most citedLanguage Models are Surprisingly Fragile to Drug Names in Biomedical Benchmarks

1 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2024

Multi-OphthaLingua: A Multilingual Benchmark for Assessing and Debiasing LLM Ophthalmological QA in LMICs

David Restrepo, Chenwei Wu, Zhengxu Tang +14

Current ophthalmology clinical workflows are plagued by over-referrals, long waits, and complex and heterogeneous medical records. Large language models (LLMs) present a promising…

cs.CL20241 cited

The use of large language models to enhance cancer clinical trial educational materials

Mingye Gao, Aman Varshney, Shan Chen +15

Cancer clinical trials often face challenges in recruitment and engagement due to a lack of participant-facing informational and educational resources. This study investigated the…

cs.CL2024

EHRmonize: A Framework for Medical Concept Abstraction from Electronic Health Records using Large Language Models

João Matos, Jack Gallifant, Jian Pei +1

Electronic health records (EHRs) contain vast amounts of complex data, but harmonizing and processing this information remains a challenging and costly task requiring significant c…

cs.CL20241 cited

Language Models are Surprisingly Fragile to Drug Names in Biomedical Benchmarks

Jack Gallifant, Shan Chen, Pedro Moreira +7

Medical knowledge is context-dependent and requires consistent reasoning across various natural language expressions of semantically equivalent phrases. This is particularly crucia…

cs.CL2024

Improving Clinical NLP Performance through Language Model-Generated Synthetic Clinical Data

Shan Chen, Jack Gallifant, Marco Guevara +5

Generative models have been showing potential for producing data in mass. This study explores the enhancement of clinical natural language processing performance by utilizing synth…