一本关于在医学研究中使用大型语言模型时逃避幻觉并保持可靠性的指南:叙事审查
Sangzin Ahn1,2
1Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan, Korea.
Annals of pediatric endocrinology & metabolism
|July 8, 2025
概括
大型语言模型 (LLM) 由于内在的架构限制,在医学研究中存在可靠性挑战. 人类的监督和验证对于安全地将LLMs集成到复杂的生物系统中至关重要.
科学领域:
- 医学研究 医学研究
- 人工智能的人工智能
- 计算生物学 计算生物学
背景情况:
- 大型语言模型 (LLM) 越来越多地被用于医学研究.
- 法律学体系结构具有固有的局限性,包括自回归预测和不可决定性约束,导致可靠性挑战.
- 现有的缓解策略,如思维链和检索增强生成 (RAG),不足以完全解决这些核心问题.
研究的目的:
- 审查医学研究中LLM的可靠性挑战.
- 评估当前的减缓策略及其有效性.
- 为LLMs在科学努力中负责任地整合提供建议.
主要方法:
- 关于LLM架构和局限性的当前文献的审查.
- 对研究环境中的人类-人工智能协作进行元分析的分析.
- 综合发现,以确定LLM应用和验证的最佳实践.
主要成果:
- 尽管LLM增强了人类的能力,但在专业医疗环境中存在根本的可靠性问题.
- 先进的提示技术和RAG框架并不能完全解决精度限制.
- 当人类验证是可行的时,人类-人工智能合作最有效.
结论:
- 在医学研究中成功整合LLMs需要仔细选择工具和强大的验证机制.
- 结合技术进步与人类专业知识,特别是人类监督的平衡方法至关重要.
- 了解LLM的技术限制是最大限度地发挥其潜力的关键,同时维护科学完整性.
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