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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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有证据的多源免费无监督域名适应.

Jiangbo Pei, Aidong Men, Yang Liu

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    本研究介绍了一种基于证据的学习方法,用于多源无监督域调整 (MSFUDA),以改进来自多个模型的知识聚合. 拟议的证据聚合和适应框架 (EAAF) 通过解决域级和地方结构挑战,实现了最先进的结果.

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    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 多源无监督域调整 (MSFUDA) 旨在利用来自多个源模型的知识进行目标域调整.
    • 现有的MSFUDA方法在有效汇集来自不同来源的知识和防止不可靠的语义传播方面面临挑战.
    • 低于最佳的域级聚合和基于局部结构的有风险的语义传播阻碍了性能.

    研究的目的:

    • 提出一种新的基于证据的学习方法,以解决MSFUDA的局限性.
    • 开发精细的实例级知识聚合和可靠的语义传播.
    • 通过一个强大的框架,提高MSFUDA的绩效.

    主要方法:

    • 制定了证据预测不确定性 (EPU),以捕捉样本模型适合不确定性,用于实例级聚合.
    • 开发了一个基于EPU的多源聚合模块,用于精细的知识集成.
    • 引入了证据相邻性-一致性不确定性 (EAU) 以在目标域样本中进行可靠的一致性测量.
    • 设计了一个EAU引导的局部结构挖矿模块,以实现可靠的语义传播.
    • 将这些组件集成到证据聚合和适应框架 (EAAF) 中.

    主要成果:

    • 拟议的EPU指标有效指导实例级聚合,允许目标样本表达对不同源模型的偏好.
    • 该EAU指标确保了相邻的目标样本之间强大的一致性,促进了可靠的语义传播.
    • 与现有方法相比,EAAF框架显示出更高的性能.
    • 在三项MSFUDA基准指标上取得了最先进的结果.

    结论:

    • 提出的循证学习方法有效地解决了MSFUDA的关键挑战.
    • 欧亚联网提供了一个强大的框架,用于精细的知识聚合和可靠的语义传播.
    • 该方法显著提升了多源无监督域名适应的最新技术.