MedQA Dataset

MedQA, and its related datasets, serve as benchmarks for evaluating large language models (LLMs) in the medical domain, focusing on their ability to accurately answer complex medical questions and exhibit clinically relevant skills. Current research emphasizes improving LLM performance through techniques like retrieval-augmented generation (RAG), chain-of-thought prompting, and addressing biases related to patient demographics. These efforts aim to enhance the reliability and safety of LLMs for medical applications, ultimately contributing to improved diagnostic accuracy, patient care, and medical education.