First, thank you for your work and this post. I am not a specialist, just interested, but confused. I don’t get the significance of the results, but appreciate the thought and effort you put into this project. I am pushing back on the ’romantic framing’ that that LLMs are “blind models” that somehow develop an some degree of internal spatial understanding of Earth through pure reasoning or emergent intelligence. In this case didn’t the author in effect say to the model “given this list of numbers - which happen to be latitude and longitude pairs—access your core intelligence (learned parameters / weights / internal representations) and decide if it would represent land or water? So, how big a leap would it be for the model to “think” hmm… latitude and longitude pairs—sounds like a map. Maybe I should look it up in textual map data I have been trained on? Surely there must have been many in the models training? Surely there would be map copious amounts of text that covers land and water masses. So, given the vast bulk of text data that the model was trained on, would that not have included many forms of public access text tables of long. lat. coordinates—like public access GeoNames, Natural Earth Data, OpenStreetMap, Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG), an NASA/USGS Satellite Data?
Perhaps “discovering how a blind model sees Earth” overstates: “visualizing which geographic patterns persisted from text training data that included extensive coordinate databases.”
How do I get to understanding that the resulting elliptical blobs of land relate to internalized concepts of geographical distance, and some kind of as a natural abstraction helps in identification of continents? Cheers. Leo
First, thank you for your work and this post. I am not a specialist, just interested, but confused. I don’t get the significance of the results, but appreciate the thought and effort you put into this project.
I am pushing back on the ’romantic framing’ that that LLMs are “blind models” that somehow develop an some degree of internal spatial understanding of Earth through pure reasoning or emergent intelligence.
In this case didn’t the author in effect say to the model “given this list of numbers - which happen to be latitude and longitude pairs—access your core intelligence (learned parameters / weights / internal representations) and decide if it would represent land or water?
So, how big a leap would it be for the model to “think” hmm… latitude and longitude pairs—sounds like a map. Maybe I should look it up in textual map data I have been trained on?
Surely there must have been many in the models training? Surely there would be map copious amounts of text that covers land and water masses.
So, given the vast bulk of text data that the model was trained on, would that not have included many forms of public access text tables of long. lat. coordinates—like public access GeoNames, Natural Earth Data, OpenStreetMap, Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG), an NASA/USGS Satellite Data?
Perhaps “discovering how a blind model sees Earth” overstates: “visualizing which geographic patterns persisted from text training data that included extensive coordinate databases.”
How do I get to understanding that the resulting elliptical blobs of land relate to internalized concepts of geographical distance, and some kind of as a natural abstraction helps in identification of continents?
Cheers.
Leo