most citedToward Global Large Language Models in Medicine

1 citations · 1 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CL20261 cited

Toward Global Large Language Models in Medicine

Rui Yang, Huitao Li, Weihao Xuan +47

Despite continuous advances in medical technology, the global distribution of health care resources remains uneven. The development of large language models (LLMs) has transformed…

cs.CL2026

Investigating the Multilingual Calibration Effects of Language Model Instruction-Tuning

Jerry Huang, Peng Lu, Qiuhao Zeng +5

Ensuring that deep learning models are well-calibrated in terms of their predictive uncertainty is essential in maintaining their trustworthiness and reliability, yet despite incre…

cs.CL2025

When Instructions Multiply: Measuring and Estimating LLM Capabilities of Multiple Instructions Following

Keno Harada, Yudai Yamazaki, Masachika Taniguchi +4

As large language models (LLMs) are increasingly applied to real-world scenarios, it becomes crucial to understand their ability to follow multiple instructions simultaneously. To…

cs.CL2025

Multilingual Definition Modeling

Edison Marrese-Taylor, Erica K. Shimomoto, Alfredo Solano +1

In this paper, we propose the first multilingual study on definition modeling. We use monolingual dictionary data for four new languages (Spanish, French, Portuguese, and German) a…

cs.CL2025

MKG-Rank: Enhancing Large Language Models with Knowledge Graph for Multilingual Medical Question Answering

Feiyang Li, Yingjian Chen, Haoran Liu +10

Large Language Models (LLMs) have shown remarkable progress in medical question answering (QA), yet their effectiveness remains predominantly limited to English due to imbalanced m…

cs.CL2025

MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation

Weihao Xuan, Rui Yang, Heli Qi +29

Existing large language model (LLM) evaluation benchmarks primarily focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-lingui…