眼睛语言增强模型 (LEME):一个开源的眼科专用大型语言模型
Aidan Gilson1, Xuguang Ai2, Qianqian Xie2
1Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts.
ArXiv
|October 1, 2025
概括
一个新的眼科专用大型语言模型 (LLM),眼睛语言增强模型 (LEME),在各种临床任务中显示出卓越的性能. 这个开源的LLM有潜力推进医疗保健和眼科医学的研究合作.
科学领域:
- 人工智能在医学中的应用
- 眼科医生 眼科 眼科
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 正在改变医疗保健,但专业眼科LLM很少.
- 现有的LLM缺乏针对复杂的眼科任务的具体培训.
研究的目的:
- 介绍和评估眼睛语言增强模型 (LEME),一个开源的专为眼科量身定制的LLM.
- 将LEME与眼科专项任务中的其他领先的LLM进行比较.
主要方法:
- LEME在Llama2 70B上接受了预先培训,并通过约127,000次眼科专用培训实例进行了微调.
- 评估包括内部 (摘要完成,填空,MCQ,简短答案QA) 和外部 (长形式QA,电子健康记录总结,临床QA) 任务.
- LEME与GPT-3.5,GPT-4,Llama2模型,PMC-LLAMA 13B,Meditron 70B和EYE-Llama进行了基准测试.
主要成果:
- 在内部任务中,LEME的表现优于其他LLM,包括抽象完成 (Rouge-L 0.20 ± 0.03) 和填空 (Rouge-L 0.82 ± 0.04).
- 在外部验证中,LEME在长形式QA (Rouge-L 0.19 ± 0.01) 中获得了高分,在EHR总结和临床QA方面排名最高.
- 在MCQ准确度方面,LEME表现强 (0.68 ± 0.09),总体排名第二.
结论:
- 在眼科专业的开源LLMs中,LEME是一个显著的进步.
- 其强大的微调和使用专业数据使其能够彻底改变临床任务和研究.
- LEME有可能使研究合作民主化,并改善眼科医疗保健成果.
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