分类学肖像:解读医学大语言模型的等级关系
Radha Nagarajan1, Vanessa Klotzman1, Midori Kondo2
1Rady Children's Health, 1201 W La Veta Ave, Orange, CA, 92868, United States, 1 714-997-3000.
JMIR medical informatics
|October 8, 2025
概括
本研究介绍了医疗大语言模型 (LLM) 的分类肖像,以帮助医疗保健采用. 结果揭示了不同的LLM家族,并建议用于高效评估的基准冗余.
科学领域:
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
- 机器学习绩效评估 机器学习绩效评估
背景情况:
- 大型语言模型 (LLM) 在医疗保健中越来越多地被采用,需要强有力的绩效评估.
- 绩效基准对于排名和指导在医疗保健领域采用LLM至关重要.
研究的目的:
- 使用多变量性能基准,开发医学LLM (n=33) 的分类图像.
- 确定客观的方法来选择高性能的LLMs,而不会影响医疗保健的效率.
主要方法:
- 从 Hugging Face 排行榜中对特定领域和非特定绩效基准进行分层分类.
- 使用威尔科克森等级和和测试和线性相关性来分析基准差异和冗余.
主要成果:
- 根据统计学上显著的性能差异,确定了两种不同的医学LLM家族.
- 跨任务绩效的共识表明了LLM的稳定性,而基准相关性表明了潜在的冗余.
结论:
- 医学LLM分类学有助于识别具有相似性能的模型,与医疗保健需求和经济学保持一致.
- 提高绩效基准和经济学的透明度对于在医疗保健中更广泛,更公平地采用LLM至关重要.
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