LLMs use a surprisingly simple mechanism to retrieve some stored knowledge

> Just because feedforward topologies with single neuron steps are the easiest to train and run on graphics cards does that really make them the actual best at accomplishing tasks?

You are ignoring a mountain of papers trying all conceivable approaches to create models. It is evolution by selection, in the end transformers won.

130T unique pages? That seems highly unlikely as that averages to over 10000 pages for each human being alive. If gp merely wants texts of interest to self as opposed to an accurate snapshot it seems LLMs should be quite capable, one day.

> the 60-70B parameters of models is basically like... just stored patterns of "if these 10 tokens in a row input, then these 10 tokens in a row output score the highest"

> Is that a good summary?

No - there's a lot more going on. It's not just mapping input patterns to output patterns.

A good starting point to understand it are linguist's sentence-structure trees (and these were the inspiration for the "transformer" design of these LLMs).

https://www.nltk.org/book/ch08.html

Note how there are multiple levels of nodes/branches to these trees, from the top node representing the sentence as a whole, to the words themselves which are all the way at the bottom.

An LLM like ChatGPT is made out of multiple layers (e.g. 96 layers for GPT-3) of transformer blocks, stacked on top of each other. When you feed an input sentence into an LLM, the sentence will first be turned into a sequence of token embeddings, then passed through each of these 96 layers in turn, each of which changes ("transforms") it a little bit, until it comes out the top of the stack as the predicted output sentence (or something that can be decoded into the output sentence). We only use the last word of the output sentence which is the "next word" it has predicted.

You can think of these 96 transformer layers as a bit like the levels in one of those linguistic sentence-structure trees. At the bottom level/layer are the words themselves, and at each successive higher level/layer are higher-and-higher level representations of the sentence structure.

In order to understand this a little better, you need to understand what these token "embeddings" are, which is the form in which the sentence is passed through, and transformed by, these stacked transformer layers.

To keep it simple, think of a token as a word, and say the model has a vocabulary of 32,000 words. You might perhaps expect that each word is represented by a number in the range 1-32000, but that is not the way it works! Instead, each word is mapped (aka "embedded") to a point in a high dimensional space (e.g. 4096-D for LLaMA 7B), meaning that it is represented by a vector of 4096 numbers (cf a point in 3-D space represented as (x,y,z)).

These 4096 element "embeddings" are what actually pass thru the LLM and get transformed by it. Having so many dimensions gives the LLM a huge space in which it can represent a very rich variety of concepts, not just words. At the first layer of the transformer stack these embeddings do just represent words, the same as the nodes do at the bottom layer of the sentence-structure tree, but more information is gradually added to the embeddings by each layer, augmenting and transforming what they mean. For example, maybe the first transformer layer adds "part of speech" information so that each embedded word is now also tagged as a noun or verb, etc. At the next layer up, the words comprising a noun phase or verb phrase may get additionally tagged as such, and so-on as each transformer layer adds more information.

This just gives a flavor of what is happening, but basically by the time the sentence has reached the top layer of the transformer it has been able to see the entire tree structure of the sentence, and only then have "understand" it well enough to predict a grammatically and semantically "correct" continuation from which it is able to predict continuation words.

Just because papers are getting published doesn't mean its actually gaining any traction. I mean we have known that time series of signals recieves plays a huge role in how bio neurons functionally operate and yet we have nearly no examples of spiking networks being pushed beyond basic academic exploration. We have known glial cells play a critical role in biological neural and yet you can probably count the number of papers that examine using an abstraction of that activity in neural net, on both your hands and toes. Neuroevolution using genetic algorithms has been basically looking for a big break since NEAT. Its the height of hubris to say that we have peaked with transformers when the entire field is based on not getting trapped in local maxima's. Sorry to be snippy, but there is so much uncovered ground its not even funny.

From the paper:

> Finally, we use our dataset and LRE-estimating method to build a visualization tool we call an attribute lens. Instead of showing the next token distribution like Logit Lens (nostalgebraist, 2020) the attribute lens shows the object-token distribution at each layer for a given relation. This lets us visualize where and when the LM finishes retrieving knowledge about a specific relation, and can reveal the presence of knowledge about attributes even when that knowledge does not reach the output.

They're just looking at what lights up in the embedding when they feed something in, and whatever lights up is "knowing" about that topic. The function is an approximation they added on top of the model. It's important to not conflate this with the actual weights of the model.

You can't separate the hallucinations from the model -- they exist precisely because of the lossy compression.