在安全数据表中实现用于文本识别任务的生成预训练变压器模型
Floris Pekel1, Gino Kalkman1, Erik Lemcke1
1TNO, Department of Risk Assessment, Prevention, Innovation and Development, Netherlands Organization for Applied Scientific Research, Princetonlaan 6, 3584CB Utrecht, The Netherlands.
Annals of work exposures and health
|November 24, 2025
概括
使用大语言模型 (LLM) 自动化安全数据表 (SDS) 数据提取可以改善化学品库存管理. 这提高了工作场所风险评估的准确性和及时性,提高了职业安全.
科学领域:
- 职业健康和安全问题 职业健康和安全问题
- 化学中的人工智能.
- 信息管理 信息管理
背景情况:
- 处理化学品的工作场所需要严格的风险评估.
- 从安全数据表 (SDS) 到库存中手动导入数据是低效的.
- 不完整或延迟的风险评估会造成重大职业危险.
研究的目的:
- 开发一条自动化管道,使用LLMs从SDS中提取数据.
- 为了简化在线系统中化学库存的管理.
- 提高工作场所风险评估的效率和准确性.
主要方法:
- 实施一个利用大型语言模型 (LLM) 的管道.
- 从SDS中自动提取关键变量 (公司名称,产品名称,危险声明).
- 与手动提取相比,LLM提取的数据准确度的比较.
主要成果:
- 该LLM管道在数据提取中实现了0.83的平均准确性.
- 从SDS中成功自动化关键数据点.
- 与手动数据导入流程相比,已经证明了效率的提高.
结论:
- 用LLM驱动的管道可以有效地自动化SDS库存管理.
- 自动数据提取支持及时和全面的风险评估.
- 这项技术有助于提高化学品处理环境中的职业安全.
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