Recovering Temporal and Geographic Signals from Language Model Embeddings
Quick summary
arXiv:2609.05721v1 Announce Type: new Abstract: Understanding whether language-model embeddings encode structured real-world information is important for both representation analysis and information retrieval. We study this question for temporal and geographic signals using a simple projection-based method that operates directly on output embeddings. Given a small set of seed examples, the method defines an axis in embedding space and ranks texts or entities by their projection onto that axis. Our approach is fully black-box and model-agnostic: it requires only embeddings, without access to mo
Key takeaways
- arXiv:2609.05721v1 Announce Type: new Abstract: Understanding whether language-model embeddings encode structured real-world information is important for both representation analysis and information retrieval.
- We study this question for temporal and geographic signals using a simple projection-based method that operates directly on output embeddings.
- Given a small set of seed examples, the method defines an axis in embedding space and ranks texts or entities by their projection onto that axis.
Why it matters
This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.
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