面向全球医疗场景的大语言模型：GlobMed 数据集、基准与模型套件
Toward Global Large Language Models in Medicine
研究团队构建了多语言医疗数据集 GlobMed，含 20 种语言、超 80 万条数据（其中 7 种为低资源语言），并据此推出 GlobMed-Bench，对 56 个 SOTA 闭源与开源权重 LLM 在 12 种语言上完成超 2 万次独立实验、逾 3200 万条模型响应评测，发现低资源语言性能差距显著。
Authors:Rui Yang, Huitao Li, Weihao Xuan, Heli Qi, Xin Li, Kunyu Yu, Yingjian Chen, Rongrong Wang, Jacques Behmoaras, Tianxi Cai, Bibhas Chakraborty, Qingyu Chen, Lionel Tim-Ee Cheng, Marie-Louise Damwanza, Chido Dzinotyiwei, Aosong Feng, Chuan Hong, Yusuke Iwasawa, Yuhe Ke, Linah Kitala, Taehoon Ko, Jisan Lee, Irene Li, Jonathan Chong Kai Liew, Hongfang Liu, Lian Leng Low, Edison Marrese-Taylor, Yutaka Matsuo, Isheanesu Misi, Yilin Ning, Jasmine Chiat Ling Ong, Marcus Eng Hock Ong, Enrico Petretto, Hossein Rouhizadeh, Abiram Sandralegar, Oren Schreier, Iain Beehuat Tan, Patrick Tan, Daniel Shu Wei Ting, Junjue Wang, Chunhua Weng, Matthew Yu Heng Wong, Fang Wu, Yunze Xiao, Xuhai Xu, Qingcheng Zeng, Zhuo Zheng, Yifan Peng, Douglas Teodoro, Nan Liu
Abstract:Despite continuous advances in medical technology, the global distribution of health care resources remains uneven. The development of large language models (LLMs) has transformed the landscape of medicine and holds promise for improving health care quality and expanding access to medical information globally. However, existing LLMs are primarily trained on high-resource languages, limiting their applicability in global medical scenarios. To address this gap, we constructed GlobMed, a large multilingual medical dataset containing more than 800,000 entries spanning 20 languages, including seven low-resource languages. Building on this, we established GlobMed-Bench, which systematically assesses 56 state-of-the-art proprietary and open-weight LLMs across 12 of the 20 languages represented in GlobMed, comprising over 20,000 independent experiments and more than 32 million model responses. This large-scale evaluation revealed significant performance disparities across languages, particularly for low-resource languages. Additionally, we introduced GlobMed-LLMs, a suite of multilingual medical LLMs trained across the 12 benchmark languages, with parameters ranging from 1.7B to 8B. GlobMed-LLMs achieved an average performance improvement of nearly 40% relative to baseline LLMs, with a maximum performance improvement of more than threefold in low-resource languages. These resources provide an important foundation for advancing the equitable development and application of LLMs globally, enabling broader language communities to benefit from technological advances.
| Comments: | 200 pages, 65 figures |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2601.02186 [cs.CL] |
| (or arXiv:2601.02186v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2601.02186 arXiv-issued DOI via DataCite |
Submission history
From: Rui Yang [view email]
[v1]
Mon, 5 Jan 2026 15:05:49 UTC (23,010 KB)
[v2]
Sat, 24 Oct 2026 02:39:31 UTC (24,239 KB)
来源：arXiv cs.CL · arxiv.org