Comment by jacomoRodriguez | Hacker News Reader

Comment by jacomoRodriguez | Hacker News Reader

jacomoRodriguez

I really enjoyed the beginning of this page, where it talked about embeddings and such. But starting with the self attention, it gets a bit fussy. How, based on what does the attention mechanism work? The similarity of embeddings can't be all of it - "it", "dog" and "bone" will alwats have the same similarity, even if the surrounding sentence changes. Can someone in simple terms describe how thus works?

Extra: the embedding explanation works great with words, but with actual tokens it gets way weirder... e.g. in German tokens are often just 1-3 characters, which do not contain any meaning in the normal sense. So the fact, that this works for example with the "hu" from "Hund", "fl" from "Flasche" and "fl" from "Fleisch" is interesting, as "Fleisch" is probably more similar to "Hund" then "Flasche". (Tokens are just examples, not sure how this wird's are broken down). I guess the solution to this is related to the question above? For this kind of embedding to be useful it is important to look at more than just the two tokens, but their complete surroundings?

Attention takes in all tokens in the sequence and outputs a new representation of the current token in context. Each layer of the transformer adds more context to the token.

I haven't read this explanation in detail and although they have some nice animations, I wouldn't go to FT to explain machine learning concepts. Here are two well known explanations that might be better:

http://jalammar.github.io/illustrated-transformer/

http://nlp.seas.harvard.edu/annotated-transformer/.

So is it analogous to how a CNN starts with fragments of images and further up the chain assembles these into objects?

Yes, I think that is a reasonable way to think about it, in my opinion. However, with the language modeling objective it predicts the next token and because of the residual connections each intermediate layer is in the same space. So, maybe it would be more accurate to say that it is an increasingly accurate representation of the next token.

thanks a lot, looking at the links right now and I think they go more in depth :)

This is the intuition that worked for me: transformers are mini search engines!

Each attention node is essentially performing a simple search on the previous text given a single word. It's kind of like Google! The actual search or "query" is the word you're looking at, the page title or "key" is the other words in the sentence, and the page/document or "value" is... also the other words in the text (each word is like its own page title). So if you imagine the whole internet is just the sentence "Hi my name is Bob", and you googled "name", you should get a results page with "Bob" at the top, followed by "my" then "is" then "Hi".

As an optimization, the transformer will actually run this "search" operation for all the words in the sentence at the same time. It then spits out a matrix saying how relevant each word is too every other word. Basically each transformer in your neural network is like a little guy who says "Hey, if you're looking at X word, here's the other words Y and Z that might be relevant in the same sentence". The rest of the NN can then use that information using more typical feed forward techniques (I'm mostly glossing over use of embeddings and other various special techniques that are used in practice here). There will be multiple transformers in a given LLM that each perform a slightly different search.

But how does a transformer know how to rank these words? In a nutshell, as we train it to predict text, signals from the error in that training prediction back propagate to the transformer node, and it gradient descends to be the best search engine it can be. Each transformer will have a slightly different random starting state and different distribution of modules further forward in the network back propagating error to it, and thus they will each converge to a slightly different "search algorithm" so to speak.

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