@article{Liu2024, 
author = {Mianxin Liu and Weiguo Hu and Jinru Ding and Jie Xu and Xiaoyang Li and Lifeng Zhu and Zhian Bai and Xiaoming Shi and Benyou Wang and Haitao Song and Pengfei Liu and Xiaofan Zhang and Shanshan Wang and Kang Li and Haofen Wang and Tong Ruan and Xuanjing Huang and Xin Sun and Shaoting Zhang},
title = {MedBench: A Comprehensive, Standardized, and Reliable Benchmarking System for Evaluating Chinese Medical Large Language Models},
year = {2024},
journal = {Big Data Mining and Analytics},
volume = {7},
number = {4},
pages = {1116-1128},
keywords = {benchmark, platform, open-source, Medical Large Language Model (MLLM)},
url = {https://www.sciopen.com/article/10.26599/BDMA.2024.9020044},
doi = {10.26599/BDMA.2024.9020044},
abstract = {Ensuring the general efficacy and benefit for human beings from medical Large Language Models (LLM) before real-world deployment is crucial. However, a widely accepted and accessible evaluation process for medical LLM, especially in the Chinese context, remains to be established. In this work, we introduce “MedBench”, a comprehensive, standardized, and reliable benchmarking system for Chinese medical LLM. First, MedBench assembles the currently largest evaluation dataset (300901 questions) to cover 43 clinical specialties, and performs multi-faceted evaluation on medical LLM. Second, MedBench provides a standardized and fully automatic cloud-based evaluation infrastructure, with physical separations between question and ground truth. Third, MedBench implements dynamic evaluation mechanisms to prevent shortcut learning and answer memorization. Applying MedBench to popular general and medical LLMs, we observe unbiased, reproducible evaluation results largely aligning with medical professionals’ perspectives. This study establishes a significant foundation for preparing the practical applications of Chinese medical LLMs. MedBench is publicly accessible at https://medbench.opencompass.org.cn.}
}