修复牙科和内牙科中大语言模型的准确性,一致性和上下文理解
Claire Lafourcade1, Olivia Kérourédan2, Benoit Ballester3
1UFR des Sciences Odontologiques, Université de Bordeaux, Bordeaux, France; CHU de Bordeaux, Pôle de Médecine et Chirurgie bucco-dentaire, Bordeaux, France.
Journal of dentistry
|April 17, 2025
概括
先进的大型语言模型 (LLM) 在修复牙科和内牙科中表现适度. 虽然ChatGPT-4和Claude 3是准确的,但它们的一致性需要谨慎地应用于牙科教育和实践.
科学领域:
- 牙科科学 牙科科学
- 人工智能的人工智能
- 医学教育 医学教育
背景情况:
- 大型语言模型 (LLM) 越来越多地在专业医疗领域探索其潜力.
- 评估牙科LLM的表现对于了解它们在教育和临床环境中的适用性至关重要.
研究的目的:
- 评估和比较在修复牙科和内牙科领先的LLM的准确性,一致性和上下文理解.
- 确定为LLM提供特定课程内容对其绩效的影响.
主要方法:
- 利用来自国家牙科住院档案的多选题和定义数据集.
- 在准确性,强度,可重复性和上下文理解方面评估了四个LLM (ChatGPT-3.5,ChatGPT-4,Claude-3,Mistral 7B).
- 在增加相关课程章节后,重新评估了LLM准确性.
主要成果:
- 与Mistral 7B相比,ChatGPT-4和Claude-3表现出更高的准确性和重复性.
- 在评估的模型中,ChatGPT-4显示出了最高的稳定性.
- 提供课程章节的LLM通常会提高准确性,但结果在不同主题上有所不同.
结论:
- 高级LLM在修复性牙科和内牙科中达到适度的性能,由于强度不一致,需要仔细实施.
- 未来的研究应该集中在整合验证的内容和优化快速工程,以提高在牙科LLM实用性.
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