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Item Difficulty and Response Time Prediction with Large Language Models: An Empirical Analysis of USMLE Items
Download2024-06-20
Bulut, O., Gorgun, G., Tan, B.
This paper summarizes our methodology and results for the BEA 2024 Shared Task. This competition focused on predicting item difficulty and response time for retired multiple-choice items from the United States Medical Licensing Examination® (USMLE®). We extracted linguistic features from the item...
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2024-04-18
Anderson, Hailey, Duarte Cuartas, Michelle, Kublik, Jocelyn, Receveur, Thomas, Raj, Anushil
Extreme weather events, particularly tornadoes and windstorms, pose significant risks to vulnerable populations, including the elderly, individuals with disabilities, the unhoused, and low-income households. Vulnerable groups often lack access to resources necessary for emergency preparedness,...