MedEthicEval: Evaluating Large Language Models Based on Chinese Medical Ethics
This addresses the need for responsible LLM use in medical applications by providing a systematic evaluation framework, though it is incremental as it builds on existing benchmark methodologies.
The paper tackles the problem of evaluating large language models (LLMs) in medical ethics by introducing MedEthicEval, a benchmark that assesses models' knowledge and application of ethical principles across diverse scenarios, resulting in a tool for understanding LLMs' ethical reasoning in healthcare.
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.