一致性和可理解性:大型语言模型预测了与健康相关的内容的普通人理解
Trevor Cohen1, Weizhe Xu1, Yue Guo1
1Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA.
Journal of biomedical informatics
|December 11, 2024
概括
大型语言模型 (LLM) 可以评估生物医学文本的一致性,提高健康素养. 这有助于识别和修复复杂的信息,使每个人都更容易理解健康内容.
科学领域:
- 生物医学通信 生物医学通信
- 健康素养研究研究健康素养研究
- 自然语言处理自然语言处理.
背景情况:
- 健康素养对于明智的健康决策至关重要.
- 文本的一致性对理解有很大影响,特别是对于以前知识较低的读者来说.
- 现有自动化方法可以使用语义接近来估计文本的一致性.
研究的目的:
- 评估自动化一致性措施与非专业人士对生物医学文本的理解之间的关系.
- 评估大型语言模型 (LLM) 在测量文本连贯性和可访问性方面的有用性.
- 确定改善文本可理解性的干预措施是否也提高了连贯性.
主要方法:
- 对生物医学文本片段应用了各种自动化的连贯性措施,包括LLM衍生指标.
- 修改了文本片段,以提高可访问性,并使用多选题测试读者理解能力.
- 与理解测试结果相关联的自动连贯性得分.
主要成果:
- 来自LLM的连贯度指标显示了与读者理解的显著关联.
- 旨在提高文本可理解性的干预措施导致了可衡量的一致性改善.
- 提高连贯性与读者更好地理解生物医学文本相关.
结论:
- 在生物医学文献中识别理解障碍时,LLM衍生的连贯性措施是有用的.
- 这些指标可以指导人工和自动化努力,以提高健康信息的可访问性.
- 提高文本的一致性是提高健康素养的可行策略.
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