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What can LLMs never do?

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Re: What can LLMs never do?

#331

Post by xanderlewis »

danenania wrote: post #316
xanderlewis wrote: post #315

Earlier quoted context omitted.

I can’t define ‘understanding’ but I can certainly identify a lack of it when I see it. And LLM chatbots absolutely do not show signs of understanding. They do fine at reproducing and remixing things they’ve ‘seen’ millions of times before, but try asking them technical questions that involve logical deduction or an actual ability to do on-the-spot ‘thinking’ about new ideas. They fail miserably. ChatGPT is a smooth-…

Your understanding of how LLMs work isn’t at all accurate. There’s a valid debate to be had here, but it requires that both sides have a basic understanding of the subject matter.

How is it not accurate? I haven’t said anything about the internal workings of an LLM — just what it able to produce (which is based on observation).

I have more than a basic understanding of the subject matter (neural networks; specifically transformers, etc.). It’s actually not a hugely technical field.

By the way, it appears that you are in category (a).

Re: What can LLMs never do?

#332

Post by danenania »

xanderlewis wrote: post #331
danenania wrote: post #316

Earlier quoted context omitted.

Your understanding of how LLMs work isn’t at all accurate. There’s a valid debate to be had here, but it requires that both sides have a basic understanding of the subject matter.

How is it not accurate? I haven’t said anything about the internal workings of an LLM — just what it able to produce (which is based on observation). I have more than a basic understanding of the subject matter (neural networks; specifically transformers, etc.). It’s actually not a hugely technical field. By the way, it appears that you are in category (a).

You don’t know what they’re able to produce because you clearly don’t know how they actually work. So your “observations” are not worth much.

Re: What can LLMs never do?

#333

Post by krainboltgreene »

naasking wrote: post #278
krainboltgreene wrote: post #256

Earlier quoted context omitted.

How do you know my cat isn't constantly solving calculus problems? I also can't come up with a "mechanistic model" for what it means to do that either. Further, if your rubric for "can reason with intelligence and have an opinion" is "looks like it" (and I certainly hope this isn't the case because woo-boy), then how did you not feel this way about Mark V. Shaney? Like I understand that people live learning about the…

> but we actually know it's a program and how it works. There is no mystery. You're right, we do know how it works. Your mistake is concluding that because we know how LLMs work and they're not that complicated, but we don't know how the brain works and it seems pretty complicated, therefore the brain can't be doing what LLMs do. That just doesn't follow. You made exactly the same argument in the opposite direction,…

Dude it's a token predictor. This all sounds very nice until you snap back to reality and remember it's a token predictor and you're not a scientist. You're a web developer. You have no evidence, you have no studies, you have no proof. You're making a claim on the basis that everyone has as much understanding of the field as you and that's just wrong.

Re: What can LLMs never do?

#334

Post by krainboltgreene »

int_19h wrote: post #314
krainboltgreene wrote: post #200

Earlier quoted context omitted.

I am extremely alarmed by the number of HN commenters who apparently confuse "is able to generate text that looks like" and "has a", you guys are going crazy with this anthropomorphization of a token predictor. Doesn't this concern you when it comes to phishing or similar things? I keep hoping it's just short-hand conversation phrases, but the conclusions seem to back the idea that you think it's actually thinking?

The "stochastic parrot" crowd keeps repeating "it's just a token predictor!" like that somehow makes any practical difference whatsoever. Thing is, if it's a token predictor that consistently correctly predicts tokens that give the correct answer to, say, novel logical puzzles, then it is a reasoning token predictor, with all that entails.

This isn't correct and I am extremely concerned if this is the level of logic running billions of dollars.

Re: What can LLMs never do?

#335

Post by a_wild_dandan »

cs702 wrote: post #310
naasking wrote: post #299

> They have been trained on more information than a human being can hope to even see in a lifetime. Assuming a human can read 300 words a min and 8 hours of reading time a day, they would read over a 30,000 to 50,000 books in their lifetime. Most people would manage perhaps a meagre subset of that, at best 1% of it. That’s at best 1 GB of data. This just isn't true. Human training is multimodal to a degree far beyond…

I agree with you, but your comment strikes me as unfair nitpicking , because the OP is referring to information that has been encoded in words.

Other modalities affect word semantics. You cannot ignore them when discussing sample efficiency.

Re: What can LLMs never do?

#336

Post by lossolo »

throwthrowuknow wrote: post #297
Etherlord87 wrote: post #292

Earlier quoted context omitted.

I'm far from being an expert on AI models, but it seems you lack the basic understanding of how these models work. They transform data EXACTLY like spreadsheets do. You can implement those models in Excel, assuming there's no row or column limit (or that it's high enough) - of course it will be much slower than the real implementations, but OP is right - LLMs are basically spreadsheets. Question is, wouldn't a brain…

You can implement Doom in a spreadsheet too, so what? That wasn’t the point op or I were making. If you bother to read the sentence before op talks about spreadsheets they are making the conjecture that LLMs are lookup tables operating on the corpus they were trained on. That is the aspect of spreadsheets they were comparing them to, not the fact that spreadsheets can be used to implement anything that any other prog…

> Which LLMs can’t produce rhyming pairs? Both the current ChatGPT 3.5 and 4 seem to be able to generate as many as I ask for

Only in english. If they would understand language and rhymes they would do it in every other language it knows, It can't in my language while it can speak in it fluently. It just fails. And fails in so many other areas, I'm using LLMs daily for work and other stuff and if you use them long enough you will see that they are statistical machines not intelligent entities.

Re: What can LLMs never do?

#337

Post by lossolo »

throwthrowuknow wrote: post #286
SomeCallMeTim wrote: post #136

Earlier quoted context omitted.

LLMs are good at tasks that don't require actual understanding of the topic. They can come up with excellent (or excellent-looking-but-wrong) answers to any question that their training corpus covers. In a gross oversimplification, the "reasoning" they do is really just parroting a weighted average (with randomness injected) of the matching training data. What they're doing doesn't really match any definition of "und…

What is your definition of understanding? Please show me where the training data exists in the model to perform this lookup operation you’re supposing. If it’s that easy I’m sure you could reimplement it with a simple vector database. Your last two paragraphs are just dualism in disguise.

Transformer is not a simple vector database doing simple lookup operation. It's doing lookup operation on a pattern, not a word. It learns patterns from the dataset. If your pattern is not there it will hallucinate or give you the wrong answer like GPT4 and Opus gave me hundreds of times already.

Re: What can LLMs never do?

#338

Post by jncfhnb »

card_zero wrote: post #308
jncfhnb wrote: post #304

Earlier quoted context omitted.

I feel you qualify during all of those waking seconds

Racoons are said to be intelligent because they're good at opening locks. On the other hand, when they have food and are within ten feet of a pool of water, they will dip the food in the water and rub it between their paws for no reason. They can reason about the locks, but not about the food. Meanwhile, I in theory can reason about anything, but in practice I wouldn't count on it. Whereas an LLM can't reason, but it…

I’m not familiar with this raccoon behavior but it sure doesn’t sound like it’s done without reason.

An LLM is never ready to react to anything because it’s just a matrix that needs a higher level system to invoke it.

Re: What can LLMs never do?

#339

Post by naasking »

cs702 wrote: post #310
naasking wrote: post #299

> They have been trained on more information than a human being can hope to even see in a lifetime. Assuming a human can read 300 words a min and 8 hours of reading time a day, they would read over a 30,000 to 50,000 books in their lifetime. Most people would manage perhaps a meagre subset of that, at best 1% of it. That’s at best 1 GB of data. This just isn't true. Human training is multimodal to a degree far beyond…

I agree with you, but your comment strikes me as unfair nitpicking , because the OP is referring to information that has been encoded in words.

I understand that's the context, but I'm not sure that it's unfair nitpicking. It's common to talk about training data and how poor LLMs are compared to humans despite the apparently larger dataset than any human could absorb in a lifetime. The argument is just wrong because it doesn't properly quantify the dataset size, and when you do, you actually conclude the opposite: it's astounding how good LLMs are despite their profound disadvantage.

Re: What can LLMs never do?

#340

Post by naasking »

krainboltgreene wrote: post #333
naasking wrote: post #278

Earlier quoted context omitted.

> but we actually know it's a program and how it works. There is no mystery. You're right, we do know how it works. Your mistake is concluding that because we know how LLMs work and they're not that complicated, but we don't know how the brain works and it seems pretty complicated, therefore the brain can't be doing what LLMs do. That just doesn't follow. You made exactly the same argument in the opposite direction,…

Dude it's a token predictor. This all sounds very nice until you snap back to reality and remember it's a token predictor and you're not a scientist. You're a web developer. You have no evidence, you have no studies, you have no proof. You're making a claim on the basis that everyone has as much understanding of the field as you and that's just wrong.

What claim am I making, specifically?
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