AI field desperately needs smarter models - not faster models.
Worse than useless for debugging.
An LLM can't think and doesn't have capabilities for self-reflection.
It will just generate a plausible stream of tokens in reply that may or may not correspond to the real reason why.
Just think about it as a human employee. Can they always say why they did what they did? Often, but not always. Sometimes you will have to work to figure out the misunderstanding.
How so? What the LLM says is whatever is more likely given the context. It has no relation to the underlying reality whatsoever.
For example, here is a simple query:
> I put my bike in the bike stand, I removed my bike from the bike stand and biked to the beach, then I biked home, where is my bike. Answer only with the answer and no explanation
> Chatgpt: At home
> Why did you answer that?
> I answered that your bike is at home because the last action you described was biking home, which implies you took the bike with you and ended your journey there. Therefore, the bike would logically be at home now.
Do you doubt that the answer would change if I changed the query to make the final destination be "the park" instead of "home"? If you don't doubt that, what do you mean that the answer doesn't correspond to the underlying reality? The reality is the answer depends on the final destination mentioned, and that's also the explanation given by the LLM, clearly the reality and the answers are related.
Then there is the question of what you would do with its response. It’s not like code where you can go in and update the logic. There are billions of floating point numbers. If you actually wanted to update the weights you’ll quickly find yourself fine-tuning the monstrosity. Orders of magnitude more work than updating an “if” statement.
> Then there is the question of what you would do with its response. I
Sure but that's a separate question. I'd say the first course of action would be to edit the prompt. If you have to resort to fine tuning I'd say the approach has failed and the tool was insufficient for the task.
For LLMs, interpretability is one problem. The ability to effectively apply fixes is another. If we are talking about business logic, have the LLM write code for it and don’t tie yourself in knots begging the LLM to do things correctly.
There is a grey area though, which is where code sucks and statistical models shine. If your task was to differentiate between a cat and a dog visually, good luck writing code for that. But neural nets do that for breakfast. It’s all about using the right tool for the job.
No it isn't. You misunderstand how LLMs work. They're giant Mad Libs machines: given these surrounding words, fill in this blank with whatever statistically is most likely. LLMs don't model reality in any way.
> They're giant Mad Libs machines: given these surrounding words, fill in this blank with whatever statistically is most likely. LLMs don't model reality in any way.
Not sure why you think this is incompatible with the statement you disagreed with.
Yes, I do. An LLM replies with the most likely string of tokens. Which may or may not correspond with the correct or reasonable string of tokens, depending on how stars align. In this case the statistically most likely explanation the LLM replied with just happened to correspond with the correct one.
I claim that case is not so uncommon as people in this thread seem to think
Today, the accuracy of LLMs is by far a bigger concern (and a harder problem to solve) than its speed. If someone releases a model which is 10x slower than o3, but is 20% better in terms of accuracy, reliability, or some other metric of its output quality, I'd switch to it in a heartbeat (and I'd be ready to pay more for it). I can't wait until o3-pro is released.
I don't know what benchmark you're looking at but I'm sure the questions in it were more complicated than the logic inside a vending machine.
Why don't you just try it out? It's easy to simulate, just tell the bot about the task and explain to it what actions to perform in different situations, then provide some user input and see if it works or not.
With vending machines costing 2-5k, it’s not out of the question, but it’s hard to imagine the business case for it. Maybe the tantalizing possibility of getting a free soda would attract traffic and result in additional sales from frustrated grifters? Idk.
It probably wouldn’t with current models. That’s exactly why I said we need smarter models - not more speed. Unless you want to “use that for iteration, writing more tests and satisfying them, especially if you can pair that with a concurrent test runner to provide feedback.” - I personally don’t.
They are simple calculators that answer with whatever tokens are most likely given the context. If you want reasonable or correct answers (rather than the most likely) then you're out of luck.
In the 90s if your cursor turned into an hourglass and someone said "it's thinking" would you have pedantically said "NO! It is merely calculating!"
Maybe you would... but normal people with normal English would not.
Computers have self-reflection to a degree - e.g., they react to malfunctions and can evaluate their own behavior. LLMs can't do this, in this respect they are even less of a thinking machine than plain old dumb software.