arXiv:2606.07183v1 Announce Type: new Abstract: This work examines the semantic geometry underlying NLP models. We compare supervised vector embeddings, such as CamemBERT, with lexical co-occurrence graphs that encode semantic relations more directly. While transformer-based embeddings achieve strong performance, their induced geometries often display unsatisfactory distributions. In contrast, graph-based models reveal a clearer and more human-readable organization of meaning. We have implemented a methodology that allows us to perform a comparative analysis either based on the structure of th
The rapid advancement and widespread deployment of transformer-based AI models necessitate deeper understanding of their underlying mechanisms and potential limitations, particularly regarding semantic representation.
Improving the interpretability and validity of semantic geometries in AI models is crucial for developing more reliable, robust, and human-aligned AI, impacting everything from search to autonomous agents.
This research suggests a potential shift towards hybrid AI models that combine the performance of vector embeddings with the interpretability of graph-based semantic representations.
- · AI researchers
- · NLP developers
- · Companies building explainable AI
- · Purely black-box AI models
- · Developers ignoring interpretability
Further research into graph-based AI models and hybrid architectures will likely increase.
New tools and frameworks might emerge to visualize and debug semantic spaces within complex AI systems.
Increased trust and adoption of AI in critical domains due to improved interpretability could accelerate.
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