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生成式检索增强的本体图和多代理策略,用于解释大型语言基于模型的材料设计设计.

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大型语言模型 (LLM) 可以通过检索信息和生成假设来帮助材料工程. 将LLM与知识图相结合,可以提高准确性,并揭示材料设计的机械洞察力.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 人工智能的人工智能
  • 计算工程 计算工程

背景情况:

  • 包括大型语言模型 (LLM) 在内的变压器神经网络在材料分析,设计和制造方面显示出潜力.
  • LLM可以处理各种数据类型,如语言,符号,代码和数值数据.

研究的目的:

  • 探索LLM作为支持材料工程分析的工具.
  • 调查信息检索,假设生成和机械关系发现中的LLM能力.
  • 开发和评估人工智能代理策略,使用LLM进行材料分析和设计问题.

主要方法:

  • 利用精心调整的LLM,MechGPT,受过材料数据力学方面的培训.
  • 实施了检索增强的本体知识图策略,以解决LLM的局限性,如幻觉和信息回忆.
  • 采用基于代理的建模和非线性抽样策略来增强生成品质.

主要成果:

  • 微调可以提高LLM对特定领域的理解,但不能防止学习背景之外的错误.
  • 检索增强的知识图表显著提高了LLM的生成性能,并提供了机械的见解.
  • 知识图中的相关性功能比标准的检索增强提供了优势,提高了LLM的性能和可解释性.

结论:

  • 随着知识图的增强,LLM为材料工程任务提供了强大的支持,包括设计和分析.
  • 知识图表提高了LLM的解释性,并允许整合新的数据源.
  • 基于代理的建模等先进策略进一步提高了复杂的生成任务和主动学习的LLM能力.