alt Hacker News> The difference in an attention layer is that the query is matched to each key using a dot product, and rather than getting a single value back you get a weighted sum of values.
I understand you meant to compare it to dicts, but this is more like (the only?) similarity to outer product memory. Worth calling out since GP is confused about this connection specifically.
Yes, the main problem with this whole way of thinking about transformers and self-attention is that when you look at how they are actually implemented (i.e. what Q, K, and V are applied to at each layer, and consider the loss function during training), then the analogy to memory quickly starts to look tenuous and far-removed from what is actually happening.
Mainly, since self-attention layers operate on deeply-stacked representations of the outputs of previous layers, and since training has a loss function that doesn't make the updates work in the way the article says, the analogy to memory and lookup tables breaks down quite strongly. IMO, thinking of Q, K and V as memory or key-value lookup is thus not actually very helpful for understanding what these layers are really doing or why they work. Others agree (e.g. https://news.ycombinator.com/item?id=46535412), so don't feel bad if you think there is something hand-wavey about the lookup-table and memory explanations - it is only a weak analogy.
This has come up on HN before, and probably it is most accurate (but less intuitive, sadly) to see self-attention as a generalization of kernel methods (https://news.ycombinator.com/item?id=46535549). Broadly, self-attention is just one of the simplest ways to model similarities / correlations between all inputs (i.e. kernel method), but there are many, many other attention mechanisms. There are also many other ways to implement memory (e.g. LSTMs), and many other architectures that might in fact have even more memory capacity than transformers.
That being said, this is still truly an excellent article and it really provides some great intuitions for why transformers might work as well as they do. The softmax / exponentiation here functioning to increase memorization capacity by suppressing weak activations is super clear and interesting, and I really appreciated it. I suspect is not really be what is happening or is actually important about it, given other research (https://arxiv.org/html/2410.18613v1), and because much of what works in DL is usually more about matrix conditioning and keeping gradients well-behaved generally, rather than what the actual bare final math / weights in isolation from training would tend to suggest. But it does give a great intuition for why a trained transformer can have the capacity it does.