If I understand your question right, this is one of the reasons BPE is nice and the parent liked it. For any character sequence, provided the characters are in the alphabet used to create the BPE vocab, there are no unknown words/sequences. One downside of some previous tokenization methods is you could have unknown/UNK tokens, EG dictionary based methods.

In our paper with bytes, we also avoid the UNK issue, since we can have an embedding for every possible byte, since it’s not that many (and for sequences of bytes we use hash embedding, although we did test n-gram lookups for the top K frequent byte n-grams in the training data).

Good description! Maybe what parent got mixed up on is an alternate way to view this is trying to chunk bytes to have roughly similar information. EG we initially tried a bunch of patching schemes, EG, keep a running total of entropy until the total exceeds a threshold, but ended up finding simple things worked better.

I’ll see if we can add more information about the small CNN in a next update to arXiv paper.

Related thought, I think BPE is quite a good, cheap inductive bias to have in a model, which is part of what made it challenging to scale better against. I also suspect this is part of why with less training FLOPs BPE is better (left side of figure 1), BLT has to expend some of its FLOPs budget to recover/learn some of this useful bias. With more training FLOPs this becomes a smaller fraction of the budget though leading to better scaling.

There is at least some work on character based modeling, but it hasn’t scaled well before. The challenge I think with something more adhoc for exceptional tokens is that it’s hard to see gains since they are by definition, infrequent. If the text is rare enough, BPE should produce many single byte tokens, so current models actually expend more compute on these rare sequences.

BLT scales well because it expends less compute (by patching) on more predictable (low entropy) byte sequences. Current models only to some degree get this benefit, if it’s a larger BPE token, but that only goes so far.

So it’s really two related, but different motivations.

In editing we couldn’t find a good place for this so cut it in the current version, but at one point had discussed a parallel with information density of speech as described by one paper. Essentially the paper found that in languages that were less information dense per syllable, speakers spoke faster to achieve similar information density as languages with higher density per syllable. You could see patching by entropy paralleling this if you consider that low entropy bytes in terms of Shannon entropy are less information dense.

Not sure what you mean by implicit? If you mean just treat bytes as tokens, one issue you run into is your sequence lengths get quite long, so compared to a regular token LLM, you can’t pack as many bytes in a batch, which means you’re pretty FLOP inefficient so scale worse. You could make the model smaller to compensate, but then the model isn’t as good.

My issue happened to be with sesamoid/metatarsal fracture, flat rootedness, and poor gait. No doctor cared about my gait which turned out to be the root cause of my other symptoms. They all wanted to do surgery/orthotics and basically thought that would fix it alone vs combined with PT.

Comments URL: https://news.ycombinator.com/item?id=20639707

Points: 1

# Comments: 1

I have zero patience for poor customer service.

As it turns out, implementing that different syntax in pyfunctional wouldn't be too hard or API breaking I think. Mainly it would require 1) wrapping/exporting functions to a module you could bring into scope (eg `from functional import functions as F` or `from functional.functions import *`), 2) Writing wrapper code to provide something functionally similar to `pipe`.

On libraries, I swear I saw something a while back, but my googlefu just now didn't help me find it.