77 citations · 105 across the 7 of their papers we have counts for
5 papers · 2 filters
ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?
Canyu Chen, Jian Yu, Shan Chen +8
Large Language Models (LLMs) hold great promise to revolutionize current clinical systems for their superior capacities on medical text processing tasks and medical licensing exams…
Wait, but Tylenol is Acetaminophen... Investigating and Improving Language Models' Ability to Resist Requests for Misinformation
Shan Chen, Mingye Gao, Kuleen Sasse +6
Background: Large language models (LLMs) are trained to follow directions, but this introduces a vulnerability to blindly comply with user requests even if they generate wrong info…
Simulated patient systems powered by large language model-based AI agents offer potential for transforming medical education
Huizi Yu, Jiayan Zhou, Lingyao Li +22
Background: Simulated patient systems are important in medical education and research, providing safe, integrative training environments and supporting clinical decision making. Ad…
Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model Bias
Shan Chen, Jack Gallifant, Mingye Gao +12
Large language models (LLMs) are increasingly essential in processing natural languages, yet their application is frequently compromised by biases and inaccuracies originating in t…
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…