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20232026
most citedIntegrating UMLS Knowledge into Large Language Models for Medical Question Answering

8 citations · 19 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CL2026

HealMed: Multilingual Evaluation of Large Language Models in Medicine

Yingjian Chen, Fan Gao, Sherry T. Tong +42

We present HealMed, an expert-reviewed benchmark for multilingual evaluation of large language models in medicine. HealMed contains 1,000 examples in each of nine languages, drawn…

cs.CL2025

The Evolving Landscape of Generative Large Language Models and Traditional Natural Language Processing in Medicine

Rui Yang, Huitao Li, Matthew Yu Heng Wong +12

Natural language processing (NLP) has been traditionally applied to medicine, and generative large language models (LLMs) have become prominent recently. However, the differences b…

q-bio.OT2025★ 1 cited

Retrieval-Augmented Generation in Biomedicine: A Survey of Technologies, Datasets, and Clinical Applications

Jiawei He, Boya Zhang, Hossein Rouhizadeh +6

Large language models (LLMs) in biomedicine face a fundamental conflict between static parameter knowledge and the dynamic nature of clinical evidence. Retrieval-Augmented Generati…

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…

cs.AI2025★ 1 cited

ReAgent: Reversible Multi-Agent Reasoning for Knowledge-Enhanced Multi-Hop QA

Xinjie Zhao, Fan Gao, Xingyu Song +7

Recent advances in large language models (LLMs) have significantly improved multi-hop question answering (QA) through direct Chain-of-Thought (CoT) reasoning. However, the irrevers…

cs.CL2025★ 1 cited

GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking

Yingjian Chen, Haoran Liu, Yinhong Liu +8

Large language models (LLMs) are widely used, but they often generate subtle factual errors, especially in long-form text. These errors are fatal in some specialized domains such a…

cs.CL2024★ 5 cited

KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques

Rui Yang, Haoran Liu, Edison Marrese-Taylor +8

Large language models (LLMs) have demonstrated impressive generative capabilities with the potential to innovate in medicine. However, the application of LLMs in real clinical sett…

cs.CL2023★ 2 cited

Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation

Rui Yang, Qingcheng Zeng, Keen You +13

This study introduces Ascle, a pioneering natural language processing (NLP) toolkit designed for medical text generation. Ascle is tailored for biomedical researchers and healthcar…

cs.CL2023★ 1 cited

NLPBench: Evaluating Large Language Models on Solving NLP Problems

Linxin Song, Jieyu Zhang, Lechao Cheng +3

Recent developments in large language models (LLMs) have shown promise in enhancing the capabilities of natural language processing (NLP). Despite these successes, there remains a…

cs.CL2023★ 8 cited

Integrating UMLS Knowledge into Large Language Models for Medical Question Answering

Rui Yang, Edison Marrese-Taylor, Yuhe Ke +3

Large language models (LLMs) have demonstrated powerful text generation capabilities, bringing unprecedented innovation to the healthcare field. While LLMs hold immense promise for…