deployment of AI raises a persistent concern: do large language models serve non-English-speaking communities as well as English speakers? This study offers a nuanced and somewhat counterintuitive answer. Models may actually encode more cultural knowledge in local languages than raw accuracy scores suggest — the apparent weakness is partly a language proficiency problem, not a knowledge problem. Disentangling the two has significant implications for multilingual AI development, localization strategies, and digital equity policy. For developers building culturally sensitive applications in healthcare, education, or civic services across diverse linguistic communities, this research reframes where investment in local-language AI is most urgently needed. Authors: Yang Zhang, Xiao Fei, Amr Mohamed, Sarah Almeida Carneiro, Mersin Konomi, Mingmeng Geng, Ahmed Asaad, Guokan Shang, Michalis Vazirgiannis Paper: https://arxiv.org/abs/2606.07422v1