自动化药监证据生成:使用大型语言模型来产生情境感知结构化查询语言
Jeffery L Painter1, Venkateswara Rao Chalamalasetti1,2, Raymond Kassekert3
1GlaxoSmithKline, Durham, NC 27701, United States.
JAMIA open
|February 10, 2025
概括
大型语言模型 (LLM) 现在可以将自然语言查询转换为用于药监数据库的SQL. 添加业务背景显著提高了准确度,从8.3%提高到78.3%.
科学领域:
- 药物监督 药物监督 药物监督
- 人工智能的人工智能
- 数据库管理数据库管理
背景情况:
- 药监数据库需要复杂的查询来检索安全数据.
- 自然语言查询 (NLQ) 往往难以转换为结构化查询语言 (SQL),用于数据库交互.
- 大型语言模型 (LLM) 提供了自动化NLQ到SQL转换的潜力.
研究的目的:
- 提高药监数据库中信息检索准确度.
- 开发一种使用LLMs将NLQ转换为SQL查询的方法.
- 评估业务环境对LLM驱动查询生成的影响.
主要方法:
- 在检索增强生成 (RAG) 框架内利用了OpenAI的GPT-4模型.
- 通过商业背景文件丰富了RAG框架.
- 评估了在不同的查询复杂度 (低,中,高) 的LLM绩效,无论在业务环境中还是没有.
主要成果:
- 从NLQ到SQL的准确性从8.3% (仅用于图表) 增加到78.3%,使用商业背景文档.
- 在所有查询复杂度级别中,准确度的改进是一致的.
- 除了高度复杂的查询外,性能达到高达85%,表明部署的巨大潜力.
结论:
- 整合业务环境显著提高了LLM准确性,用于生成可执行和语义上正确的SQL查询.
- 拟议的方法提高了非技术用户对药监数据的可访问性.
- 这种方法为改善各种数据密集型领域的数据检索提供了可转移的框架.
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