How would this change if we augmented the Transformer so the first layer can “look back” at the final layer from the previous token?
Optimist: Now the first layer activations can express very complicated concepts that involve many steps of sequential sequencing. So perhaps this helps a lot.
Sceptical: but from the perspective of the weights that produce the first layer activations, all the new concepts still look like gobbledegook.
Optimist: True, but in this set-up the the weights are describing the learning rule, they’re not trying to “understand” the object-level content at all. It is the activations that come to “understand” the new concepts.
Well, if it’s doable after this one simple architectural change, why not via chains of thought like Alex was saying?
Firstly, you can send back detailed and novel representations from the final layer activations, but can’t do either from a new token.
Second, consider the specific weights that create the first layer activations.
With the architectural change, there are specialised weights trained to give sophisticated novel representations to the first layer (stealing them from the final layer of the previous token).
But with current transformers, the same weights that map words to existing concepts (that map the word “dog” to a complex representation of dogs in concept space) would have to do double duty and also help create sophisticated novel concepts.
One Q I have:
How would this change if we augmented the Transformer so the first layer can “look back” at the final layer from the previous token?
Optimist: Now the first layer activations can express very complicated concepts that involve many steps of sequential sequencing. So perhaps this helps a lot.
Sceptical: but from the perspective of the weights that produce the first layer activations, all the new concepts still look like gobbledegook.
Optimist: True, but in this set-up the the weights are describing the learning rule, they’re not trying to “understand” the object-level content at all. It is the activations that come to “understand” the new concepts.
Well, if it’s doable after this one simple architectural change, why not via chains of thought like Alex was saying?
Firstly, you can send back detailed and novel representations from the final layer activations, but can’t do either from a new token.
Second, consider the specific weights that create the first layer activations.
With the architectural change, there are specialised weights trained to give sophisticated novel representations to the first layer (stealing them from the final layer of the previous token).
But with current transformers, the same weights that map words to existing concepts (that map the word “dog” to a complex representation of dogs in concept space) would have to do double duty and also help create sophisticated novel concepts.
I think these two differences are pretty big.