大型语言中的认识压缩模型解释了肠肝轴的解释
Man Sun1, Dan Zang1, Huan Zhou1
1Department of Oncology, The Second Hospital of Dalian Medical University, Dalian, Liaoning, China.
大型语言模型 (LLM) 可以过度简化复杂的生物系统,如肠肝轴,呈现信息过于连贯. 评估LLM输出需要关注来源和不确定性,而不仅仅是可读性.
科学领域:
- 生物医学信息学 生物医学信息学
- 人工智能在医学中的应用
- 肠 - 肝研究轴研究
背景情况:
- 肠肝轴是肠道和肝脏系统的复杂相互作用,受微生物生态和免疫调节等因素的影响.
- 它的上下文依赖性为清晰的解释带来了挑战,特别是随着生物医学话语中大型语言模型 (LLM) 的兴起.
- 评估LLM能够准确地表示复杂,开放的生物框架的能力至关重要.
研究的目的:
- 在解释肠肝轴时,系统地评估广泛使用的大型语言模型 (LLM) 的认识体系完整性和语言可访问性.
- 评估LLM产生的内容如何与医疗信息质量的既定标准保持一致.
- 确定LLM解释的可读性和事实准确性之间的关系.
主要方法:
- 对五个LLM进行了跨平台,混合方法的信息学分析.
- 关于肠肝轴的20个临床相关问题被提出,产生了100个答案.
- 使用可读性指数测量语言可访问性,使用JAMA基准标准,全球质量评分和修改后的DISCERN框架评估认识体系完整性.
主要成果:
- 对于以干预为重点的查询,LLM的回复显示语言复杂性增加,而透明度或质量没有提高.
- 信息完整性因LLM平台而异,而不是根据查询的等级域.
- 观察到高叙事清晰度,但来源归因和不确定性不足,导致过度连贯,潜在的误导性解释.
结论:
- 关于肠肝轴的LLM解释容易出现"经验压缩",其中叙事流性掩盖了事实细微差别.
- 可读性指标无法可靠地预测LLM生成的医疗信息的认识系统稳定性.
- 未来的LLM评估应优先考虑来源,不确定性校准,并明确区分相关性和临床相关性之间的因果关系.
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