文学搜索沙箱:一个大型语言模型,用于生成系统评论的搜索查询
Gaelen P Adam1, Jay DeYoung2, Alice Paul3
1Center for Evidence Synthesis in Health, Brown University School of Public Health, Providence, RI 02903, United States.
JAMIA open
|September 26, 2024
概括
大型语言模型 (LLM) 可以从标题和关键问题中生成布尔式搜索查询,用于从标题和关键问题中进行系统审查 (SR). 虽然不取代专家设计,但这些人工智能生成的查询可以帮助关键字建议和初始搜索框架.
科学领域:
- 图书统计学 图书统计学
- 人工智能的人工智能
- 信息科学 信息科学 信息科学
背景情况:
- 系统审查 (SRs) 需要全面的搜索查询,这个过程往往耗时.
- 大型语言模型 (LLM) 为自动化复杂信息检索任务提供了潜在的解决方案.
研究的目的:
- 开发和评估基于LLM的方法,用于从SR标题和关键问题生成布尔式搜索查询.
- 评估微调的LLM在创建SRs有效搜索策略方面的表现.
主要方法:
- 来自PROSPERO的10,346个SR搜索查询的数据集被精选为微调.
- 使用此数据集,Mistral-Instruct-7b模型进行了微调.
- 通过图书馆员采访,对模型的性能进行了定量和定性评估.
主要成果:
- 模型生成的查询实现了 85% (IQR 40%-100%) 的中位灵敏度.
- 需要阅读的中位数是1206个引用 (IQR 205-5810).
- 定性反表示模型对于主题范围有用,但需要专家的精确审查.
结论:
- 通过提供关键字建议和查询框架,LLM可以帮助生成SR的布尔搜索查询.
- 开发的数据集对于培训和评估LLMs在这个领域是有价值的.
- 需要进一步的研究来改善数据集质量和模型性能.
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