大型语言模型对英语和中文查询性能进行心血管预防的比较
Hongwei Ji1,2,3, Xiaofei Wang4, Ching-Hui Sia5,6
1Beijing Visual Science and Translational Eye Research Institute (BERI), Eye Center of Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China.
Communications medicine
|May 16, 2025
概括
聊天GPT-4.0在提供准确的心血管疾病 (CVD) 预防信息方面表现出色,优于其他大型语言模型 (LLMs). 对于中文查询,性能略有下降,突出显示了LLMs中潜在的语言偏差.
科学领域:
- 医疗保健中的人工智能
- 医疗信息的自然语言处理.
- 预防心血管疾病 预防心血管疾病
背景情况:
- 大型语言模型 (LLM) 显示出回答有关心血管疾病 (CVD) 预防的公共卫生问题的潜力.
- 对于预防心血管疾病的一般LLM信息的准确性和可靠性仍在调查中.
研究的目的:
- 评估领先的大型语言模型 (LLM) 响应心血管疾病 (CVD) 预防查询的准确性和一致性.
- 为了比较BARD,ChatGPT-3.5,ChatGPT-4.0和ERNIE在英语和中文中CVD预防信息检索的性能.
主要方法:
- 评估了BARD,ChatGPT-3.5,ChatGPT-4.0和ERNIE的功能,使用英语和中文的75个心血管疾病 (CVD) 预防问题.
- 对答案的准确性进行了评分,分别是适当的,边界的或不适当的.
- 评估每个模型的时间改善和响应正确性的自我意识.
主要成果:
- 在英语中,ChatGPT-4.0获得了最高的准确性 (97.3%),其次是ChatGPT-3.5 (92.0%) 和BARD (88.0%).
- 所有模型都随着时间的推移显示出改善,ChatGPT-4.0显示出优越的时间增长和自我纠正.
- 对于中文查询,ERNIE (84.0%) 和ChatGPT-3.5 (88.0%) 的准确性高于ChatGPT-4.0 (85.3%),ERNIE在改进和自我意识方面表现出色.
结论:
- 聊天GPT-4.0是英语CVD预防查询的领先LLM,表现出卓越的准确性,时间改进和自我意识.
- 中国语的轻微表现下降表明,目前的LLM可能会出现语言偏差.
- 定期,严格的评估对于评估跨语言LLM生成的医疗信息的质量和局限性至关重要.
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