Diversity in Unity, Theory in Practice: Hierarchical Multitask Benchmarks for Chinese Minority Languages
Yijie Li, Xi Cao, Yuan Sun Minzu University of China, Quulgan Minggad, Abdulla Ablikim, Jia Qing Cai Wang
Despite the rapid advancement of LLMs, their performance on linguistically and culturally diverse minority languages within a unified national context remains underexplored. We present CMiLBench, a collection of hierarchical multitask benchmarks designed to translate theoretical notions of “diversity in unity” into practical evaluation for three representative Chinese minority languages: Tibetan, Mongolian, and Uyghur. CMiLBench comprises 24,663 instances across 5 difficulty levels and 17 tasks spanning foundational ability, cultural specificity, and safety alignment. We adopt existing dataset adaptation, minority knowledge construction, and high-resource benchmark translation to construct CMiLBench. We assess 14 state-of-the-art commercial and open-source LLMs with a hybrid framework that integrates automatic metrics and LLM-as-a-Judge scoring. The comparative experimental results reveal the gap between theoretical capability and practical utility. CMiLBench serves as a foundational and scalable evaluation resource to bridge the digital language divide and promote the informatization and intelligentization of low-resource Chinese minority languages.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表