[danielmarkbruce]

I'll bet said phds can't answer the equivalent question in a language they don't understand. LLMs don't speak character level english. LLMs are, in some stretched meaning of the word, illiterate.

If LLMs used character level tokenization it would work just fine. But we don't do that and accept the trade off. It's only folks who have absolutely no idea how LLMs work that find the strawberry thing meaningful.

[wat10000]

I’ll bet said PhDs will tell you they don’t know instead of confidently stating the wrong answer in this case. Getting LLMs to express an appropriate level of confidence in their output remains a major problem.

[sdesol]

> It's only folks who have absolutely no idea how LLMs work that find the strawberry thing meaningful.

I think it is meaningful in that it highlights how we need to approach things a bit differently. For example, instead of asking "How many r's in strawberry?", we say "How many r's in strawberry? Show each character in an ordered list before counting. When counting, list the position in the ordered list." If we do this, every model that I asked got it right.

https://beta.gitsense.com/?chat=167c0a09-3821-40c3-8b0b-8422...

There are quirks we need to better understand and I would say the strawberry is one of them.

Edit: I should add that getting LLMs to count things might not be the best way to go about it. Having it generate code to count things would probably make more sense.

[fzzzy]

Yes, you should say "could you please write and execute a program to count the number of "r" characters in the string "strawberry"

[HarHarVeryFunny]

I was impressed with Claude Sonnet the other day - gave it a photo of my credit card bill (3 photos actually - long bill) and asked it to break it down by recurring categories, counting anything non-recurring as "other". It realized without being asked that a program was needed, and wrote/ran it to give me what I asked for.

[sdesol]

I think this will be the future. LLMs will know enough to know that it should hand things off to something else.

[danielmarkbruce]

It's the present. ChatGPT, for example, is an application. It uses models, but it does all kinds of stuff at the application level too.

[sdesol]

Is the instructions to pass something off built into the model or is it clever prompting or a bit of both?

[HarHarVeryFunny]

In general it's "tool use" where the model's system prompt tells it to use certain tools for certain tasks, and having been trained to follow instructions, it does so!

[danielmarkbruce]

It's not that hard of a problem to solve at the application level. It's just hard to get a single model to do all the things.

[sdesol]

> It's not that hard of a problem to solve at the application level.

I think it will be easy if you are focused on one or two models from the same family, but I think the complexity comes when you try to get a lot models to act in the same way.

[HarHarVeryFunny]

I don't think that (sub-word) tokenization is the main difficulty. Not sure which models still fail the "strawberry" test, but I'd bet they can at least spell strawberry if you ask, indicating that breaking the word into letters is not the problem.

The real issue is that you're asking a prediction engine (with no working memory or internal iteration) to solve an algorithmic task. Of course you can prompt it to "think step by step" to get around these limitations, and if necessary suggest an approach (or ask it to think of one?) to help it keep track of it's letter by letter progress through the task.

[danielmarkbruce]

Breaking words/tokens is very explicitly the problem.

[HarHarVeryFunny]

No ... try claude.ai or meta.ai (both behave the same) by asking them how many r's in the (made up) word ferrybridge. They'll both get it wrong and say 2.

Now ask them to spell ferrybridge. They both get it right.

gemini.google.com still fails on "strawberry" (the other two seem to have trained on that, which is why i used a made up word instead), but can correctly break it into a letter sequence if asked.

[danielmarkbruce]

Yep, if by chance you hit a model that has seen the training data that happens to shove those tokens together in a way that it can guess, lucky you.

The point is, it would be trivial for an LLM to get it right all the time with character level tokenization. The reason LLMs using the current tokenization best tradeoff find this activity difficult is that the tokens that make up tree don't include the token for e.

[HarHarVeryFunny]

No - you can give the LLM a list of letters and it STILL won't be able to count them reliably, so you are guessing wrong about where the difficult lies.

Try asking Claude: how many 'r's are in this list (just give me a number as your response, nothing else) : s t r a w b e r r y

[danielmarkbruce]

How many examples like that do you think it's seen? You can't given an example of something that is in effect a trick to get character level tokenization and then expect it to do well when it's seen practically zero of such data in it's training set.

Nobody who suggests methods like character or byte level 'tokenization' suggests a model trained on current tokenization schemes should be able to do what you are suggesting. They are suggesting actually train it on characters or bytes.

You say all this as though I'm suggesting something novel. I'm not. Appealing to authority is kinda lame, but maybe see Andrej's take: https://x.com/karpathy/status/1657949234535211009

[michaelt]

You say that very confidently - but why shouldn't an LLM have learned a character-level understanding of tokens?

LLMs would perform very badly on tasks like checking documents for spelling errors, processing OCRed documents, pluralising, changing tenses and handling typos in messages from users if they didn't have a character-level understanding.

It's only folks who have absolutely no idea how LLMs work that would think this task presents any difficulty whatsoever for a PhD-level superintelligence :)

[danielmarkbruce]

LLMs are fed token ids, out of a tokenizer.... no characters. They don't even have any concept of a character.

You are in a discussion where you are just miles out of your depth. Go read LLMs 101 somewhere.

[michaelt]

If the LLM hasn't learned the letters that comprise input tokens, how do you explain this sort of behaviour?

https://chatgpt.com/share/678e95cf-5668-8011-b261-f96ce5a33a...

It can literally spell out words, one letter per line.

Seems pretty clear to me the training data contained sufficient information for the LLM to figure out which tokens correspond to which letters.

And it's no surprise the training data would contain such content - it'd be pretty easy to synthetically generate misspellings, and being able to deal with typos and OCR mistakes gracefully would be useful in many applications.

[danielmarkbruce]

Two answers: 1 - ChatGPT isn't an LLM, its an application using one/many LLMs and other tools (likely routing that to a split function).

2 - even for a single model 'call':

It can be explained with the following training samples:

"tree is spelled t r e e" and "tree has 2 e's in it"

The problem is, the LLM has seen something like:

8062, 382, 136824, 260, 428, 319, 319

and

19816, 853, 220, 17, 319, 885, 306, 480

For a lot of words, it will have seen data that results in it saying something sensible. But it's fragile. If LLMs used character level tokenization, you'd see the first example repeat the token for e in tree rather than tree having it's own token.

There are all manner of tradeoffs made in a tokenization scheme. One example is that openai made a change in space tokenization so that it would produce better python code.

[HarHarVeryFunny]

You're the one out of your depth ...

LLMs are taught to predict. Once they've seen enough training samples of words being spelled, they'll have learnt that in a spelling context the tokens comprising the word predict the tokens comprising the spelling.

Once they've learnt the letters predicted by each token, they'll be able to do this for any word (i.e. token sequence).

Of course, you could just try it for yourself - ask an LLM to break a non-dictionary nonsense word like "asdpotyg" into a letter sequence.

[famouswaffles]

Have you seen the Byte-latent Transformer paper?

It does away with sub-word tokenization but is still more or less a transformer (no working memory or internal iteration). Mostly, the (performance) gains seem modest (not unanimous, some benchmarks it's a bit worse) ....until you hit anything to do with character level manipulation and it just stomps. 1.1% to 99% on CUTE - Spelling as a particularly egregious example.

I'm not sure what the problem is exactly but clearly something about sub-word tokenization is giving these models a particularly hard time on these sort of tasks.

https://arxiv.org/abs/2412.09871

[HarHarVeryFunny]

The CUTE benchmark is interesting, but doesn't have enough examples of the actual prompts used and model outputs to be able to evaluate the results. Obviously transformers internally manipulate their input at token level granularity, so to be successful at character level manipulation they first need to generate the character level token sequence, THEN do the manipulation. Prompting them to directly output a result without allowing them to first generate the character sequence would therefore guarantee bad performance, so it'd be important to see the details.

https://arxiv.org/pdf/2409.15452

[danielmarkbruce]

> Once they've learnt the letters predicted by each token, they'll be able to do this for any word (i.e. token sequence).

They often fail at things like this, hence the strawberry example. Because they can't break down a token or have any concept of it. There is a sort of sweat spot where it's really hard (like strawberry). The example you give above is so far from a real word that it gets tokenized into lots of tokens, ie it's almost character level tokenization. You also have the fact that none of the mainstream chat apps are blindly shoving things into a model. They are almost certainly routing that to a split function.

[HarHarVeryFunny]

You're still not getting it ...

Why would an LLM need to "break down" tokens into letters to do spelling?! That is just not how they work - they work by PREDICTION. If you ask an LLM to break a word into a sequence of letters, it is NOT trying to break it into a sequence of letters - it is trying to do the only thing it was trained to do, which is to predict what tokens (based on the training samples) most likely follow such a request, something that it can easily learn given a few examples in the training set.

[danielmarkbruce]

The LLM can't, thats what makes it relatively difficult. The tokenizer can.

Run it through your head with character level tokenization. Imagine the attention calculations. See how easy it would be? See how few samples would be required? It's a trivial thing when the tokenizer breaks everything down to characters.

Consider the amount and specificity of training data required to learn spelling 'games' using current tokenization schemes. Vocabularies of 100,000 plus tokens, many of which are close together in high dimensional space but spelled very differently. Then consider the various data sets which give phonetic information as a method to spell. They'd be tokenized in ways which confuse a model.

Look, maybe go build one. Your head will spin once you start dealing with the various types of training data and how different tokenization changes things. It screws spelling, math, code, technical biology material, financial material. I specifically build models for financial markets and it's an issue.

[fzzzy]

The llm has absolutely no way of knowing which characters are in which token.

[throwaway2037]

    > LLMs are, in some stretched meaning of the word, illiterate.

You raise an interesting point here. How would LLMs need to change for you to call them literate? As a thought experiment, I can take a photograph of a newspaper article, then ask a LLM to summarise it for me. (Here, I assume that LLMs can do OCR.) Does that count?

[danielmarkbruce]

It's a bit of a stretch to call them illiterate, but if you squint, it's right.

The change is easy - get rid of tokenization and feed in characters or bytes.

The problem is, that causes all kinds of other problems with respect to required model size, required training, and so on. It's a researchy thing, I doubt we end up there any time soon.