Back to search

Article

Multimodal Large Language Model Passes Specialty Board Examination and Surpasses Human Test-Taker Scores: A Comparative Analysis Examining the Stepwise Impact of Model Prompting Strategies on Performance

2024-07-29

Abstract excerpt

<h4>ABSTRACT</h4> <h4>Background</h4> Large language models (LLMs) have shown promise in answering medical licensing examination-style questions. However, there is limited research on the performance of multimodal LLMs on subspecialty medical examinations. Our study benchmarks the performance of multimodal LLM’s enhanced by model prompting strategies on gastroenterology subspeciality examination-style questions an...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
e7afa353-2daf-531c-a9a5-2d37992a13d7
DOI
10.1101/2024.07.27.24310809
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Multimodal Large Language Model Passes Specialty Board Examination and Surpasses Human Test-Taker Scores: A Comparative Analysis Examining the Stepwise Impact of Model Prompting Strategies on PerformanceDOI 10.1101/2024.07.27.24310809
Select a neighboring publication to make it the new centre.