arXiv:2607.07047v1 Announce Type: new Abstract: Understanding the geometric structure of pre-trained language model embeddings matters for interpretability and safety. We ask whether sentence-level classification signal lives in the Riemannian geometry of contextual token embeddings, and probe it by extracting per-token pullback metrics from a learned encoder's analytical Jacobian and aggregating them with the Fr\'echet mean on the symmetric positive definite (SPD) manifold; we call this procedure Riemannian Mean Pooling (RMP). Across three datasets with non-trivial linguistic structure (CoLA,
The increasing complexity and opacity of large language models necessitate deeper understanding of their internal representations for improved performance, safety, and interpretability, driving new research methods.
Understanding the geometric structure of language model embeddings is crucial for developing more interpretable, robust, and potentially safer AI systems, impacting how models are designed and deployed.
New methodologies like Riemannian Mean Pooling offer a more sophisticated way to analyze and leverage the inherent geometric properties of AI language models, moving beyond simpler linear interpretations.
- · AI researchers
- · NLP developers
- · AI interpretability platforms
- · Academic institutions
- · Black-box AI approaches
- · Companies with opaque AI models
Improved interpretability and safety mechanisms for pre-trained language models become possible.
This foundational understanding could lead to the development of more efficient and contextually aware AI agents.
Enhanced interpretability might reduce regulatory friction for advanced AI deployments by providing clearer insight into decision-making processes.
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Read at arXiv cs.CL