[kherud]

Now that context length seems abundant for most tasks, I'm wondering why sub-word tokens are still used. I'm really curious how character-based LLMs would compare. With 2 M context, the compute bottleneck fades away. I'm not sure though what role the vocabulary size has. Maybe a large size is critical, since the embedding already contains a big chunk of the knowledge. On the other hand, using a character-based vocabulary would solve multiple problems, I think, like glitch tokens and possibly things like arithmetic and rhyming capabilities. Implementing sub-word tokenizers correctly and training them seems also quite complex. On a character level this should be trivial.

[AaronFriel]

The attention mechanism is vastly more efficient to train when it can attend to larger, more meaningful tokens. For inference servers, a significant amount of memory goes into the KV cache, and as you note, to build up the embedding through attention would then require correlating far more tokens, each of which is "less meaningful".

I think we may get to this point eventually, in the limit we will want multimodal LLMs that understand images and sounds down to the pixel and frequency, and it seems like for text, too, we will eventually want that as well.

[thomasahle]

Maybe you could just use a good-old 1D-CNN for the bottom 3-4 layers. Then the model has been able to combine characters into roughly token length chunks anyway.

Just make sure to have some big MLPs at the start too, to enrich the "tokens" with the information currently stored in the embedding tables.

[yk]

> a significant amount of memory goes into the KV cache

Is there a good paper (or talk) how inference looks at scale? (Kinda like ELI-using-single-gpus)

[darby_eight]

> On a character level this should be trivial.

Characters are not the semantic components of words—these are syllables. Generally speaking, anyway. I've got to imagine this approach would yield higher quality results than the roman alphabet. I'm curious if this could be tested by just looking at how LLMs handle English vs Chinese.

[inbetween]

The minimal semantic parts of words are morphemes. Syllables are phonological units (roughly: the minimal unit for rhythmic purposes such as stress, etc)

[joaogui1]

I would say 2 big problems are:

1. latency, which would get worse if you have to sequentially generate more output

2. These models very roughly turn tokens -> "average meaning" on the embedding layer, followed by attention layers that combine the meanings, and feed forward layers that match the current meaning combination to some kind of learned archetype/prototype almost. When you move from word parts to characters all of that becomes more confusing (what's the average meaning of a?) and so I don't think there are good enough techniques to learn character-based models yet

[novaRom]

In AI music generation we have much better results with large vocabulary sizes of 10^6 order, my uneducated guess is that's because transformers are not universal pattern recognizers, they can catch patterns on a certain granularity level only.