JMedEthicBench: A Multi-Turn Conversational Benchmark for Evaluating Medical Safety in Japanese Large Language Models

1. 1Develop a multi-turn conversational benchmark to evaluate medical safety in LLMs
2. 2Create a dataset with Japanese language prompts and responses to address the language gap in existing benchmarks
3. 3Evaluate LLMs using JMedEthicBench to identify potential medical safety risks
4. 4Fine-tune LLMs based on the evaluation results to improve their medical safety performance

AI engineers and researchers working on healthcare applications of LLMs can benefit from JMedEthicBench to ensure medical safety, while data scientists and ml-researchers can utilize it to fine-tune and evaluate their models

💡 JMedEthicBench is the first multi-turn conversational benchmark for evaluating medical safety in Japanese LLMs, addressing the need for language-specific and clinically relevant evaluations

🚑 Evaluate medical safety in Japanese LLMs with JMedEthicBench!