Wondering if Modern LLMs like GPT4, Claude Sonnet and llama 3 are closer to human intelligence or next word predictor. Also not sure if this graph is right way to visualize it.
Wondering if Modern LLMs like GPT4, Claude Sonnet and llama 3 are closer to human intelligence or next word predictor. Also not sure if this graph is right way to visualize it.
Somewhere on the vertical axis. 0 on the horizontal. The AGI angle is just to attract more funding. We are nowhere close to figuring out the first steps towards strong AI. LLMs can do impressive things and have their uses, but they have nothing to do with AGI
AGI could be possible if a new breakthrough is made. Currently LLMs are just pretty good text predictor, and any intelligence exhibited by them is because they are trained on texts exhibiting intelligence (written by humans) . Make a large enough model, and it will seem like an intelligent being.
That was already true in previous paradigms. A non-fuzzy non-neural-network algorithm large and complex enough will seem like an intelligent being. But “large enough” is beyond our resources and processing time for each response would be too long.
And then you get into the Chinese room problem. Is there a difference between seems intelligent and is intelligent?
But the main difference between an actual intelligence and various algorithms, LLMs included, is that intelligence works on its own, it’s always thinking, it doesn’t only react to external prompts. You ask a question, you get an answer, but the question remains at the back of its mind, and it might come back to you 10min later and say you know, I’ve given it some more thought and I think it’s actually like this.
https://www.youtube.com/watch?v=KKF7kL0pGc4 what’s your take on this?
A next word predictor algorithm is still a next word predictor algorithm even if you change it’s training algorithm. To think that a LLM will eventually lead to intelligence inherently asserts that intelligence comes from the ability to use language.
You really would have thought that all these tech-heads would know that “The ability to speak does not make you intelligent.”
We know, through studies on actual humans, that language filters, constrains and quantises our thoughts process, and that different languages do this in different ways. Language harms our ability to reason. We’ve internalised it to such a degree that it now forces our ideas to fit into what the language can express. However, the ability to share our thoughts with others and collaborate is a massive boon for us as a species.
The whole this field is drawing pictures on the walls of Plato’s cave, trying to mimick the shadows being cast in from outside. Their drawings might look superficially similar to their inspiration, but they’re a poor imitation and that’s all they will ever be.
Is it not the case that predicting the next word often requires reasoning about the next word?
And that if you select for better and better prediction, you have to also select for reasoning?
This is true, but it’s specifically not what LLMs are doing here. It may come to some very limited, very specific reasoning about some words, but there’s no “general reasoning” going on.
Did you watch the video I linked?
It seems to be essentially about a way to trick them into doing general reasoning, and a direct response to your comment.
It’s not a direct response.
First off, the video is pure speculation, the author doesn’t really know how it works either (or at least doesn’t seem to claim to know). They have a reasonable grasp of how it works, but what they believe it implies may not be correct.
Second, the way O1 seems to work is that it generates a ton of less-than-ideal answers and picks the best one. It might then rerun that step until it reaches a sufficient answer (as the video says).
The problem with this is that you still have an LLM evaluating each answer based on essentially word prediction, and the entire “reasoning” process is happening outside any LLM; it’s thinking process is not learned, but “hardcoded”.
We know that chaining LLMs like this can give better answers. But I’d argue this isn’t reasoning. Reasoning requires a direct understanding of the domain, which ChatGPT simply doesn’t have. This is explicitly evident by asking it questions using terminology that may appear in multiple domains; it has a tendency of mixing them up, which you wouldn’t do if you truly understood what the words mean. It is possible to get a semblance of understanding of a domain in an LLM, but not in a generalised way.
It’s also evident from the fact that these AIs are apparently unable to come up with “new knowledge”. It’s not able to infer new patterns or theories, it can only “use” what is already given to it. An AI like this would never be able to come up with E=mc2 if it hasn’t been fed information about that formula before. It’s LLM evaluator would dismiss any of the “ideas” that might come close to it because it’s never seen this before; ergo it is unlikely to be true/correct.
Don’t get me wrong, an AI like this may still be quite useful w.r.t. information it has been fed. I see the utility in this, and the tech is cool. But it’s still a very, very far cry from AGI.
I’d also like to ask how you feel about this paper:
https://arxiv.org/abs/2402.12875
I won’t pretend I understand all the math and the notation they use, but the abstract/conclusions seem clear enough.
I’d argue what they’re presenting here isn’t the LLM actually “reasoning”. I don’t think the paper really claims that the AI does either.
The CoT process they describe here I think is somewhat analogous to a very advanced version of prompting an LLM something like “Answer like a subject matter expert” and finding it improves the quality of the answer.
They basically help break the problem into smaller steps and get the LLM to answer smaller questions based on those smaller steps. This likely also helps the AI because it was trained on these explained steps, or on smaller problems that it might string together.
I think it mostly helps to transform the prompt into something that is easier for an LLM to respond accurately to. And because each substep is less complex, the LLM has an easier time as well. But the mechanism to break down a problem is quite rigid and not something trainable.
It’s super cool tech, don’t get me wrong. But I wouldn’t say the AI is really “reasoning” here. It’s being prompted in a really clever way to increase the answer quality.