6 ms·

A fun and insightful read, but the idea that it isn’t “just a prompting issue” is objectively false, and I don’t mean that in the “lemme show you how it’s done”

by docjay 8mo ago

A fun and insightful read, but the idea that it isn’t “just a prompting issue” is objectively false, and I don’t mean that in the “lemme show you how it’s done” way. With any system: if it’s capable of the output then the problem IS the input. Always. That’s not to say it’s easy or obvious, but if it’s possible for the system to produce the output then it’s fundamentally an input problem. “A calculator will never understand the obesity epidemic, so it can’t be used to calculate the weight of 12 people on an elevator.”

reedlaw 8mo ago
This article, (https://michaelmangialardi.substack.com/p/the-celestial-mirror-how-medieval https://michaelmangialardi.substack.com/p/the-celestial-mirr...), came to similar conclusions as the parent article, and includes some tests (e.g. https://colab.research.google.com/drive/1kTqyoYpTcbvaz8tiYgjR8Oprg2vk5KxH https://colab.research.google.com/drive/1kTqyoYpTcbvaz8tiYgj...) showing that LLMs, while good at understanding, fail at intellectual reasoning. The fact that they often produce correct outputs has more to do with training and pattern recognition than ability to grasp necessity and abstract universals.
- dnautics 8mo ago
  isn't intellectual reasoning just pattern recognition + a forward causal token generation mechanism?
  - D-Machine 8mo ago
    Obviously not. In actual thinking, we can generate an idea, evaluate it for internal consistency and consistency with our (generally much more than linguistic, i.e. may include visual imagery and other sensory representations) world models, decide this idea is bad / good, and then explore similar / different ideas. I.e. we can backtrack and form a branching tree of ideas. LLMs cannot backtrack, do not have a world model (or, to the extent they do, this world model is solely based on token patterns), and cannot evaluate consistency beyond (linguistic) semantic similarity.
    - astrange 8mo ago
      There's no such thing as a "world model". That is metaphor-driven development from GOFAI, where they'd just make up a concept and assume it existed because they made it up. LLMs are capable of approximating such a thing because they are capable of approximating anything if you train them to do it. > or, to the extent they do, this world model is solely based on token patterns Obviously not true because of RL environments.
      - D-Machine 8mo ago
        > There's no such thing as a "world model" There obviously is in humans. When you visually simulate things or e.g. simulate how food will taste in your mind as you add different seasonings, you are modeling (part of) the world. This is presumably done by having associations in our brain between all the different qualia sequences and other kinds of representations in our mind. I.e. we know we do some visuospatial reasoning tasks using sequences of (imagined) images. Imagery is one aspect of our world model(s). We know LLMs can't be doing visuospatial reasoning using imagery, because they only work with text tokens. A VLM or other multimodal might be able to do so, but an LLM can't, and so an LLM can't have a visual world model. They might in special cases be able to construct a linguistic model that lets them do some computer vision tasks, but the model will itself still only be using tokenized words. There are all sorts of other sensory modalities and things that humans use when thinking (i.e. actual logic and reasoning, which goes beyond mere semantics and might include things like logical or other forms of consistency, e.g. consistency with a relevant mental image), and the "world model" concept is supposed, in part, to point to these things that are more than just language and tokens. > Obviously not true because of RL environments. Right, AI generally can have much more complex world models than LLMs. An LLM can't even handle e.g. sensor data without significant architectural and training modification (https://news.ycombinator.com/item?id=46948266 https://news.ycombinator.com/item?id=46948266), at which point, it is no longer an LLM.
        astrange 8mo ago
        > When you visually simulate things or e.g. simulate how food will taste in your mind as you add different seasonings, you are modeling (part of) the world. Modeling something as an action is not "having a world model". A model is a consistently existing thing, but humans don't construct consistently existing models because it'd be a waste of time. You don't need to know what's in your trash in order to take the trash bags out. > We know LLMs can't be doing visuospatial reasoning using imagery, because they only work with text tokens. All frontier LLMs are multimodal to some degree. ChatGPT thinking uses it the most.
  - docjay 8mo ago
    You can replicate an LLM: You and a buddy are going to play “next word”, but it’s probably already known by a better name than I made up. You start with one word, ANY word at all, and say it out loud, then your buddy says the next word in the yet unknown sentence, then it’s back to you for one word. Loop until you hit an end. Let’s say you start with “You”. Then your buddy says the next word out loud, also whatever they want. Let’s go with “are”. Then back to you for the next word, “smarter” -> “than” -> “you” -> “think.” Neither of you knew what you were going to say, you only knew what was just said so you picked a reasonable next word. There was no ‘thought’, only next token prediction, and yet magically the final output was coherent. If you want to really get into the LLM simulation game then have a third person provide the first full sentence, then one of you picks up the first word in the next sentence and you two continue from there. As soon as you hit a breaking point the third person injects another full sentence and you two continue the game. With no idea what either of you are going to say and no clue about what the end result will be, no thought or reasoning at all, it won’t be long before you’re sounding super coherent while explaining thermodynamics. But one of the rounds someone’s going to mess it up, like “gluons” -> “weigh” -> “…more?…” -> “…than…(damnit Gary)…” but you must continue the game and finish the sentence, then sit back and think about how you just hallucinated an answer without thinking, reasoning, understanding, or even knowing what you were saying until it finished.
    - dnautics 8mo ago
      that's not how llms work. study the transformer architecture. every token is conditioned not just on the previous token, but each layer's activation generates a query over the kv cache of the previous activations, which means that each token's generation has access to any higher order analytical conclusions and observations generated in the past. information is not lost between the tokens like your thought exercise implies.
      - docjay 8mo ago
        “The cow goes ‘mooooo’” “that’s not how cow work. study bovine theory. contraction of expiratory musculature elevates abdominal pressure and reduces thoracic volume, generating positive subglottal pressure…”
- docjay 8mo ago
  They neither understand nor reason. They don’t know what they’re going to say, they only know what has just been said. Language models don’t output a response, they output a single token. We’ll use token==word shorthand: When you ask “What is the capital of France?” it actually only outputs: “The” That’s it. Truly, that IS the final output. It is literally a one-way algorithm that outputs a single word. It has no knowledge, memory, and it’s doesn’t know what’s next. As far as the algorithm is concerned it’s done! It outputs ONE token for any given input. Now, if you start over and put in “What is the capital of France? The” it’ll output “ “. That’s it. Between your two inputs were a million others, none of them have a plan for the conversation, it’s just one token out for whatever input. But if you start over yet again and put in “What is the capital of France? The “ it’ll output “capital”. That’s it. You see where this is going? Then someone uttered the words that have built and destroyed empires: “what if I automate this?” And so it was that the output was piped directly back into the input, probably using AutoHotKey. But oh no, it just kept adding one word at a time until it ran of memory. The technology got stuck there for a while, until someone thought “how about we train it so that <DONE> is an increasingly likely output the longer the loop goes on? Then, when it eventually says <DONE>, we’ll stop pumping it back into the input and send it to the user.” Booya, a trillion dollars for everyone but them. It’s truly so remarkable that it gets me stuck in an infinite philosophical loop in my own head, but seeing how it works the idea of ‘think’, ‘reason’, ‘understand’ or any of those words becomes silly. It’s amazing for entirely different reasons.
  - reedlaw 8mo ago
    Yes, LLMs mimic a form of understanding partly through the way language embeds concepts that are preserved when embedded geometrically in vector space.
    - docjay 8mo ago
      Your continued use of the word “understanding” hints at a lingering misunderstanding. They’re stateless one-shot algorithms that output a single word regardless of the input. Not even a single word, it’s a single token. It isn’t continuing a sentence or thought it had, you literally have to put it into the input again and it’ll guess at the next partial word. By default that would be the same word every time you give the same input. The only reason it isn’t is because the fuzzy randomized selector is cranked up to max by most providers (temp + seed for randomized selection), but you can turn that back down through the API and get deterministic outputs. That’s not a party trick, that’s the default of the system. If you say the same thing it will output the same single word (token) every time. You see the aggregate of running it through the stateless algorithm 200+ times before the collection of one-by-one guessed words are sent back to you as a response. I get it, if you think that was put into the glowing orb and it shot back a long coherent response with personality then it must be doing something, but the system truly only outputs one token with zero memory. It’s stateless, meaning nothing internally changed, so there is no memory to remember it wants to complete that thought or sentence. After it outputs “the” the entire thing resets to zero and you start over.
matthewkayin 8mo ago
But that's only true if the system is deterministic? And in an LLM, the size of the inputs is vast and often hidden from the prompter. It is not something that you have exact control over in the way that you have exact control over the inputs that go into a calculator or into a compiler.
dzjkb 8mo ago
a system that copies its input into the output is capable of any output, no?
- docjay 8mo ago
  That would depend - is the input also capable of anything? If it’s capable of handling any input, and as you said the output will match it, the yes of course it’s capable of any output. I’m not pulling a fast one here, I’m sure you’d chuckle if you took a moment to rethink your question. “If I had a perfect replicator that could replicate anything, does that mean it can output anything?” Well…yes. Derp-de-derp? ;) It aligns with my point too. If you had a perfect replicator that can replicate anything, and you know that to be true, then if you weren’t getting gold bars out of it you wouldn’t say “this has nothing to do with the input.”
  - dzjkb 8mo ago
    It doesn't align with your point My point is that your reasoning is too reductive - completely ignoring the mechanics of the system - and you claim the system is capable of _anything_ if prompted correctly. You wouldn't say the replicator system is capable of the reasoning outlined in the article, right?
Terr_ 8mo ago
> With any system: if it’s capable of the output then the problem IS the input. Always. [...] if it’s possible for the system to produce the output then it’s fundamentally an input problem. No, that isn't true. I can demonstrate it with a small (and deterministic) program which is obviously "capable of the output": def predict_coin_flip(player_prayer): if len(player_prayer) % 2 == 0: return "Heads" else return "Tails" Is the "fundamental problem" here "always the input"? Heck no! While a user could predict all coin-tosses by providing "the correct prayers" from some other oracle... that's just, shall we say, algorithm laundering: Secretly moving the real responsibility to some other system. There's an enormously important difference between "output which happens to be correct" versus "the correct output from a good process." Such as, in this case, the different processes of wor[l]d models.
- docjay 8mo ago
  I think you may believe what I said was controversial or nuanced enough to be worthy of a comprehensive rebuttal, but really it’s just an obvious statement when you stop to think about it. Your code is fully capable of the output I want, assuming that’s one of “heads” or “tails”, so yes that’s a succinct example of what I said. As I said, knowing the required input might not be easy, but we KNOW it’s possible to do exactly what I want and we KNOW that it’s entirely dependent on me putting the right input into it, then it’s just a flat out silly thing to say “I’m not getting the output I want, but it could do it if I use the right input, thusly input has nothing to do with it.” What? If I wanted all heads I’d need to figure out “hamburgers” would do it, but that’s the ‘input problem’ - not “input is irrelevant.”
  - Grimblewald 8mo ago
    This reads like "if we have to solution, then we have the solution". If I can model the system required to condition inputs such that outouts are deseriable, haven't i given the model the world model it required? More to the point, isn't this just what the article argues? Scaling the model cannot solve this issue. it's like saying a pencil is a portraint drawring device, like it isn't thr artist who makes it a portrait drawring device, wheras in the hands of a peot a peom generating machine.

by docjay 8mo ago

reedlaw 8mo ago

This article, (https://michaelmangialardi.substack.com/p/the-celestial-mirror-how-medieval https://michaelmangialardi.substack.com/p/the-celestial-mirr...), came to similar conclusions as the parent article, and includes some tests (e.g. https://colab.research.google.com/drive/1kTqyoYpTcbvaz8tiYgjR8Oprg2vk5KxH https://colab.research.google.com/drive/1kTqyoYpTcbvaz8tiYgj...) showing that LLMs, while good at understanding, fail at intellectual reasoning. The fact that they often produce correct outputs has more to do with training and pattern recognition than ability to grasp necessity and abstract universals.

dnautics 8mo ago
isn't intellectual reasoning just pattern recognition + a forward causal token generation mechanism?
- D-Machine 8mo ago
  Obviously not. In actual thinking, we can generate an idea, evaluate it for internal consistency and consistency with our (generally much more than linguistic, i.e. may include visual imagery and other sensory representations) world models, decide this idea is bad / good, and then explore similar / different ideas. I.e. we can backtrack and form a branching tree of ideas. LLMs cannot backtrack, do not have a world model (or, to the extent they do, this world model is solely based on token patterns), and cannot evaluate consistency beyond (linguistic) semantic similarity.
  - astrange 8mo ago
    There's no such thing as a "world model". That is metaphor-driven development from GOFAI, where they'd just make up a concept and assume it existed because they made it up. LLMs are capable of approximating such a thing because they are capable of approximating anything if you train them to do it. > or, to the extent they do, this world model is solely based on token patterns Obviously not true because of RL environments.
    - D-Machine 8mo ago
      > There's no such thing as a "world model" There obviously is in humans. When you visually simulate things or e.g. simulate how food will taste in your mind as you add different seasonings, you are modeling (part of) the world. This is presumably done by having associations in our brain between all the different qualia sequences and other kinds of representations in our mind. I.e. we know we do some visuospatial reasoning tasks using sequences of (imagined) images. Imagery is one aspect of our world model(s). We know LLMs can't be doing visuospatial reasoning using imagery, because they only work with text tokens. A VLM or other multimodal might be able to do so, but an LLM can't, and so an LLM can't have a visual world model. They might in special cases be able to construct a linguistic model that lets them do some computer vision tasks, but the model will itself still only be using tokenized words. There are all sorts of other sensory modalities and things that humans use when thinking (i.e. actual logic and reasoning, which goes beyond mere semantics and might include things like logical or other forms of consistency, e.g. consistency with a relevant mental image), and the "world model" concept is supposed, in part, to point to these things that are more than just language and tokens. > Obviously not true because of RL environments. Right, AI generally can have much more complex world models than LLMs. An LLM can't even handle e.g. sensor data without significant architectural and training modification (https://news.ycombinator.com/item?id=46948266 https://news.ycombinator.com/item?id=46948266), at which point, it is no longer an LLM.
      - astrange 8mo ago
        > When you visually simulate things or e.g. simulate how food will taste in your mind as you add different seasonings, you are modeling (part of) the world. Modeling something as an action is not "having a world model". A model is a consistently existing thing, but humans don't construct consistently existing models because it'd be a waste of time. You don't need to know what's in your trash in order to take the trash bags out. > We know LLMs can't be doing visuospatial reasoning using imagery, because they only work with text tokens. All frontier LLMs are multimodal to some degree. ChatGPT thinking uses it the most.
- docjay 8mo ago
  You can replicate an LLM: You and a buddy are going to play “next word”, but it’s probably already known by a better name than I made up. You start with one word, ANY word at all, and say it out loud, then your buddy says the next word in the yet unknown sentence, then it’s back to you for one word. Loop until you hit an end. Let’s say you start with “You”. Then your buddy says the next word out loud, also whatever they want. Let’s go with “are”. Then back to you for the next word, “smarter” -> “than” -> “you” -> “think.” Neither of you knew what you were going to say, you only knew what was just said so you picked a reasonable next word. There was no ‘thought’, only next token prediction, and yet magically the final output was coherent. If you want to really get into the LLM simulation game then have a third person provide the first full sentence, then one of you picks up the first word in the next sentence and you two continue from there. As soon as you hit a breaking point the third person injects another full sentence and you two continue the game. With no idea what either of you are going to say and no clue about what the end result will be, no thought or reasoning at all, it won’t be long before you’re sounding super coherent while explaining thermodynamics. But one of the rounds someone’s going to mess it up, like “gluons” -> “weigh” -> “…more?…” -> “…than…(damnit Gary)…” but you must continue the game and finish the sentence, then sit back and think about how you just hallucinated an answer without thinking, reasoning, understanding, or even knowing what you were saying until it finished.
  - dnautics 8mo ago
    that's not how llms work. study the transformer architecture. every token is conditioned not just on the previous token, but each layer's activation generates a query over the kv cache of the previous activations, which means that each token's generation has access to any higher order analytical conclusions and observations generated in the past. information is not lost between the tokens like your thought exercise implies.
    - docjay 8mo ago
      “The cow goes ‘mooooo’” “that’s not how cow work. study bovine theory. contraction of expiratory musculature elevates abdominal pressure and reduces thoracic volume, generating positive subglottal pressure…”
docjay 8mo ago
They neither understand nor reason. They don’t know what they’re going to say, they only know what has just been said. Language models don’t output a response, they output a single token. We’ll use token==word shorthand: When you ask “What is the capital of France?” it actually only outputs: “The” That’s it. Truly, that IS the final output. It is literally a one-way algorithm that outputs a single word. It has no knowledge, memory, and it’s doesn’t know what’s next. As far as the algorithm is concerned it’s done! It outputs ONE token for any given input. Now, if you start over and put in “What is the capital of France? The” it’ll output “ “. That’s it. Between your two inputs were a million others, none of them have a plan for the conversation, it’s just one token out for whatever input. But if you start over yet again and put in “What is the capital of France? The “ it’ll output “capital”. That’s it. You see where this is going? Then someone uttered the words that have built and destroyed empires: “what if I automate this?” And so it was that the output was piped directly back into the input, probably using AutoHotKey. But oh no, it just kept adding one word at a time until it ran of memory. The technology got stuck there for a while, until someone thought “how about we train it so that <DONE> is an increasingly likely output the longer the loop goes on? Then, when it eventually says <DONE>, we’ll stop pumping it back into the input and send it to the user.” Booya, a trillion dollars for everyone but them. It’s truly so remarkable that it gets me stuck in an infinite philosophical loop in my own head, but seeing how it works the idea of ‘think’, ‘reason’, ‘understand’ or any of those words becomes silly. It’s amazing for entirely different reasons.
- reedlaw 8mo ago
  Yes, LLMs mimic a form of understanding partly through the way language embeds concepts that are preserved when embedded geometrically in vector space.
  - docjay 8mo ago
    Your continued use of the word “understanding” hints at a lingering misunderstanding. They’re stateless one-shot algorithms that output a single word regardless of the input. Not even a single word, it’s a single token. It isn’t continuing a sentence or thought it had, you literally have to put it into the input again and it’ll guess at the next partial word. By default that would be the same word every time you give the same input. The only reason it isn’t is because the fuzzy randomized selector is cranked up to max by most providers (temp + seed for randomized selection), but you can turn that back down through the API and get deterministic outputs. That’s not a party trick, that’s the default of the system. If you say the same thing it will output the same single word (token) every time. You see the aggregate of running it through the stateless algorithm 200+ times before the collection of one-by-one guessed words are sent back to you as a response. I get it, if you think that was put into the glowing orb and it shot back a long coherent response with personality then it must be doing something, but the system truly only outputs one token with zero memory. It’s stateless, meaning nothing internally changed, so there is no memory to remember it wants to complete that thought or sentence. After it outputs “the” the entire thing resets to zero and you start over.

matthewkayin 8mo ago

But that's only true if the system is deterministic? And in an LLM, the size of the inputs is vast and often hidden from the prompter. It is not something that you have exact control over in the way that you have exact control over the inputs that go into a calculator or into a compiler.

dzjkb 8mo ago

a system that copies its input into the output is capable of any output, no?

docjay 8mo ago
That would depend - is the input also capable of anything? If it’s capable of handling any input, and as you said the output will match it, the yes of course it’s capable of any output. I’m not pulling a fast one here, I’m sure you’d chuckle if you took a moment to rethink your question. “If I had a perfect replicator that could replicate anything, does that mean it can output anything?” Well…yes. Derp-de-derp? ;) It aligns with my point too. If you had a perfect replicator that can replicate anything, and you know that to be true, then if you weren’t getting gold bars out of it you wouldn’t say “this has nothing to do with the input.”
- dzjkb 8mo ago
  It doesn't align with your point My point is that your reasoning is too reductive - completely ignoring the mechanics of the system - and you claim the system is capable of _anything_ if prompted correctly. You wouldn't say the replicator system is capable of the reasoning outlined in the article, right?

Terr_ 8mo ago

> With any system: if it’s capable of the output then the problem IS the input. Always. [...] if it’s possible for the system to produce the output then it’s fundamentally an input problem. No, that isn't true. I can demonstrate it with a small (and deterministic) program which is obviously "capable of the output": def predict_coin_flip(player_prayer): if len(player_prayer) % 2 == 0: return "Heads" else return "Tails" Is the "fundamental problem" here "always the input"? Heck no! While a user could predict all coin-tosses by providing "the correct prayers" from some other oracle... that's just, shall we say, algorithm laundering: Secretly moving the real responsibility to some other system. There's an enormously important difference between "output which happens to be correct" versus "the correct output from a good process." Such as, in this case, the different processes of wor[l]d models.

docjay 8mo ago
I think you may believe what I said was controversial or nuanced enough to be worthy of a comprehensive rebuttal, but really it’s just an obvious statement when you stop to think about it. Your code is fully capable of the output I want, assuming that’s one of “heads” or “tails”, so yes that’s a succinct example of what I said. As I said, knowing the required input might not be easy, but we KNOW it’s possible to do exactly what I want and we KNOW that it’s entirely dependent on me putting the right input into it, then it’s just a flat out silly thing to say “I’m not getting the output I want, but it could do it if I use the right input, thusly input has nothing to do with it.” What? If I wanted all heads I’d need to figure out “hamburgers” would do it, but that’s the ‘input problem’ - not “input is irrelevant.”
- Grimblewald 8mo ago
  This reads like "if we have to solution, then we have the solution". If I can model the system required to condition inputs such that outouts are deseriable, haven't i given the model the world model it required? More to the point, isn't this just what the article argues? Scaling the model cannot solve this issue. it's like saying a pencil is a portraint drawring device, like it isn't thr artist who makes it a portrait drawring device, wheras in the hands of a peot a peom generating machine.