[karterk]

Solving the strawberry problem will probably require a model that just works with bytes of text. There have been a few attempts at building this [1] but it just does not work as well as models that consume pre-tokenized strings.

[1]: https://arxiv.org/abs/2106.12672

[randomdata]

Or just a way to compel the model to do more work without needing to ask (isn't that what o1 is all about?). If you do ask for the extra effort it works fine.

    + How many "r"s are found in the word strawberry? Enumerate each character.

    - The word "strawberry" contains 3 "r"s. Here's the enumeration of each character in the word:
    - 
    - [omitted characters for brevity]
    -
    - The "r"s are in positions 3, 8, and 9.

[jeroenhd]

I tried that with another model not that long ago and it didn't help. It listed the right letters, then turned "strawberry" into "strawbbery", and then listed two r's.

Even if these models did have a concept of the letters that make up their tokens, the problem still exists. We catch these mistakes and we can work around them by altering the question until they answer correctly because we can easily see how wrong the output is, but if we fix that particular problem, we don't know if these models are correct in the more complex use cases.

In scenarios where people use these models for actual useful work, we don't alter our queries to make sure we get the correct answer. If they can't answer the question when asked normally, the models can't be trusted.

[mistercow]

I think o1 is a pretty big step in this direction, but the really tricky part is going to be to get models to figure out what they’re bad at and what they’re good at. They already know how to break problems into smaller steps, but they need to know what problems need to be broken up, and what kind of steps to break into.

One of the things that makes that problem interesting is that during training, “what the model is good at” is a moving target.

[eithed]

Are you saying that I have to know how LLM work to know what I should ask LLM about?

[randomdata]

Perhaps. LLMs are trained to be as human-like as possible, and you most definitely need to know how the individual human you are asking works if you want a reliable answer. It stands to reason that you would need to understand how an LLM works as well.

The good news is that if you don't have that understanding, at least you'll laugh it off with "Boy, that LLM technology just isn't ready for prime time, is it?". In contrast to when you don't understand how the human works – that leads to, at very least name calling (e.g. "how can you be so stupid?!"), a grander fight, even all out war at the extreme end of the spectrum.

[eithed]

You're right in aspect that I need to know how humans work to ask them a question = if I were to ask my dad how many Rs are in strawberry, he would say "I don't have a clue" because he doesn't speak english. But he wouldn't hallucinate an answer - he would admit that he doesn't know what I'm asking him about. I gather that here LLM is convinced that the answer is 2, but that means that LLM are being trained to be alien, or at least, when I'm asking questions I need to be precise on what I'm asking about (which isn't any better). Or maybe humans also hallucinate 2, dependent on human

[randomdata]

It seems your dad has more self-awareness than most.

A better example is right there on HN. 90% of the content found on this site is just silly back and forths around trying to figure out what each other is saying because the parties never took the time to stop and figure out how each other works to be able to tailor the communication to what is needed for the actors involved.

In fact, I suspect I'm doing that to you right now! But I didn't bother trying to understand how you work, so who knows?

[viraptor]

Or "when trying to answer questions that involve spelling or calculation, use python". No need for extra training really.

[karterk]

There are many different classes of problems that are affected by tokenization. Some of them can be tackled by code.