Summary of Unveiling Ontological Commitment in Multi-modal Foundation Models, by Mert Keser et al.
Unveiling Ontological Commitment in Multi-Modal Foundation Models
by Mert Keser, Gesina Schwalbe, Niki Amini-Naieni, Matthias Rottmann, Alois Knoll
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a method for extracting the learned superclass hierarchy from deep neural networks (DNNs) for a given set of leaf concepts, enabling the validation and verification of DNNs’ qualitative reasoning models. The authors leverage multimodal foundation models to learn rich representations of concepts and relations, but these learned representations are currently opaque, preventing easy inspection or adaptation against available ontological commitment models. To address this challenge, they develop a three-step approach: obtaining leaf concept embeddings using the DNN’s textual input modality, applying hierarchical clustering to capture semantic similarities via vector distances, and labeling parent concepts using search in available ontologies from qualitative reasoning. The authors demonstrate the effectiveness of their method by extracting meaningful ontological class hierarchies from state-of-the-art foundation models and validating and verifying a DNN’s learned representations against given ontologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to extract and understand the learned knowledge from deep neural networks, which is important for building reliable and trustworthy AI systems. The authors develop a method that can extract the superclass hierarchy of concepts from these networks, allowing us to validate and verify their reasoning abilities. This research has potential applications in fields like artificial intelligence, natural language processing, and cognitive science. |
Keywords
» Artificial intelligence » Hierarchical clustering » Natural language processing
