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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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了解嵌入模型的生成AI输出.

Max Vargas1, Reilly Cannon1, Andrew Engel1

  • 1Pacific Northwest National Laboratory, Richland, WA 99354, USA.

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概括
此摘要是机器生成的。

深度神经网络 (DNN) 自动创建称为嵌入的数据特征. 应用于这些嵌入式的尺寸缩小技术可以揭示数据差异,区分真实与人工智能生成的样本.

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科学领域:

  • 数据科学数据科学数据科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 特性工程对于定量分析至关重要.
  • 传统上,功能工程依赖于领域专业知识.
  • 深度神经网络 (DNN) 为功能工程提供了一种自动化的方法.

研究的目的:

  • 探索DNN嵌入的维度减小的实用性.
  • 调查基础模型中的嵌入是否包含可解释的信息.
  • 使用这个框架来评估真实和人工智能生成的数据的分离性.

主要方法:

  • 利用深度神经网络 (DNN) 来从输入数据中生成嵌入式.
  • 应用主要组件分析 (PCA) 和其他尺寸缩小技术在这些嵌入式上.
  • 分析了固有的数据结构的低维表示结果.

主要成果:

  • 尺寸缩小技术成功地揭示了数据嵌入中的异质性.
  • 揭露的数据结构与人类可以理解的解释相符.
  • 经验证据显示,嵌入空间内的真实样本和人工智能生成样本之间有明显的分离性.

结论:

  • DNN嵌入式,当用维度减小来分析时,提供了对数据异质性的洞察.
  • 这种方法提供了一种方法来区分真实数据和人工智能生成的数据.
  • 基础模型嵌入包含丰富的,可解释的信息,易于分析.