大型语言模型的检索增强用于普通语言生成
Yue Guo1, Wei Qiu2, Gondy Leroy3
1Biomedical and Health Informatics, University of Washington, United States of America.
Journal of biomedical informatics
|January 1, 2024
概括
生产生物医学研究的普通语言摘要需要背景解释. 采用维基百科的Retrieval-Augmented Lay Language (RALL) 生成,提高了总结质量和简单性,以更广泛地传播科学知识.
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.
- 科学沟通科学沟通
背景情况:
- 生物医学文献的复杂性阻碍了公众的理解.
- 现有的自动化方法专注于总结/简化,忽视了背景解释.
- 有效的背景解释对于普通人的理解至关重要,但未被充分探索.
研究的目的:
- 引入Retrieval-Augmented Lay Language (RALL) 代码,以提高生物医学文本的简化能力.
- 开发和评估添加背景解释到简要总结的方法.
- 目前的CELLS,是一个大型的平行语料库,用于普通语言的生成.
主要方法:
- 开发了RALL生成,将信息检索与文本生成模型集成在一起.
- 使用UMLS和维基百科用于术语定义和解释嵌入.
- 通过使用最先进的文本生成模型评估RALL模型,包括Llama 2和GPT-4.
- 创建并使用CELLS体 (63k对) 跨越12个期刊.
主要成果:
- 基于嵌入的RALL模型显著提高了摘要质量和简单性.
- 维基百科证明了背景解释的宝贵来源.
- 大型语言模型 (LLM) 显示出潜力,但需要进一步改进以获得最佳的摘要质量.
- 拉尔在增强普通语言生成背景解释方面表现出有效性.
结论:
- RALL生成是使生物医学文献易于获取的有希望的方法.
- 整合外部知识来源,如维基百科,可以增强普通语言的摘要.
- 需要进一步的研究来优化LLM在科学内容的背景解释方面的表现.
- 这项工作为更广泛的科学知识传播奠定了基础.
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