MedEthicEval：基于中国医学伦理评估大型语言模型
MedEthicEval: Evaluating Large Language Models Based on Chinese Medical Ethics
AI总结：
本文提出MedEthicEval基准，通过知识和应用两个维度及三个专门数据集，系统评估大型语言模型在中国医学伦理方面的推理能力，促进其在医疗场景中的负责任应用。
摘要：
大型语言模型（LLMs）在推进医疗应用方面展现出巨大潜力，但其在应对医学伦理挑战方面的能力仍有待深入探索。本文介绍了MedEthicEval，这是一个旨在系统评估LLMs在医学伦理领域表现的新型基准。我们的框架包含两个关键组件：知识，评估模型对医学伦理原则的掌握程度；应用，关注模型在多样化场景中应用这些原则的能力。为支持该基准，我们咨询了医学伦理研究人员，并开发了三个应对不同伦理挑战的数据集：公然违反医学伦理的行为、具有明显倾向性的优先级困境，以及没有明显解决方案的平衡困境。MedEthicEval是理解LLMs在医疗保健中伦理推理的重要工具，为其在医疗环境中的负责任和有效使用铺平了道路。
英文摘要：
Large language models (LLMs) demonstrate significant potential in advancing medical applications, yet their capabilities in addressing medical ethics challenges remain underexplored. This paper introduces MedEthicEval, a novel benchmark designed to systematically evaluate LLMs in the domain of medical ethics. Our framework encompasses two key components: knowledge, assessing the models' grasp of medical ethics principles, and application, focusing on their ability to apply these principles across diverse scenarios. To support this benchmark, we consulted with medical ethics researchers and developed three datasets addressing distinct ethical challenges: blatant violations of medical ethics, priority dilemmas with clear inclinations, and equilibrium dilemmas without obvious resolutions. MedEthicEval serves as a critical tool for understanding LLMs' ethical reasoning in healthcare, paving the way for their responsible and effective use in medical contexts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
