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Nested State Clouds: Distilling Knowledge Graphs From Contextual Embeddings.pdf

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Preview of Nested State Clouds: Distilling Knowledge Graphs from Contextual Embeddings
🔗 Source: fse.studenttheses.ub.rug.nl
📊 Size: 2.63 MB
📄 Pages: 17 pages
⬇️ Downloads: 102

Summary

This thesis proposes a novel interpretability technique called Nested State Clouds (NSC) to distill sparse knowledge graphs from a model's high-dimensional embeddings using conceptors. NSC takes advantage of the way state clouds of contextual embeddings are positioned relative to each other in latent space. The technique is applied to a pretrained masked language model, recovering an ontology of concepts grounded in the model's latent space. The goal is to provide a modality-agnostic approach to interpretability, allowing for the analysis of DL models operating with various non-text modalities. The initial investigation focuses on a pretrained masked language model, with potential applications to arbitrary classification models.

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Description

This thesis proposes a novel interpretability technique called Nested State Clouds (NSC) to distill sparse knowledge graphs from a model's high-dimensional...

Technical Information

  • File Format: PDF
  • File Size: 2.63 MB
  • Pages: 17
  • Language: EN
  • Total Downloads: 102
  • Last Updated: 3 hours ago

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Nested guideNested tutorialState guideState tutorialClouds: guideClouds: tutorialDistilling guideDistilling tutorialKnowledge guideKnowledge tutorialGraphs guideGraphs tutorialfrom guidefrom tutorialContextual guideContextual tutorialEmbeddings guideEmbeddings tutorial

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