生物医学SQL:用于基于生物医学知识的科学推理的文本对SQL
Mathew J Koretsky1,2, Maya Willey1,2, Owen Bianchi1,2
1Center for Alzheimer's Disease and Related Dementias, NIA, NIH.
ArXiv
|October 1, 2025
概括
生物医学SQL,一个新的基准,评估文本到SQL在生物医学研究的科学推理. 当前的人工智能模型在复杂的查询中扎,突出显示了在数据分析中需要改进特定领域的推理.
科学领域:
- 生物医学信息学 生物医学信息学
- 人工智能的人工智能
- 数据库管理数据库管理
背景情况:
- 生物医学研究人员对于大型结构化数据库需要先进的数据分析工具.
- 现有的文本到SQL系统缺乏复杂科学查询所需的域推理.
- 弥合自然语言问题和可执行SQL之间的差距对于科学发现至关重要.
研究的目的:
- 介绍 BiomedSQL,这是评估文本到SQL生成中的科学推理的第一个基准.
- 评估大型语言模型 (LLM) 对复杂的生物医学查询的性能.
- 为开发人工智能系统提供基础,这些系统支持科学发现,而不是结构化的生物医学数据.
主要方法:
- 开发了BiomedSQL,一个具有68,000个问题/SQL查询/答案三倍的基准.
- 在协调的BigQuery知识库中将基因疾病,奥米克和药物数据集成为基准.
- 评估了使用不同的提示策略和交互范式的各种LLM.
主要成果:
- 当前的LLM在执行科学查询方面表现出显著的绩效差距.
- GPT-3-mini实现了59.0%的准确性,而定制的BMSQL代理达到了62.6%.
- 这两种模型的性能都远低于专家基线准确率的90.0%.
结论:
- 生物医学SQL有效地突出了当前文本到SQL模型在科学推理中的局限性.
- 人工智能系统有很大的需求,具有增强的特定领域推理能力.
- 生物医学SQL数据集和代码将促进生物医学研究人工智能的进步.
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