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Updated: May 10, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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在航空航天医学原则上评估大型语言模型

Kyle D Anderson1, Cole A Davis2, Shawn M Pickett3

  • 1Department of Orthopaedics, Emory Sports Performance and Research Center, Emory School of Medicine, Flowery Branch, GA.

Wilderness & environmental medicine
|April 28, 2025
PubMed
概括

大型语言模型 (LLM) 显示出太空医学决策支持的希望,但需要进一步开发. 虽然它们是准确的,但它们表现出知识差距和不一致性,需要在自主医疗操作中保持谨慎.

关键词:
聊天GPT-4 聊天GPT-4 聊天谷歌的双子座是先进的获取 - 增强后代的获取航空航天医学 航空航天医学人工智能的人工智能是人工智能.

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科学领域:

  • 航空航天医学 航空航天医学
  • 医疗保健中的人工智能

背景情况:

  • 大型语言模型 (LLM) 为太空飞行中的临床决策支持提供了潜力.
  • 由LLM产生的不正确信息在地球独立的医疗环境中构成风险.

研究的目的:

  • 评估公开可用的LLM (ChatGPT-4,Gemini Advanced) 和一个定制的Retrieval-Augmented Generation (RAG) LLM的表现.
  • 用航空航天医学材料评估事实知识,临床推理和LLM的一致性.

主要方法:

  • 在857个自由答案和20个多选题的航空航天医学委员会问题上测试了LLM.
  • 评估的读者得分 (利克尔特尺度1-5) 对于自由答案的答案.
  • 评估了多选题的正确答案率.

主要成果:

  • 聊天GPT-4,双子高级和RAG LLM的平均读者分数分别为4.23-5.00,3.30-4.91和4.69-5.00.
  • 多选择题的正确答案率为70% (ChatGPT-4),55% (Gemini Advanced) 和85% (RAG LLM).这些问题中的正确答案率为70% (ChatGPT-4),55% (Gemini Advanced) 和85% (RAG LLM).
  • 所有的LLM都展示了事实知识缺口和潜在的不一致性,理由可能无法通过董事会考试.

结论:

  • 在太空飞行中,LLM对自主医疗操作有相当大的希望.
  • 预计在法学士培训,数据质量和微调方面将继续取得进展.
  • 在航空航天医学广泛临床应用之前,仔细的验证和开发至关重要.