评估大型语言模型在健康素养方面的有效性:一项全面的跨部门研究
Kanhai S Amin1, Linda C Mayes2, Pavan Khosla3
1Yale College, New Haven, CT, USA.
The Yale journal of biology and medicine
|April 1, 2024
概括
大型语言模型 (LLM) 通过生成适合年龄的解释,有望提高儿童的健康素养. 在与较低的年级水平作斗争的同时,LLMs可以为年长的儿童和青少年调整内容.
科学领域:
- 儿科健康 儿科健康
- 医疗保健中的人工智能
- 卫生识字研究 研究 卫生识字研究
背景情况:
- 在儿童中提高健康素养与更好的长期健康结果有关.
- 很少有干预措施有效地提高了儿童的健康素养.
- 调查像大型语言模型 (LLM) 这样的新工具至关重要.
研究的目的:
- 评估LLMs在改善儿童健康素养方面的潜力.
- 为了评估在儿科条件下LLM产生的内容的阅读等级水平 (RGL).
- 确定LLM调整健康信息以适应不同年级水平的能力.
主要方法:
- 使用26个提示在ChatGPT-3.5,ChatGPT-4,微软Bing和Google Bard (现在是Google Gemini) 中测试了儿科条件.
- 使用枪雾,弗莱什-金凯德,ARI和科尔曼-利亚乌指数测量输出读数等级水平.
- 评估了词数和LLM适应特定年级水平的适应性.
主要成果:
- 所有经过测试的LLM通常在基本提示中产生与第十级RGL或以上的输出.
- 课程学习者表现出不同的能力,可以根据特定的学年级 (1至12年级) 量身定制答案.
- 与ChatGPT-3.5和Google Bard相比,ChatGPT-4和微软Bing在调整RGL方面表现更好,在六年级RGL以下发现了挑战.
结论:
- LLM为青少年提供了一种潜在的机制,以访问和理解各种复杂度级别的健康信息.
- 士课程能够产生超过六年级RGL的内容,这对健康教育来说是有前途的.
- 需要进一步的研究来验证儿童健康素养LLM的准确性和有效性.
关键词:
人工智能的人工智能聊天GPT 聊天 在GPT 聊天谷歌巴德 (Google Bard) 是一个谷歌的双子座是双子座.卫生识字 卫生识字大型语言模型微软微软的Bing Bing是什么意思儿科 儿科 儿科阅读等级水平 阅读等级水平更多相关视频
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