What's the issue with character-level tokenization(I assume this would be much better at count-the-letter tasks)? The article mentions it as an option but doesn't talk about why subword tokenization is preferred by most of the big LLMs out there.
Besides that, for alphabetic languages, there exists almost no relation between form and meaning. I.e.: “ring” and “wing” differ by one letter but have no real common meaning. By picking the character or byte as your choice of representation, the model basically has to learn to distinguish ring and wing in context. This is a lot of work!
So, while working on the character or byte level saves you some embeddings and thus makes your model smaller, it puts all of the work of distinguishing similar sequences with divergent meanings on the model itself, which means you need a larger model.
By having subwords, a part of this distinguishing work already has been done by the vocabulary itself. As the article points out, this sometimes fails.
Also true for Abugida-based languages, for eg. சரம் (saram = string) vs மரம் (maram = tree), and many more. I think your intention with specifying "alphabetic languages" was to say "non-logographic languages", right?
And even in Chinese it's a fairly weak relationship. A large portion of the meanings of individual characters come from sound loan. For example the 英 in 英雄 means "hero", in 英语 means "England", an in 精英 means "flower". The relationship there is simple homophony.
On the other hand, one thing you do get with written Chinese is that "1 character = 1 morpheme" very nearly works. So mechanistically breaking a text into a sequence of morphemes can be done pretty reliably without the aid of a semantic model or exhaustive hard-coded mapping. I think that for many other languages you can't even get close using only syntactic analysis.
Written Japanese is much more ideographic than written Chinese. Japanese spelling is determined, such as it is, by semantics. Chinese spelling is determined by sound. Thus, 女的, 娘们, and 妮子, all meaning 'girl' or 'woman', have no spelling in common because they are different words, while Japanese uses 女 for "jo" and "onna" despite a total lack of any relationship between those words.
In either case, isn't this something we already do well?
And no, at least for the languages with which I'm familiar SOTA tokenizers tend to only capture the easy cases.
For example, the GPT-4 tokenizer breaks the first sentence of your post like so:
What/ do/ you/ mean/ by/ non/-m/orp/heme/ lexical/ units/?
Or in French, the word "cafetière" gets chopped willy-nilly into "c/afet/ière". The canonical breakdown is "cafe/t/ière".
The problem with pure numerical values representing a class as input to nueral network layer is that the byte encoding number is going to be very hard for the transformer to memorize exact values especially when relatively close numbers to each other often do not share much meaning. Catagories are usually encoded somehow, like a one-hot embedding layer, or more recently a learned embedding, so that these different categories can be easily distinguished (different categories are close to orthogonal).
My prediction would be that using the numerical value directly would not work at all, and using learnable embeddings would work but You would have to reserve that part of the token embedding for each token which would hurt performance a lot on non-spelling tasks relative to just letting that whole embedding represent the token however the model sees fit.
But, IDK! It would be easy enough to try! You should on a small toy model. And then try a small learnable re-usable character embedding. And write a blog post. Would be happy to coach / offer some gpu time / answer any other questions you have while building it.
And then use something like helloswag to measure how much you've lost on general text completion compared to a vanilla LLM with the same embedding size all dedicated to just the token.
For example “parent” and “parents” are aligned, they share letters in the same position, but “skew” and “askew” share no letters in the same position.
you can hypothetically try to ameliorate this by other means, but if you just naively drop from tokenization to character or byte level models this is what goes wrong