人工智能模型的思维链推理与语言优化在牙科执照考试的牙套部分
Nan Hsu Myat Mon Hlaing1, Koungjin Park1, Seoyoun Hahn2
1Graduate student, Department of Prosthodontics, Seoul National University School of Dentistry, Seoul, Republic of Korea.
The Journal of prosthetic dentistry
|October 14, 2025
概括
思想链 (CoT) 大语言模型 (LLM) 在韩国牙科考试中实现了与人类相比的准确性. 本地化语言模型的表现并没有超过一般模型,这表明架构是临床领域专业知识的关键.
科学领域:
- 人工智能在医学中的应用
- 自然语言处理自然语言处理.
- 牙科教育 牙科教育
背景情况:
- 了解大型语言模型 (LLM) 在专门的非印欧临床环境中的表现是有限的.
- 在这些领域,思维链 (Chain-of-Thought,CoT) 推理与语言优化模型的有效性尚不清楚.
研究的目的:
- 为了比较韩国牙科执照考试 (KDLE) 假牙科部分六个具有不同架构的LLM的表现.
- 为了将LLM绩效与KDLE公布的人类平均值进行基准测试.
主要方法:
- 从KDLE (2020-2024) 进行的161个韩语假牙多选题对六名LLMs进行了管理.
- 模型包括CoT推理,通用和韩国优化的架构.
- 性能通过准确度进行测量,并使用统计测试进行比较,以人类平均值作为基准.
主要成果:
- 在LLM中发现了显著的绩效差异 (P<.001).
- 基于CoT的ChatGPT-o1获得了最高的准确度 (80.54%),与人类平均水平 (79.51%) 相比.
- 韩国优化的CLOVA X得分最低 (34.37%),性能因LLM架构而异.
结论:
- 具有CoT架构的LLM可以在非英语牙科考试中获得合格准确度,与人类的表现相当.
- 法学士的表现高度依赖于建筑设计.
- 语言优化本身并不能保证在专门的临床领域的专业知识.
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