Year: 2025
Type: conference-paper
Abstract: Large language models (LLMs) excel in highresource languages but struggle with lowresource 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 1 .
Authors: Chen Zhang, Mingxu Tao, Zhiyuan Liao, Yansong Feng
Institution: Peking University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Award: G3747035606
OpenAlex ID: W4412888349