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Comment by nitnelave | Hacker News Reader

I was trying to keep the article at a level that everyone understands, from middle school up. I thought about going a bit deeper in the structure and mentioning attention, but my problem is that the intuitive concept of "attention" is quite different from the mathematical reality of an attention layer, and I'm sure I would have lost quite a few people there. It's always a trade-off :)

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Indeed the word 'attention' implies some active process, which is also unfortunate, because it is really similarity maps over long ranges. This process apparenty creates a cross-reference maps of items that are commonly co-activated. But then what do the different heads do? Some kind of different "modalities of cross-reference"?

I struggle to understand why this thing works the way it does. It's possible that Vaswani et al. have made one of the greatest discoveries of this century that solved the language problem in an unintuitive, and yet very unappreciated way. It's also possible that there are other architectures that can simulate the same level of intelligence with such large numbers of parameters.

I think you re right that it's not intuitive, it's like basic arithmetic is laughing at us

> I struggle to understand why this thing works the way it does.

I'm not in this field but have recently found myself going on the deepest dive possible into it as my small brain can absorb.

I now know about (on a surface level) neural networks, transformers, attention mechanisms, vectors, maticies, tokenization, loss functions and all sorts of other crazy stuff.

I come out of this realizing that there are some incredibly brilliant minds behind this. I knew AI was a complex subject but not on the level I've learned about now. To get what is essentially matrix multiplications to learn complex patterns and relationships in language is mind-blowing.

And it's creative. It can have a rap battle with an alter-ego, host a quiz party with other AIs of varying personalities, co-author a short story with me, respond to me only in emojis. The list is seemingly endless. Oh, and it can also do useful things. It's my programming companion too.

And we're just getting started.

Got a good Youtube list? Other than the HN threads and submissions I can look up this weekend.

this series is good https://www.youtube.com/watch?v=Nw_PJdmydZY

Multi-head attention just means that you're looking at all the words at once rather than only looking at one word at a time, and using that to generate the next word. So instead of using attention only on the last word you also have attention on the penultimate word and the one before that and the one before that, etc. I think it is fairly obvious why this gives better results than say an RNN – you are utilizing context better than in a recurrent system, which is also just closer to how a human brain works. When you read/write a sentence you're not really going one word at a time, you're thinking about all the words at once, even if the last word is technically the most important.

The other clear benefit of transformers over an arch like RNNs (and what has probably made more of a difference imo) is that its properly parallelizable, which means you can do huge training runs in a fraction of the time. RNNs might be able to get to a level of coherence that approaches GPT-3, but with current hardware that would be very time-prohibitive.

That's not what multi-head attention means. Multi-head attention is the use of learned projection operators to perform attention operations within multiple lower-dimensional subspaces of the network's embedding space, rather than a single attention operation in the full embedding space. E.g. projecting 10 512-D vectors into 80 64-D vectors, attending separately to the 8 sets of 10 embedding projections, then concatenating the results together to reform 10 512-D vector outputs.

In fact the projection operations are the only learned part of a Transformer's self-attention function -- the rest of self-attention is just a weighted sum of the input vectors, where the weights come from the (scaled) vector correlation matrix.

How is that different from what I said?

So in training, chatgpt turned words into embedding , and given context window N , looked at N embeddings an created a probabilities list for the following next embedding ?

And if I tell it something that was excatly in it's trained context windows, I get the most likely next word and the one after itm

But what happens if I ask it something slighty different than it's training context ? Or something largely different?

By "embedding" in this context what you're actually referring to is called a "token" which are sub-word strings of usually 1-4 characters.

It's not possible for you to ask it things even slightly different from it training data, unless you ask exclusively in emojis that didn't exist yet when it was trained (in which case it sees nothing, just like when someone sends you an emoji your phone doesn't support).

Any novel sentence and even novel words like "Blobdarfnk" ARE in its training data. "Blobdarfnk" is encoded as the five tokens Bl, ob, dar, fn, and k.

No worries, readers can just ask ChatGPT 'what's the meaning of attention in the context of LLM design' and go from there [edit also temperature].

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