大型语言模型在中文语言中的表现 医疗咨询关于杆菌
Mingjun Zhang1, Shiming Zhou1, Shulin Zhang1
1Department of Gastroenterology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, People's Republic of China.
Infection and drug resistance
|December 26, 2025
概括
大型语言模型 (LLM) 显示了Helicobacter pylori (H. pylori) 医学咨询的潜力,准确度可接受,但清晰度和可靠性可变. 像Ernie Bot和Doubao这样的中国LLM在H. pylori信息传递方面表现出了竞争力.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 公共卫生 公共卫生
背景情况:
- 杆菌 (H. pylori) 感染是一个重大的全球健康问题,增加了获得医疗建议的需求.
- 大型语言模型 (LLM) 是提供医疗咨询的潜在途径,但它们的有效性需要彻底评估.
研究的目的:
- 评估LLM在中国背景下提供与H. pylori相关的医疗咨询方面的有效性.
- 为了比较不同LLM在响应H. pylori查询方面的表现.
主要方法:
- 在四个领域收集了20个与H. pylori相关的问题:定义/症状,诊断,治疗和预防.
- 向多名法学士中文问题,评估答案的准确性,相关性,完整性,清晰性和可靠性.
- 进行了两批测试,包括与先前评估进行比较.
主要成果:
- 总体表现不同,初步测试显示33.3%的好评,66.1%的中等评分和0.6%的差评分.
- 准确性,相关性和完整性通常是可以接受的,但清晰性和可靠性显示得分较低.
- 埃尔尼博特和杜巴奥表现强,埃尔尼博特在可靠性方面表现出色,杜巴奥在相关性和清晰性方面表现出色.
- 最近的LLM评估表明,与前一年的评估相比,总体表现显著改善.
结论:
- LLM可以为H. pylori医疗咨询提供可接受的准确性,相关性和完整性.
- 清晰度和可靠性仍然是LLM响应中需要改进的领域.
- 像Ernie Bot这样的中国法学士学位显示出前景,并有可能在专业指导下作为医疗咨询的宝贵辅助工具.
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