Update README.md

README.md

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 # KorMedMCQA : Multi-Choice Question Answering Benchmark for Korean Healthcare Professional Licensing Examinations
 
-We introduce KorMedMCQA, the first Korean multiple-choice question answering (MCQA) benchmark derived from Korean healthcare professional licensing examinations, covering from the year 2012 to year 2023. 
-This dataset consists of a selection of questions from the license examinations for doctors, nurses, and pharmacists, featuring a diverse array of subjects. 
-We conduct baseline experiments on various large language models, including proprietary/open-source, multilingual/Korean-additional pretrained, and clinical context pretrained models, highlighting the potential for further enhancements. 
-We make our data publicly available on HuggingFace and provide a evaluation script via LM-Harness, inviting further exploration and advancement in Korean healthcare environments.
+We present KorMedMCQA, the first Korean Medical Multiple-Choice Question
+Answering benchmark, derived from professional healthcare licensing
+examinations conducted in Korea between 2012 and 2024. The dataset contains
+7,469 questions from examinations for doctor, nurse, pharmacist, and dentist,
+covering a wide range of medical disciplines. We evaluate the performance of 59
+large language models, spanning proprietary and open-source models,
+multilingual and Korean-specialized models, and those fine-tuned for clinical
+applications. Our results show that applying Chain of Thought (CoT) reasoning
+can enhance the model performance by up to 4.5% compared to direct answering
+approaches. We also investigate whether MedQA, one of the most widely used
+medical benchmarks derived from the U.S. Medical Licensing Examination, can
+serve as a reliable proxy for evaluating model performance in other regions-in
+this case, Korea. Our correlation analysis between model scores on KorMedMCQA
+and MedQA reveals that these two benchmarks align no better than benchmarks
+from entirely different domains (e.g., MedQA and MMLU-Pro). This finding
+underscores the substantial linguistic and clinical differences between Korean
+and U.S. medical contexts, reinforcing the need for region-specific medical QA
+benchmarks. 
 
 Paper : https://arxiv.org/abs/2403.01469
 
 ## Notice
 
-In collaboration with [Hugging Face](https://huggingface.co/datasets/ChuGyouk/KorMedMCQA_edited), we have made the following updates to the KorMedMCQA dataset:
+We have made the following updates to the KorMedMCQA dataset:
 
 1. **Dentist Exam**: Incorporated exam questions from 2021 to 2024.
 2. **Updated Test Sets**: Added the 2024 exam questions for the doctor, nurse, and pharmacist test sets.