生物医学文本的简单语言改编使用LLMs:评估指标的比较
Primoz Kocbek1,2, Leon Kopitar1, Gregor Stiglic1,3
1University of Maribor, Faculty of Health Sciences, Maribor, Slovenia.
Studies in health technology and informatics
|August 8, 2025
概括
大型语言模型 (LLM) 可以简化复杂的生物医学文本,以提高健康素养. 与其他方法相比,gpt-4o-mini模型在简化文本方面表现优异.
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.
- 卫生沟通健康沟通
背景情况:
- 生物医学文本由于其复杂性,往往对健康素养构成障碍.
- 简化这些文本对于改善公众对健康信息的理解至关重要.
研究的目的:
- 调查大型语言模型 (LLM) 在简化生物医学文本方面的有效性.
- 为了比较不同的LLM方法,包括提示模板,AI代理和微调,以简化文本.
主要方法:
- 利用公共数据集对生物医学摘要的简单语言调整.
- 开发并评估了基线 (提示模板),两个AI代理,以及微调方法.
- 采用了定量指标 (弗莱什-金凯德,SMOG,SARI,BERTScore,G-Eval) 和定性指标 (利克特尺度).
- 选择的OpenAI gpt-4o和gpt-4o迷你模型进行评估.
主要成果:
- 在简化生物医学文本方面,gpt-4o-mini模型表现出卓越的性能.
- 与其他方法相比,微调 (FT) 方法表现不佳.
- 基于LLM的G-Eval指标显示出有希望的结果,与定性评估保持一致.
结论:
- 通过简化生物医学文本,LLM,特别是gpt-4o-mini,显示了通过简化生物医学文本来提高健康素养的巨大潜力.
- 该G-Eval指标提供了一个可靠的,LLM驱动的方法来评估文本简化质量.
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