@inproceedings{li-etal-2026-diversity,
title = "Diversity in Unity, Theory in Practice: Hierarchical Multitask Benchmarks for {C}hinese Minority Languages",
author = "Li, Yijie and
Cao, Xi and
Sun, Yuan and
Minggad, Quulgan and
Ablikim, Abdulla and
Wang, Jia Qing Cai",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1684/",
pages = "36353--36373",
ISBN = "979-8-89176-390-6",
abstract = "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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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Diversity in Unity, Theory in Practice: Hierarchical Multitask Benchmarks for Chinese Minority Languages
%A Li, Yijie
%A Cao, Xi
%A Sun, Yuan
%A Minggad, Quulgan
%A Ablikim, Abdulla
%A Wang, Jia Qing Cai
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F li-etal-2026-diversity
%X 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.
%U https://aclanthology.org/2026.acl-long.1684/
%P 36353-36373
Markdown (Informal)
[Diversity in Unity, Theory in Practice: Hierarchical Multitask Benchmarks for Chinese Minority Languages](https://aclanthology.org/2026.acl-long.1684/) (Li et al., ACL 2026)
ACL
- Yijie Li, Xi Cao, Yuan Sun, Quulgan Minggad, Abdulla Ablikim, and Jia Qing Cai Wang. 2026. Diversity in Unity, Theory in Practice: Hierarchical Multitask Benchmarks for Chinese Minority Languages. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 36353–36373, San Diego, California, United States. Association for Computational Linguistics.