MedBench: A Comprehensive, Standardized, and Reliable Benchmarking System for Evaluating Chinese Medical Large Language Models

• URL: http://arxiv.org/abs/2407.10990v1
• Date: Mon, 24 Jun 2024 02:25:48 GMT
• Title: MedBench: A Comprehensive, Standardized, and Reliable Benchmarking System for Evaluating Chinese Medical Large Language Models
• Authors: Mianxin Liu, Jinru Ding, Jie Xu, Weiguo Hu, Xiaoyang Li, Lifeng Zhu, Zhian Bai, Xiaoming Shi, Benyou Wang, Haitao Song, Pengfei Liu, Xiaofan Zhang, Shanshan Wang, Kang Li, Haofen Wang, Tong Ruan, Xuanjing Huang, Xin Sun, Shaoting Zhang,
• Abstract summary: "MedBench" is a comprehensive, standardized, and reliable benchmarking system for Chinese medical LLM. First, MedBench assembles the largest evaluation dataset (300,901 questions) to cover 43 clinical specialties. Third, MedBench implements dynamic evaluation mechanisms to prevent shortcut learning and answer remembering.
• Score: 55.215061531495984
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ensuring the general efficacy and goodness 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 (300,901 questions) to cover 43 clinical specialties and performs multi-facet evaluation on medical LLM. Second, MedBench provides a standardized and fully automatic cloud-based evaluation infrastructure, with physical separations for question and ground truth. Third, MedBench implements dynamic evaluation mechanisms to prevent shortcut learning and answer remembering. 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.

Related papers

• MedHallBench: A New Benchmark for Assessing Hallucination in Medical Large Language Models [0.0]
  Medical Large Language Models (MLLMs) have demonstrated potential in healthcare applications. Their propensity for hallucinations presents substantial risks to patient care. This paper introduces MedHallBench, a comprehensive benchmark framework for evaluating and mitigating hallucinations in MLLMs.
  arXiv  Detail & Related papers  (2024-12-25T16:51:29Z)
• CliMedBench: A Large-Scale Chinese Benchmark for Evaluating Medical Large Language Models in Clinical Scenarios [50.032101237019205]
  CliMedBench is a comprehensive benchmark with 14 expert-guided core clinical scenarios. The reliability of this benchmark has been confirmed in several ways.
  arXiv  Detail & Related papers  (2024-10-04T15:15:36Z)
• Towards Evaluating and Building Versatile Large Language Models for Medicine [57.49547766838095]
  We present MedS-Bench, a benchmark designed to evaluate the performance of large language models (LLMs) in clinical contexts. MedS-Bench spans 11 high-level clinical tasks, including clinical report summarization, treatment recommendations, diagnosis, named entity recognition, and medical concept explanation. MedS-Ins comprises 58 medically oriented language corpora, totaling 13.5 million samples across 122 tasks.
  arXiv  Detail & Related papers  (2024-08-22T17:01:34Z)
• MedBench: A Large-Scale Chinese Benchmark for Evaluating Medical Large Language Models [56.36916128631784]
  We introduce MedBench, a comprehensive benchmark for the Chinese medical domain. This benchmark is composed of four key components: the Chinese Medical Licensing Examination, the Resident Standardization Training Examination, and real-world clinic cases. We perform extensive experiments and conduct an in-depth analysis from diverse perspectives, which culminate in the following findings.
  arXiv  Detail & Related papers  (2023-12-20T07:01:49Z)
• ChiMed-GPT: A Chinese Medical Large Language Model with Full Training Regime and Better Alignment to Human Preferences [51.66185471742271]
  We propose ChiMed-GPT, a benchmark LLM designed explicitly for Chinese medical domain. ChiMed-GPT undergoes a comprehensive training regime with pre-training, SFT, and RLHF. We analyze possible biases through prompting ChiMed-GPT to perform attitude scales regarding discrimination of patients.
  arXiv  Detail & Related papers  (2023-11-10T12:25:32Z)
• PromptCBLUE: A Chinese Prompt Tuning Benchmark for the Medical Domain [24.411904114158673]
  We re-build the Chinese Biomedical Language Understanding Evaluation (CBlue) benchmark into a large scale prompt-tuning benchmark, PromptCBlue. Our benchmark is a suitable test-bed and an online platform for evaluating Chinese LLMs' multi-task capabilities on a wide range bio-medical tasks.
  arXiv  Detail & Related papers  (2023-10-22T02:20:38Z)
• CMB: A Comprehensive Medical Benchmark in Chinese [67.69800156990952]
  We propose a localized medical benchmark called CMB, a Comprehensive Medical Benchmark in Chinese. While traditional Chinese medicine is integral to this evaluation, it does not constitute its entirety. We have evaluated several prominent large-scale LLMs, including ChatGPT, GPT-4, dedicated Chinese LLMs, and LLMs specialized in the medical domain.
  arXiv  Detail & Related papers  (2023-08-17T07:51:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.