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大型语言模型的可读性,可靠性和科学质量的基准测试,用于传达有机体科学

Man Sun¹, Dan Zang¹, Jun Chen¹

¹Department of Oncology, The Second Hospital of Dalian Medical University, Dalian, Liaoning, China.

Frontiers in bioengineering and biotechnology

|February 2, 2026

概括

大型语言模型 (LLM) 在解释有机体方面表现不同,GPT-5的表现优于其他类型. 可读性并不能保证科学准确性,强调了对专门AI系统的需求.

科学领域:

生物医学研究的研究.
医学中的人工智能
翻译性瘤学是指翻译性瘤学.

背景情况:

器官对精确瘤学和翻译研究至关重要.
关于有机体的准确,透明和临床负责的沟通是必不可少的.
大型语言模型 (LLM) 对于解释有机体的可靠性尚未得到充分证实.

研究的目的:

系统地评估五个主流LLM在解释有机体相关概念方面的表现.
评估LLM产生的内容的可读性,科学严谨性和教育适用性之间的平衡.
识别LLM绩效的差异及其对临床和教育使用的影响.

主要方法:

五个LLM (GPT-5,DeepSeek,Doubao,Tongyi Qianwen,Wenxin Yiyan) 进行了测试. 这些LLM包括:
一组30个代表性的有机体相关问题被用于评估.
输出使用C-PEMAT-P,全球质量评分 (GQS) 和七个可读性指数得分.

主要成果:

GPT-5表现出卓越的性能 (最高的C-PEMAT和GQS分数),其次是DeepSeek和Doubao.
义文和温义的表现明显较低,结果不稳定.
根据模型和问题类型,可读性各不相同;语言简单性与科学质量或可靠性没有很强的相关性.

结论:

在传递有机体信息方面,LLM表现出显著的变化,创造了不同的性能层.
可读性,科学质量和可靠性在模型之间存在差异,这意味着简单的语言不足以准确.
需要有机体特定的AI系统,以确保沟通的透明度,可靠性和安全性.

关键词:

人工智能的人工智能是人工智能.大型语言模型.在线医疗信息在线医疗信息有机生物有机物可读性可读性

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