Clinfo.ai:一个开源检索增强的大型语言模型系统,用于使用科学文献来回答医学问题
Alejandro Lozano1, Scott L Fleming, Chia-Chun Chiang
1Department of Biomedical Data Science, Stanford University, Stanford, CA, USA*Equal Contribution, lozanoe@stanford.edu.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|December 31, 2023
概括
很难跟上医学文献的发展. 本研究介绍了Clinfo.ai,一个开源工具,以及一个新的数据集 (PubMedRS-200) 来评估临床问题答案的大型语言模型 (LLM) 总结系统.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 生物医学文学分析 医学文献分析
背景情况:
- 医学文献的快速增长给证据综合提出了挑战.
- 现有的大型语言模型 (LLM) 总结工具缺乏严格的评估.
- 高质量的数据集和用于评估这些工具的基准任务很少.
研究的目的:
- 为了解决评估基于LLM的临床问答系统的需要.
- 引入一个开源工具和一个用于检索增强的LLM系统的基准数据集.
- 提供一个标准化的方法来评估系统的性能,总结医学文献.
主要方法:
- 开发和发布Clinfo.ai,一个开源的WebApp用于临床问题答案.
- 信息检索和抽象总结任务的规范用于评估.
- 创建和发布PubMed检索和合成 (PubMedRS-200) 数据集 (200个问答对).
- 在PubMedRS-200.上对Clinfo.ai和其他开放领域问答 (OpenQA) 系统进行比较.
主要成果:
- Clinfo.ai提供了使用动态检索文献来回答临床问题的答案.
- 通过PubMedRS-200数据集,可以系统地评估提取增强的LLM性能.
- 报告了Clinfo.ai和其他新数据集中的系统的基准结果.
结论:
- 该研究引入了有价值的资源 (Clinfo.ai,PubMedRS-200) 来评估基于LLM的医学文献总结.
- 开源工具和标准化基准对于推进临床决策支持至关重要.
- 这项工作有助于对人工智能系统在合成医学证据方面的严格评估.
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