在医疗保健中流行开源大型语言模型的性能评估
Saif Khairat1, Tianyi Niu1, John Geracitano1
1University of North Carolina at Chapel Hill.
Studies in health technology and informatics
|July 1, 2025
概括
开源大型语言模型 (LLM) 显示出作为医疗保健助理的希望,提供透明度和定制性. 虽然GPT-4o更受欢迎,但开源模型在负载下表现出效率和可靠性.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 医疗保健信息学 医疗保健信息学
背景情况:
- 大型语言模型 (LLM) 越来越多地被用于医疗保健应用.
- 开源的LLM提供了诸如透明度,成本效益和定制潜力等优势.
- 评估用户偏好和计算性能对于实际的LLM部署至关重要.
研究的目的:
- 为了评估用户对开源LLM (Llama 3.1 8B,Mistral 3 Small 24B) 和专有模型 (GPT-4o) 的偏好,作为医疗保健助理.
- 在不同的用户负载下对这些LLM的计算性能指标进行基准测试.
- 了解开源LLM对于面向用户的医疗保健应用程序的适用性.
主要方法:
- 一种双重方法的方法,结合了主观的用户评估和客观的性能基准测试.
- 十名参与者对Llama 3.1 8B,Mistral 3 Small 24B (AWQ) 和GPT-4o对与医疗保健相关的查询的回答进行了排名.
- 使用响应时间,吞吐量和模拟用户负载下的时间到第一个令牌来测量计算性能.
主要成果:
- 大多数参与者更喜欢GPT-4o产生的反应.
- 开源的LLMs,Llama 3.1 8B和Mistral 3 Small 24B (AWQ),获得了类似的用户评分.
- 基准测试显示,开源模型的性能高效,可靠,即使在显著的用户负载下.
结论:
- 开源的LLM证明了医疗保健助理角色的可行性和效率.
- 虽然专有模式目前可能导致用户偏好,但开源替代方案提供了令人信服的优势.
- 需要对各种领域的开源LLM采用进行进一步的研究.
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