What does the input side of the neutral network look like? Is it enough bits to represent N tokens where N is the context size? How does it handle inputs that are shorter than the context size?
I think embedding is one of the more interesting concepts behind LLMs but most pages treat it as a side note. How does embedding treat tokens that can have vastly different meanings in different contexts - if the word "bank" were a single token, for example, how does embedding account for the fact that it can mean river bank or money bank? Do the elements of the vector point in both directions? And how exactly does embedding interact with the training and inference processes - does inference generate updated embeddings at any point or are they fixed at training time?
(Training vs inference time is another thing explanations are usually frustrating vague on)
Not quite. The raw text converted into IDs corresponding to tokens by the tokenizer. Each token maps onto a vector, via a so-called embedding lookup (I always thought the word choice embedding was weird, but it's a standard).
This vector is then augmented with further information, such as positional and relational information, which happens inside the model.
The context is not a bitfield of tokens. It's a collection of vectors that are annotated with additional information by the model. The context size of a model is a maximum usable sequence length, it's not a fixed input array.
> if the word "bank" were a single token, for example, how does embedding account for the fact that it can mean river bank or money bank? Do the elements of the vector point in both directions?
The vector mapped to "bank" sorts the token into a very high dimensional space that points at all kinds of areas. These mappings are unlabeled, they are learned relationships between concepts. So the embedding vector derived from the token "bank" achieves most of its semantic meaning contextually, by the model putting it into relation to its interpretation of the source text. This is part of the relational annotations I mentioned earlier.
> you end up with about 44 terabytes — roughly what fits on a single hard drive
No normal person would think that 44 TB is a usual hard drive size (I don't think it even exists ? 32TB seems the max in my retailer of choice). I don't think it's wrong per se to use LLM to produce cool visualization, but this lack of proof reading doesn't inspire confidence (especially since the 44TB is displayed proheminently with a different color).
learningToFly33•3h ago