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arXiv · 2503.01150

MiLiC-Eval: Benchmarking Multilingual LLMs for China's Minority Languages

Chen Zhang·Mingxu Tao·Zhiyuan Liao·Yansong Feng
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Abstract

Large language models (LLMs) excel in high-resource languages but struggle with low-resource languages (LRLs), particularly those spoken by minority communities in China, such as Tibetan, Uyghur, Kazakh, and Mongolian. To systematically track the progress in these languages, we introduce MiLiC-Eval, a benchmark designed for minority languages in China, featuring 24K instances across 9 tasks. MiLiC-Eval focuses on underrepresented writing systems. Its parallelism between tasks and languages can provide a faithful and fine-grained assessment of linguistic and problem-solving skills. Our evaluation reveals that open-source LLMs perform poorly on syntax-intensive tasks and multi-script languages. We further demonstrate how MiLiC-Eval can help advance LRL research in handling diverse writing systems and understanding the process of language adaptation.

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Chen Zhang, Mingxu Tao, Zhiyuan Liao, Yansong Feng. 2025-03-03. MiLiC-Eval: Benchmarking Multilingual LLMs for China's Minority Languages. https://arxiv.org/abs/2503.01150

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