基于大型语言模型的环境意识综合培训模块的开发,用于辐射保护的持续教育
Anna Romanyukha1, Mahta Mazloumi1, Thomas De Waelheyns1
1Qaelum NV, Leuven, Belgium.
概括
一个新的AI模型为医疗保健专业人员提供可靠,快速的辐射保护信息. 这种特定领域的大型语言模型 (LLM) 使用受控知识进行持续教育,改进研究和培训.
科学领域:
- 医疗保健中的人工智能
- 辐射保护科学
- 医疗教育技术
背景情况:
- 大型语言模型 (LLM) 需要特定领域的调整以提供可靠的信息.
- 医疗教育和研究需要准确,易于获取的科学知识.
- 对于专业人士和研究人员来说,持续的辐射保护教育至关重要.
研究的目的:
- 开发一个特定领域的LLM,用于辐射保护的持续教育.
- 使用户能够查询特定主题并获得可靠的科学信息.
- 减少对广泛的教育材料进行手动审查的需要.
主要方法:
- 开发和培训一个特定领域的LLM使用定制的辐射保护知识.
- 用各种场景和微调超参数 (例如,顶部k,块大小,温度) 测试模型.
- 根据答案的准确性和可靠性评估模型的性能.
主要成果:
- 该课程提供了有关辐射保护问题的可靠和准确答案.
- 超参数调整,特别是嵌入模型和相似性切断,显著影响了性能.
- 该模型成功地处理和传递来自受控教育材料的信息.
结论:
- 经过验证的LLM有效地支持辐射保护的持续教育.
- 用户可以快速可靠地获取有关放射生物和辐射安全的信息.
- 这种人工智能工具增强了医疗保健专业人员和现场研究人员的学习能力.
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