That they can't do this sort of simple question speaks volumes to the entire approach.
I don't think generative AI will ever be able to reach AGI, and most people selling LLM today pretend it is AGI
I don't think generative AI will ever be able to reach AGI, and most people selling LLM today pretend it is AGI
LLMs can't effectively execute algorithms similarly in their context, nor can they memorize new data or facts it was given without providing it tools like function calling or embeddings. If you give LLMs tool calling and storage mechanisms then counting letters in words becomes pretty damn reliable.
No one said that LLMs aren’t good for anything.
I pointed out — in response to another poster downplaying mention of a well-known and undisputed limitation that LLMs often have — that it is valid to consider these well-known and undisputed limitations if one is considering using them for anything other than a toy.
It is downright silly to discourage discussion of well-known and undisputed limitations! The only reason for that can only be entirely emotional as there is genuinely nothing tangible to be gained by being steadfast in silence about a fact that isn’t up for debate.
I took your response to be arguing against a message I'd read to be saying something like the above. Especially when you basically seemed to be saying that limitations like this are important in everything but toy applications. It's uninteresting because it doesn't point towards larger problems with their use in the kind of application they're being used for and are intended for unlike prompts that point to weakness in logic or propensity to hallucinate.
Don’t need to be exact as firing is statistical, just give us a good average.
Hard? You can’t count?
Computers count pixels no problem. So weird you can’t.
Dementia? Not an AGI? /h
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This is what is happening.
Here are the “Reasons”.
In your vision system, the raw information from individual retina signals is munged into a different representation before reaching a level where you have flexible processing.
Likewise, in LLMs, letters are munged into tokens before LLMs “see” them.
When they sometimes get that “simple” question right, it’s actually a bit of an amazing feat. Given how they are constructed.
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Now try counting R’s as you read at a normal rate, or listen to someone speak.
You can’t do that either, during normal processing.
When we add spelling to LLMs training examples, they will do it easily. Just as you learned to do it, only after special lessons, after you had already learned to listen and speak.
Spelling is its own special practiced skill, in humans and LLMs.
This analogy makes sense because everybody could count their red retina cells until a couple years ago when the new painting paradigm arose, and also counting red retinal cells is a good analogy for being able to see simple objects that have always been distinguishable.
It is fascinating how tapping the “Do Not Use LLMs For Computation If The Results Need To Be Reliably Better Than A Random Output” sign invites explanations of why that fact is actually Cool and Good
You learned to listen and speak words before you could spell. Imagine if nobody had actually shown you written words?
Or they were speaking another dialect but expecting you to count R’s in standard English?
LLMs are not trained on words in the form of letters.
They process and generate the words in the form of tokens. Pre- and post-processing systems converts letters to tokens and the reverse, without their ability to access that processing.
Spelling, for both us and LLMs, requires specific training/lessons.
> It is fascinating how tapping the “Do Not Use LLMs For Computation If The Results Need To Be Reliably Better Than A Random Output” sign invites explanations of why that fact is actually Cool and Good
Also fascinating:
People who hallucinate/confabulate ridiculous straw man rationales for people they disagree with, unaware they are filling in gaps in their knowledge regarding other people’s actual reasoning and the actual subject at hand.
So LLM! Such unreliable processing!
Perhaps, start posting a reliability disclaimer?
(There are tasks that LLMs totally fail on that would be obvious to an illiterate human though)
To envision what a next generation model bound by the same constraints should do, it'd be to recognize that it can't count tokens and use code access to write code that solves the strawberry problem without prompting.
Asked to count cells it'd be a model that could write and execute OpenCV tasks. Or to go a step further, be a multimodal model that can synthesize 10000 varations of the target cell, and finetune a model like YOLO on it autonomously.
I find arguments that reduce LLMs to "It can't do the simple thing!!!!" come from people unable to apply lateral thinking to how a task can be solved.
The VQA problems I'm describing can be solved seemingly in one case but not combined with counting. Counting is fundamentally challenging for sort of unknown reasons, or perhaps known to the very best labs who are trying to tackle it directly.
Another POV is that the stuff you are describing is in some sense so obvious that it has been tried, no?
What I described is tried, and works, but the models are still not cheap/fast/reliable enough to always do what I described for every query.
The difference between what I described and directly asking the model to count is that we know the models can get cheaper, faster, and more reliable at what I described without any earth shattering discoveries
Like I don't see any reason why GPT 10 will ever be able to count how many letters there are in the word strawberry without a complete paradigm shift in model building... but going from GPT 3 to GPT 4 we already got a model that can always write the dead simple code required to count it out, and the models that can do so are already getting cheaper and faster every few months without any crazy discoveries.
Maybe they should make you Chief Scientist at OpenAI, I hear the position is open.
Thats about the same kind of question, given that LLMs see tokens instead of characters, and we see characters instead of bits.
GPT 3 would essentially always fail on higher math problems/arithmetic, but whenever I've used it for math GPT 4 has always delegated out to executing code where necessary.
Having said that, the 40b model wasn't able to answer any of my real-world example questions correctly. Some of these (e.g. "how do I add a sequential number after my titles in an HTML page using just CSS, without changing the page") are questions that even some of the better small local models can answer correctly. It gave very authoritatively sounding wrong answers.
Who exactly does this in this space? Would be good to be able to call them out on it right now.
But it's likely to be an important component in an AGI system. I suppose the interesting question is how to integrate LLMs with more traditional logic and planning systems.
>most people selling LLM today pretend it is AGI
Who are these "most people"?
The choice just comes with trade-offs in memory usage, compute, and effectiveness of the model in various scenarios.
So what we ended up with was a pragmatic/engineering decision rather than a theoretical or fundamental constraint.