Hint for authors: when discussing linear algebra (or really most other kinds of math), follow normal conventions. In this case, the convention would be that - (the minus sign) means subtraction. It does not mean "and also", especially when you sandwich it between two variables that represent matrices. I read the paper with much head scratching all the way through sections 1 and 2 and part of 3 before I figured out th…
Do transformers need three projections? Systematic study of QKV variants
51–54 of 54 posts
Re: Do transformers need three projections? Systematic study of QKV variants
#52Hint for authors: when discussing linear algebra (or really most other kinds of math), follow normal conventions. In this case, the convention would be that - (the minus sign) means subtraction. It does not mean "and also", especially when you sandwich it between two variables that represent matrices. I read the paper with much head scratching all the way through sections 1 and 2 and part of 3 before I figured out th…
I think the primary reason it works is because the difference between K and Q, which is not all that obvious is that it’s allowing the model to have an asymmetric relationship between tokens, so one token can attend to another without the reverse being true. It seems to me if you just have a single value that you’re representing symmetric relationship, which might degrade the quality of reasoning over a set of tokens…
Re: Do transformers need three projections? Systematic study of QKV variants
#53Re: Do transformers need three projections? Systematic study of QKV variants
#54I can see why the QKV gets used but I can't help but think that thete's got to be a better mechanism with turning a pair of vectors into a new vector and a significance field. Geometrically I imagine the process of attention like picking up a bunch of vectots and spinning and squishing them in many-D until you can find a crack where you can see all the way through, then leveraging that crack to seperate what you want…
There is.
Transformers are basically autoencoders on the decode step - they take a compressed set of information and expand it into a 3 matrices which then get combined back into one matrix.
You can unroll the entire self attention step into fully connected layers, just with a lot of zeros for things that don't get multiplied together.
So it stands to reason that there is probably an optimal form of weights that does the same thing as current transformers.