在用精心调整的大型语言模型替换超音符后,生物医学文本可读性
Karl Swanson1, Shuhan He2, Josh Calvano3
1Department of Medicine-Clinical Informatics, University of California-San Francisco, San Francisco, United States of America.
PLOS digital health
|April 16, 2024
概括
大型语言模型 (LLM) 通过用更简单的同义词取代困难的术语来简化复杂的生物医学文本. 这提高了患者对健康信息的理解,使医疗内容更容易获得.
科学领域:
- 生物医学信息学 生物医学信息学
- 自然语言处理自然语言处理.
- 卫生识字 卫生识字
背景情况:
- 患者访问在线医疗信息需要简化文本.
- 生物医学文本理解是具有挑战性的,因为专门的词汇.
- 改善患者的理解对于健康参与至关重要.
研究的目的:
- 通过使用大型语言模型 (LLM) 调查生物医学文本的简化.
- 评估超名词替换对提高文本可读性的有效性.
- 评估生物医学文本处理的不同LLM变体.
主要方法:
- 精心调整的三个LLM变体用于复杂的单词/短语用超音符替换.
- 从统一医疗语言系统 (UMLS) 处理了1000个生物医学定义.
- 使用可读性指标和句子复杂度指标评估文本更改.
主要成果:
- 这三种LLM方法都提高了生物医学文本的可读性,并减少了句子的复杂性.
- 可读性从大学水平转移到美国高中水平.
- 在句子复杂度方面,GPT-J-6b表现出最显著的改善.
结论:
- 超音符替代是一种可行的方法来简化复杂的生物医学文本.
- 微调的开放式LLM对于生物医学NLP任务是有效的.
- 简化医疗信息提高了公共卫生素养和参与度.
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