Intuitively this makes sense. When you have small models, they need to compress more by leveraging superposition and polysemanticity.

Nick is looking at a 32B model, which certainly has to compress less... Now we have 10 trillion parameter models... do they need to compress at all? As far as I can tell, experiments show that Fable has basically memorized every lat/long position on the map of the earth can can tell you at a glance whether your standing in the ocean or not based on your coordinates

The 'Exploring Image Models' section addresses that specifically, particularly the 'What Neurons Do Under Pressure' subsection.

'If it switched to superposition, we should see overlapping mixtures on top of each other, ... But that’s not what we see. The neurons stay interpretable and generalized, but coarser'

> far as I can tell, experiments show that Fable has basically memorized every lat/long position on the map of the earth … your standing in the ocean or not based on your coordinates

Memorising a small shape file of the earth’s continents can allow you to determine if a person is on land or in water.

where's this specific example/experiment from? curious to read more, never heard of this

All functional knowledge is compression and contextual decompression. Sometimes this compression can be lossless or nearly so. Compression doesn't automatically mean loss of information.