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 that, no, really, the description "Q-K=V" does not mean "Q minus K equals V" (the head scratching was because a bunch of their descriptions and symmetry comments really make little sense if you think "Q minus K equals V"). If you want to say that "K equals V", please spell it "K=V" :)
I am curious whether it makes any sense at all to enforce a more general linear constraint on the query, key and value attention matrices along the line of Q-K=V.
It is an entertaining paper. I admit I'm surprised that K=V appears to work as well as it does -- it seems like it's almost enforcing a sort of model where the query is a guess as to what the value is and the attention head returns a (softmaxed) value that is closest to the query's guess. Maybe it works because the sequences are short and the dimension is high and there's plenty of room for interesting results to fit in the merged key/value space.
> Maybe it works because the sequences are short and the dimension is high and there's plenty of room for interesting results to fit in the merged key/value space.
In fact, on the second last page of the paper, they discuss this very problem. There is a clear correlation between performance and increasing sequence lengths for the Q-K=V model. While limited to a tight n=3 sample between 512, 1024, 2048 lengths, the degradation decreases from 5.4% to 2.2% as context is increased, suggesting that it is unlikely shorter sequences are the reason K=V performs acceptably.
Yeah the weird notation confused me too. Their own Limitations also says their experiments are too small. I am quite curious how it will play out big now, but unironically I cannot afford the hardware lol.
These types of ablation studies are always good. However, I'm not sure how generalizable the language model findings here are.
Their 1.2B model was trained on only 10B tokens, which is less than half of the chinchilla compute optimal number. Modern overtrained 1B LLMs are trained on the order of 10T tokens (1000x more).
This is important because, from my own experience, simplifications and alternatives to standard attention can look fine in the under-trained regime but lag after over-training. This happens because attention has very little out-of-the-gate inductive bias, so it takes a lot of training for the expressiveness to really shine through.
I can't fault the authors since longer training runs cost money, but it warrants pointing out.
I'm also disappointed that they didn't report reasoning benchmark results for the Q=K-V case, since that is by far the most theoretically interesting case (in my eyes).
I'm terribly sorry, but scaling curves or GTFO. Any random pile of linear algebra works fine-ish at small scales. Very few random piles of linear algebra push the Pareto envelope at large scales.
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 that, no, really, the description "Q-K=V" does not mean "Q minus K equals V" (the head scratching was because a bunch of their descriptions and symmetry comments really make little sense if you think "Q minus K equals V"). If you want to say that "K equals V", please spell it "K=V" :)
I am curious whether it makes any sense at all to enforce a more general linear constraint on the query, key and value attention matrices along the line of Q-K=V.
It is an entertaining paper. I admit I'm surprised that K=V appears to work as well as it does -- it seems like it's almost enforcing a sort of model where the query is a guess as to what the value is and the attention head returns a (softmaxed) value that is closest to the query's guess. Maybe it works because the sequences are short and the dimension is high and there's plenty of room for interesting results to fit in the merged key/value space.
> Maybe it works because the sequences are short and the dimension is high and there's plenty of room for interesting results to fit in the merged key/value space.
In fact, on the second last page of the paper, they discuss this very problem. There is a clear correlation between performance and increasing sequence lengths for the Q-K=V model. While limited to a tight n=3 sample between 512, 1024, 2048 lengths, the degradation decreases from 5.4% to 2.2% as context is increased, suggesting that it is unlikely shorter sequences are the reason K=V performs acceptably.
Yeah the weird notation confused me too. Their own Limitations also says their experiments are too small. I am quite curious how it will play out big now, but unironically I cannot afford the hardware lol.
These types of ablation studies are always good. However, I'm not sure how generalizable the language model findings here are.
Their 1.2B model was trained on only 10B tokens, which is less than half of the chinchilla compute optimal number. Modern overtrained 1B LLMs are trained on the order of 10T tokens (1000x more).
This is important because, from my own experience, simplifications and alternatives to standard attention can look fine in the under-trained regime but lag after over-training. This happens because attention has very little out-of-the-gate inductive bias, so it takes a lot of training for the expressiveness to really shine through.
I can't fault the authors since longer training runs cost money, but it warrants pointing out.
I'm also disappointed that they didn't report reasoning benchmark results for the Q=K-V case, since that is by far the most theoretically interesting case (in my eyes).
Will be great and amusing if it actually turns out that we have been doing transformer overly-complex. The code repo is missing tho...
Gets the juices flowing though..
I'm terribly sorry, but scaling curves or GTFO. Any random pile of linear algebra works fine-ish at small scales. Very few random piles of linear algebra push the Pareto envelope at large scales.