Comment by PaulDavisThe1st | Hacker News Reader

Comment by PaulDavisThe1st | Hacker News Reader

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PaulDavisThe1st

Actually, I'm not narrowly fixated at all.

I do not believe that any part of any current or future LLM (i.e. using the same fundamental architecture) is capable of reasoning, or in fact, capable of anything other than, essentially, doing a really, really, really good job of generating the next word in a response.

I am all about emergent properties of neural networks, but I absolutely do not believe that LLMs have them, specifically because of the way they are designed.

However, as to the specifics, people who seem to believe otherwise claim that they can reason, and so that's merely one specific angle of attack: to show that they cannot reason, and that in fact, everything they do is implicitly contained in their training set.

As I've already said, what they can do is enormously powerful and in many (most?) ways entirely unexpected, so I still regard the advent of LLMs as extremely significant, both from a practical but also a scientific point of view. I think it may force a revision in the most basic aspects of understanding human speech behavior, for example.

Nevertheless, I do not believe that anybody is served by believing that these systems can do things that they cannot. I do not understand why the seemingly magic results of these systems is leading so many into a denial of what they actually do.

> I am all about emergent properties of neural networks, but I absolutely do not believe that LLMs have them, specifically because of the way they are designed.

Even when faced with evidence that contradicts your beliefs and proves that you are wrong?

LLMs are a type of neural network. What fundamentally prevents LLMs from having emergent abilities while other neural networks do have them?

How do you explain the emergent abilities that we have actually observed in LLMs?

You don't have any evidence, and neither does any paper I've read claiming as such.

The level of naievete around this stuff is quite incredible.

BEFORE: "We have no idea really what a system like this will do"

... amazing results ...

AFTER: "Wow, look at those emergent properties"

What do you mean we don't have evidence? It has already been presented to you. You choose to reject it for reasons I truly don't understand. Search on Google Scholar if you want a more academic explanation. You can try it out on ChatGPT right now and see it yourself.

> The level of naievete around this stuff is quite incredible.

"Emergent properties" has a formal definition in the literature.

In machine learning we don't explicitly program the machine to understand anything. It automatically learns patterns in the data. That's the entire point of machine learning. With such a large neural network and training set obviously it's hard to predict all of its capabilities due to the sheer scale of it all. Of course we cannot predict exactly how it will model things.

Take this for example. No one programmed it to understand Go. It learned by itself and became the world champion. That's what deep learning is capable of.

https://en.wikipedia.org/wiki/AlphaGo_Zero

"The neural network initially knew nothing about Go beyond the rules."

Here is an example of an emergent ability of ChatGPT that you can try yourself right now.

Give it this prompt: "Write a short play where "Karen" (who behaves as the Karen meme) is on a romantic date with Hunter S. Thompson. They are eating at a Chinese restaurant."

It is able to script their interaction and dialog in a way that makes sense in the context of the setting and their personalities, including an absurd meme character. That is an emergent ability that it was not trained to do and is certainly not in its training set.

Try it. Add different characters. Ask it to rewrite the play using pirate metaphors. You can go deep into its "mind" and see the emergent abilities at play. Just apply some creativity and skip the boring arithmetic problems, as that's a well accepted weakness of this type of model.

Solving natural language understanding tasks requires reasoning, by definition. I think you are sticking to a narrow definition of reasoning that is not very technical. Formally there are many types of reasoning in AI.

But LLMs do not solve natural language understanding in any of the meanings that the phrase meant before LLMs. Instead, they throw a completely new technique at it that completely sidesteps the need for language understanding and what do you know? For the purposes of responding in meaningful, generally sensible ways, it works amazingly well. And that is incredibly cool. But it doesn't solve the (all) problem(s) that more historical approaches to machine language "understanding" were concerned with.

But there is no world representation inside an LLM, only text (words, letters) representations, so nothing the LLM does can be based on reasoning in a traditional sense.

I would wager that if we build an LLM based on a training data set collection, and then we rebuild it with a heavily edited version of the data set that explicitly excludes certain significant areas of human discourse, the LLM will be severely impaired in its apparent ability to "reason" about anything connected with the excluded areas. That sounds as if it ought to surprise you, since you think they are capable of reasoning beyond the training set. It wouldn't surprise me at all, since I do not believe that it what they are doing.

LLMs contain a model of human speech (really text) behavior that is almost unimaginably more complex than anything we've built before. But by itself that doesn't mean very much with respect to general reasoning ability. The fact that LLMs can convince you otherwise points, to me, to the richness of the training data in suitable responses to almost any prompt,suitable, that is, for the purpose of persuading you that there is some kind of reasoning occuring. But there is not. The fact that neither you nor I can really build a model (hah!) of what the LLM is actually doing doesn't change that.

> But LLMs do not solve natural language understanding in any of the meanings that the phrase meant before LLMs.

Are you saying that NLP as a field of research did not exist before LLMs? This is a continuation of research that has been in progress for decades.

> But there is no world representation inside an LLM, only text (words, letters) representations, so nothing the LLM does can be based on reasoning in a traditional sense.

Not true. The model has learned a representation of semantic relationships between words and concepts at multiple levels of abstraction. That is the entire point. That's what is was trained to do.

It's a vast and deep neural network with a very high dimensional representation of the data. Those semantic/meaning relations are automatically learned and encoded in the model.

Damn HN and its delayed reply policies ...

> It's a vast and deep neural network with a very high dimensional representation of the data.

the data is text, so ...

It's a vast and deep neural network with a very high dimensional representation of *text*

And yes, to some extent, text represents the world in interesting ways. But not adequately, IMO.

If you were an alien seeking to understand the earth, starting with humans' textual encoding thereof might be a palce to start. But its inadequacies would rapidly become evident, I claim, and you would realize that you need a "vast and deep representation" of the actual planet.

> Are you saying that NLP as a field of research did not exist before LLMs? This is a continuation of research that has been in progress for decades.

Of course I'm not saying that (the first sentence). Part of my whole point is that LLMs are to NLPs as rockets are to airplanes. They're fundamentally a "rip it up and start again" approach, that discards almost everything everyone knew about NLP. The results are astounding, but the connection with, yes, "traditional" NLP is tenuous.

> Part of my whole point is that LLMs are to NLPs as rockets are to airplanes.

Yes it is deep learning applied to NLP. Makes the old designs obsolete

> the data is text

It is not randomly generated text. There are patterns in that text. It was trained to model the semantics or "meaning" in the text. There is a structure in the text which the machine has recognized.

It automatically learned a model of many concepts without any of those concepts being explicitly programmed into it. That's the entire point of machine learning.

> But not adequately, IMO.

It is adequate for some things and not adequate for other things.

It seems that all you are saying is that GPT is not AGI and doesn't have human level of understanding and reasoning. No one disagrees with that.

> It seems that all you are saying is that GPT is not AGI and doesn't have human level of understanding and reasoning. No one disagrees with that.

No, I'm saying that LLMs perform what is essentially an amazing, really hard to understand party trick that happens to be so good it is actually useful. They may be indicative (surprisingly) of how a lot of actual human speech production happens (or may not), and if so, that's really good to discover. That this is surprising represents a bit of a failure of our imagination, but it also leaves untouched the parts of speech production that don't use this sort of process.

Like actual reasoning.

Alright so deep learning, the state of the art of AI, is a "party trick". AlphaZero is likewise a party trick. No "true" reasoning involved.

> Like actual reasoning.

You're relying on intuition and personal beliefs of what constitutes "true" reasoning instead of formal rigorous mathematical definitions of reasoning. The general concept of reasoning includes what the language models are doing when they solve natural language understanding tasks, by definition.

It just sounds like a No True Scotsman fallacy.

Here's a prompt: "If there is a bob outside, and a bib inside, and the bib cannot see the bob, can the bob see the bib?"

Answer:

"If the bib cannot see the bob due to being inside, it is likely that the bob, which is outside, also cannot see the bib. The reason for the bib not being able to see the bob is presumably due to some kind of barrier, such as a wall or a window, which would likely obstruct the view for both the bib and the bob."

Prompt:

"What type of reasoning did you just apply to solve that task?"

Answer:

"In solving the task, I applied abductive reasoning. Abductive reasoning involves inferring the most likely explanation for a set of observations or statements. In this case, I inferred that a barrier (e.g., a wall or window) is the most likely explanation for why the bib cannot see the bob, and thus, the bob likely cannot see the bib either."

ChatGPT solves the problem by a mechanical reasoning process, not a "party trick". It's a machine, not a person at a party. Surely you don't believe that it has seen that exact sequence of questions and replies in its training set and is simply regurgitating. You can argue about whether its actually using "abductive reasoning" or not, but that is besides the point that it involves some mechanical reasoning process over an interpretation of the prompt. It's not a simple regurgitation.

AlphaZero learned to play Go starting with nothing but the rules of the game. What is it regurgitating there?

(follow up to adjacent comment)

So what I'm saying is, GPT "knows" what a cat is. It "knows" what an orange is. It has inferred these concepts from the data set.

Imagine approaching someone who is tripping on LSD and demanding they immediately solve a 10 digit multiplication problem, then saying "AHA! You cannot solve it, therefore you are incapable of any reasoning whatsoever!"

Also

> reasoning in a traditional sense.

We are talking about reasoning in a general sense. There are many types of reasoning in AI which I'm sure you know how to look up and read about. "Traditional" is not one of the categories.

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