2) Language is already a higher-order lossy compressed abstraction made by humans to communicate fast and fill out the left out information with a learned prior. If you train a model on language only, it will not have the opportunity to have a non-compressed representation to make its own abstraction from.
3) If you are LLM-pilled and believe that we will be able to reach arbitrary levels of precise informational representation using language only, and that all abstractions that we may ever want can live on every single embedding layer in an LLM, your argument is fair.
is this not true also of humans?
2) The idea that there is a non-compressed prior is already philosophical no? It seems to just be a rehash of whether an example of a "Chair" is a representation of a perfect Idea of a chair, or whether accidental things put together read as a chair. Its just dualism or not, Plato vs aristotle but for LLMs vs human learning.
If we do happen to just form an abstraction from less examples than an LLM, then its a question of efficiency in the learning algo and the capability to fill the gaps faster/better.
Obvs a world model and multi modal learning would close the gap between LLM latent space and what the human mind encodings might look like. But you can formulate a reality where humans do not have access to a non compressed representation (or even say that doesnt exist) and we are just more efficient at abstraction and recollection
3) There are multiple answers to this. One is that language can just be a prong in the learning modes available. Another is that language is many times redundant and includes supplemental information and nuances that despite being a higher order abstraction has enough additional information to reconstruct entire messages even after the lossy transmission.
Btw I am pretty against LLMs as path forward, but I have been interested in their ability to solve problems with Convergent solutions and their learning model as a lossy compression algorithm where instead of saving the actual data, you save a very good guesser of the next word. So if a text is part of its traning algo then you get almost a 1:1 (like a zip dictionary) but if its not, then he gives you something that plausibly could be in an imaginary Zip with that title.