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Related Experiment Video

Updated: Jan 10, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1000

Understanding generative AI output with embedding models.

Max Vargas1, Reilly Cannon1, Andrew Engel1

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

Science Advances
|November 26, 2025
PubMed
Summary
This summary is machine-generated.

Deep neural networks (DNNs) automatically create data features called embeddings. Dimensionality reduction techniques applied to these embeddings can reveal data differences, distinguishing real from AI-generated samples.

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Area of Science:

  • Data Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Feature engineering is crucial for quantitative analysis.
  • Traditionally, feature engineering relied on domain expertise.
  • Deep neural networks (DNNs) offer an automated approach to feature engineering.

Purpose of the Study:

  • To explore the utility of dimensionality reduction on DNN embeddings.
  • To investigate if embeddings from foundation models contain interpretable information.
  • To assess the separability of real and AI-generated data using this framework.

Main Methods:

  • Utilized deep neural networks (DNNs) to generate embeddings from input data.
  • Applied principal components analysis (PCA) and other dimensionality reduction techniques to these embeddings.
  • Analyzed the resulting low-dimensional representations for inherent data structures.

Main Results:

  • Dimensionality reduction techniques successfully uncovered heterogeneity in the data embeddings.
  • The revealed data structures corresponded to human-understandable explanations.
  • Empirical evidence showed clear separability between real and AI-generated samples within the embedding space.

Conclusions:

  • DNN embeddings, when analyzed with dimensionality reduction, offer insights into data heterogeneity.
  • This approach provides a method for distinguishing between real and AI-generated data.
  • Foundation model embeddings contain rich, interpretable information amenable to analysis.