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使用LLM驱动工具在制造业的知识共享:用户研究和模型基准测试
Samuel Kernan Freire1, Chaofan Wang1, Mina Foosherian2
1Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands.
Frontiers in artificial intelligence
|April 11, 2024
概括
本研究介绍了一个大型语言模型 (LLM) 系统,以改善工厂的知识共享. 虽然LLM提高了信息获取,但人类专家仍然是复杂学习的首选,GPT-4领先于绩效.
科学领域:
- 制造业 制造技术 制造技术
- 人工智能的人工智能
- 知识管理知识管理
背景情况:
- 制造业务越来越需要知识密集型,迫使操作员培训和支持.
- 有效的知识共享对于工厂效率和操作员发展至关重要.
研究的目的:
- 开发和评估基于大型语言模型 (LLM) 的系统,用于在工厂中检索和共享知识.
- 评估系统对运营商效率和信息获取的影响.
- 为了对各种LLM进行基准测试,以确定它们在制造业知识管理中的适用性.
主要方法:
- 开发基于LLM的系统,以访问工厂文档和专家知识.
- 在工厂环境中进行的用户研究,以评估系统采用和感知到的好处.
- 商业和开源大型语言模型 (LLM) 的基准测试.
主要成果:
- 该LLM系统在更快的信息检索和问题解决方面展示了好处.
- 运营商表示,他们更喜欢在可用时向人类专家学习.
- 在经过测试的LLM中,GPT-4表现优越,开源模型提供了竞争力的替代方案.
结论:
- 基于LLM的系统为提高制造业知识管理提供了一个有希望的方法.
- 将人工智能支持与人类专业知识相平衡,是有效操作员培训的关键.
- 进一步研究优化LLM在工业环境中的应用是有必要的.
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